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rliger: Linked Inference of Genomic Experimental Relationships

Description

Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019)doi:10.1016/j.cell.2019.05.006, and Liu J, Gao C, Sodicoff J, et al (2020)doi:10.1038/s41596-020-0391-8 for more details.

Author(s)

Maintainer: Yichen Wangwayichen@umich.edu

Authors:

• Joshua Welchwelchjd@umich.edu

• Chao Gaogchao@umich.edu

• Jialin Liualanliu@umich.edu

• Joshua Sodicoffsodicoff@umich.edu [contributor]

• Velina Kozareva [contributor]

• Evan Macosko [contributor]

Other contributors:

• Paul Hoffman [contributor]

• Ilya Korsunsky [contributor]

• Robert Lee [contributor]

• Andrew Robbinsrobbiand@med.umich.edu [contributor]

See Also

Useful links:

• https://welch-lab.github.io/liger/

• Report bugs athttps://github.com/welch-lab/liger/issues

Generate dot plot from input matrix with ComplexHeatmap

Description

Generate dot plot from input matrix with ComplexHeatmap

Usage

.complexHeatmapDotPlot(  colorMat,  sizeMat,  featureAnnDF = NULL,  cellSplitVar = NULL,  cellLabels = NULL,  maxDotSize = 4,  clusterFeature = FALSE,  clusterCell = FALSE,  legendColorTitle = "Matrix Value",  legendSizeTitle = "Fraction Value",  transpose = FALSE,  baseSize = 8,  cellTextSize = NULL,  featureTextSize = NULL,  cellTitleSize = NULL,  featureTitleSize = NULL,  legendTextSize = NULL,  legendTitleSize = NULL,  featureGrpRot = 0,  viridisOption = "C",  viridisDirection = -1,  ...)

Arguments

Value

AHeatmapList object.

Produce single violin plot with data frame passed from upstream

Description

Produce single violin plot with data frame passed from upstream

Usage

.ggCellViolin(  plotDF,  y,  groupBy = NULL,  colorBy = NULL,  violin = TRUE,  violinAlpha = 0.8,  violinWidth = 0.9,  box = FALSE,  boxAlpha = 0.6,  boxWidth = 0.4,  dot = FALSE,  dotColor = "black",  dotSize = getOption("ligerDotSize"),  xlabAngle = 45,  raster = NULL,  seed = 1,  ...)

Arguments

Value

ggplot object by default. Whenplotly = TRUE, returnsplotly (htmlwidget) object.

Produce single scatter plot with data frame passed from upstream

Description

Produce single scatter plot with data frame passed from upstream

Usage

.ggScatter(  plotDF,  x,  y,  colorBy = NULL,  shapeBy = NULL,  dotOrder = c("shuffle", "ascending", "descending"),  dotSize = getOption("ligerDotSize"),  dotAlpha = 0.9,  trimHigh = NULL,  trimLow = NULL,  zeroAsNA = TRUE,  raster = NULL,  labelBy = colorBy,  labelText = TRUE,  labelTextSize = 4,  ggrepelLabelTick = FALSE,  seed = 1,  ...)

Arguments

Details

Having package "ggrepel" installed can help adding tidier textlabels on the scatter plot.

Value

ggplot object by default. Whenplotly = TRUE, returnsplotly (htmlwidget) object.

Generic ggplot theme setting for rliger package

Description

Controls content and size of all peripheral texts.

Usage

.ggplotLigerTheme(  plot,  title = NULL,  subtitle = NULL,  xlab = TRUE,  ylab = TRUE,  xlabAngle = 0,  legendColorTitle = NULL,  legendFillTitle = NULL,  legendShapeTitle = NULL,  legendSizeTitle = NULL,  showLegend = TRUE,  legendPosition = "right",  baseSize = getOption("ligerBaseSize"),  titleSize = NULL,  subtitleSize = NULL,  xTextSize = NULL,  xFacetSize = NULL,  xTitleSize = NULL,  yTextSize = NULL,  yFacetSize = NULL,  yTitleSize = NULL,  legendTextSize = NULL,  legendTitleSize = NULL,  legendDotSize = 4,  panelBorder = FALSE,  legendNRow = NULL,  legendNCol = NULL,  colorLabels = NULL,  colorValues = NULL,  colorPalette = "magma",  colorDirection = -1,  naColor = "#DEDEDE",  colorLow = NULL,  colorMid = NULL,  colorHigh = NULL,  colorMidPoint = NULL,  plotly = FALSE)

Arguments

Value

Updated ggplot object by default. Whenplotly = TRUE, returnsplotly (htmlwidget) object.

General heatmap plotting with prepared matrix and data.frames

Description

This is not an exported function. This documentation justserves for a manual of extra arguments that users can use when generatingheatmaps withplotGeneHeatmap orplotFactorHeatmap.

Note that the following arguments are pre-occupied by upstream wrappers sousers should not include them in a function call:dataMatrix,dataName,cellDF,featureDF,cellSplitVar,featureSplitVar.

The following arguments ofHeatmap is occupiedby this function, so users should include them in a function call as well:matrix,name,col,heatmap_legend_param,top_annotation,column_title_gp,column_names_gp,show_column_names,column_split,column_gap,left_annotation,row_title_gp,row_names_gp,show_row_names,row_split,row_gap.

Usage

.plotHeatmap(  dataMatrix,  dataName = "Value",  cellDF = NULL,  featureDF = NULL,  transpose = FALSE,  cellSplitVar = NULL,  featureSplitVar = NULL,  dataScaleFunc = NULL,  showCellLabel = FALSE,  showCellLegend = TRUE,  showFeatureLabel = TRUE,  showFeatureLegend = TRUE,  cellAnnColList = NULL,  featureAnnColList = NULL,  scale = FALSE,  trim = c(-2, 2),  baseSize = 8,  cellTextSize = NULL,  featureTextSize = NULL,  cellTitleSize = NULL,  featureTitleSize = NULL,  legendTextSize = NULL,  legendTitleSize = NULL,  viridisOption = "A",  viridisDirection = -1,  RColorBrewerOption = "RdBu",  ...)

Arguments

Value

HeatmapList-class object

Apply function to chunks of H5 data in ligerDataset object

Description

h5 calculation wrapper, that runs specified calculation withon-disk matrix in chunks

Usage

H5Apply(  object,  FUN,  init = NULL,  useData = c("rawData", "normData"),  chunkSize = 1000,  verbose = getOption("ligerVerbose"),  ...)

Arguments

Details

TheFUN function has to have the first four arguments orderedby:

1. chunk data: A sparse matrix(dgCMatrix-class) containing maximumchunkSizecolumns.

2. x-vector index: The index that subscribes the vector ofxslot of a dgCMatrix, which points to the values in each chunk. Mostly usedwhen need to write a new sparse matrix to H5 file.

3. cell index: The column index of each chunk out of the wholeoriginal matrix

4. Initialized result: A customized object, the value passed toH5Apply(init) argument will be passed here in the first iteration. Andthe returned value ofFUN will be iteratively passed here in nextchunk iterations. So it is important to keep the object structure of thereturned value consistent withinit.

No default value to these four arguments should be pre-defined becauseH5Apply will automatically generate the input.

Align factor loadings to get final integration

Description

This function is a wrapper to switch between alternative factor loadingalignment methods that LIGER provides, which is a required step for producingthe final integrated result. Two methods are provided (click on options formore details):

• method = "quantileNorm": Previously published quantilenormalization method. (default)

• method = "centroidAlign": Newly developed centroidalignment method.

Usage

alignFactors(object, method = c("quantileNorm", "centroidAlign"), ...)## S3 method for class 'liger'alignFactors(object, method = c("quantileNorm", "centroidAlign"), ...)## S3 method for class 'Seurat'alignFactors(object, method = c("quantileNorm", "centroidAlign"), ...)

Arguments

See Also

quantileNorm,centroidAlign

Converting other classes of data to a liger object

Description

This function converts data stored in SingleCellExperiment (SCE), Seuratobject or a merged sparse matrix (dgCMatrix) into a liger object. This isdesigned for a container object or matrix that already contains multipledatasets to be integerated with LIGER. For individual datasets, please usecreateLiger instead.

Usage

## S3 method for class 'dgCMatrix'as.liger(object, datasetVar = NULL, modal = NULL, ...)## S3 method for class 'SingleCellExperiment'as.liger(object, datasetVar = NULL, modal = NULL, ...)## S3 method for class 'Seurat'as.liger(object, datasetVar = NULL, modal = NULL, assay = NULL, ...)seuratToLiger(object, datasetVar = NULL, modal = NULL, assay = NULL, ...)as.liger(object, ...)

Arguments

Details

For Seurat V5 structure, it is highly recommended that users make use of itssplit layer feature, where things like "counts", "data", and "scale.data"can be held for each dataset in the same Seurat object, e.g. with"count.ctrl", "count.stim", not merged. If a Seurat object with split layersis given,datasetVar will be ignored and the layers will be directlyused.

Value

aliger object.

Examples

# dgCMatrix (common sparse matrix class), usually obtained from other# container object, and contains multiple samples merged in one.matList <- rawData(pbmc)multiSampleMatrix <- mergeSparseAll(matList)# The `datasetVar` argument expects the variable assigning the sample sourcepbmc2 <- as.liger(multiSampleMatrix, datasetVar = pbmc$dataset)pbmc2if (requireNamespace("SingleCellExperiment", quietly = TRUE)) {    sce <- SingleCellExperiment::SingleCellExperiment(        assays = list(counts = multiSampleMatrix)    )    sce$sample <- pbmc$dataset    pbmc3 <- as.liger(sce, datasetVar = "sample")    pbmc3}if (requireNamespace("Seurat", quietly = TRUE)) {    seu <- SeuratObject::CreateSeuratObject(multiSampleMatrix)    # Seurat creates variable "orig.ident" by identifying the cell barcode    # prefixes, which is indeed what we need in this case. Users might need    # to be careful and have it confirmed first.    pbmc4 <- as.liger(seu, datasetVar = "orig.ident")    pbmc4    # As per Seurat V5 updates with layered data, specifically helpful udner the    # scenario of dataset integration. "counts" and etc for each datasets can be    # split into layers.    seu5 <- seu    seu5[["RNA"]] <- split(seu5[["RNA"]], pbmc$dataset)    print(SeuratObject::Layers(seu5))    pbmc5 <- as.liger(seu5)    pbmc5}

Converting other classes of data to a ligerDataset object

Description

Works for converting a matrix or container object to a single ligerDataset,and can also convert the modality preset of a ligerDataset. When used witha dense matrix object, it automatically converts the matrix to sparse form(dgCMatrix-class). When used with container objectssuch as Seurat or SingleCellExperiment, it is highly recommended that theobject contains only one dataset/sample which is going to be integrated withLIGER. For multi-sample objects, please useas.liger withdataset source variable specified.

Usage

## S3 method for class 'ligerDataset'as.ligerDataset(  object,  modal = c("default", "rna", "atac", "spatial", "meth"),  ...)## Default S3 method:as.ligerDataset(  object,  modal = c("default", "rna", "atac", "spatial", "meth"),  ...)## S3 method for class 'matrix'as.ligerDataset(  object,  modal = c("default", "rna", "atac", "spatial", "meth"),  ...)## S3 method for class 'Seurat'as.ligerDataset(  object,  modal = c("default", "rna", "atac", "spatial", "meth"),  assay = NULL,  ...)## S3 method for class 'SingleCellExperiment'as.ligerDataset(  object,  modal = c("default", "rna", "atac", "spatial", "meth"),  ...)as.ligerDataset(object, ...)

Arguments

Value

aliger object.

Examples

ctrl <- dataset(pbmc, "ctrl")ctrl# Convert the modality presetas.ligerDataset(ctrl, modal = "atac")rawCounts <- rawData(ctrl)class(rawCounts)as.ligerDataset(rawCounts)

liger object of bone marrow subsample data with RNA and ATAC modality

Description

liger object of bone marrow subsample data with RNA and ATAC modality

Usage

bmmc

Format

liger object with two dataset named by "rna" and "atac"

Source

https://www.nature.com/articles/s41587-019-0332-7

References

Jeffrey M. Granja and et. al., Nature Biotechnology, 2019

Calculate adjusted Rand index (ARI) by comparing two cluster labeling variables

Description

This function aims at calculating the adjusted Rand index for the clusteringresult obtained with LIGER and the external clustering (existing "true"annotation). ARI ranges from 0 to 1, with a score of 0 indicating noagreement between clusterings and 1 indicating perfect agreement.

The true clustering annotation must be specified as the base line. We suggestsetting it to the object cellMeta so that it can be easily used for manyother visualization and evaluation functions.

The ARI can be calculated for only specified datasets, since true annotationmight not be available for all datasets. Evaluation for only one or a fewdatasets can be done by specifyinguseDatasets. IfuseDatasetsis specified, the argument checking fortrueCluster anduseCluster will be enforced to match the cells in the specifieddatasets.

Usage

calcARI(  object,  trueCluster,  useCluster = NULL,  useDatasets = NULL,  verbose = getOption("ligerVerbose", TRUE),  classes.compare = trueCluster)

Arguments

Value

A numeric scalar, the ARI of the clustering result indicated byuseCluster compared totrueCluster.

A numeric scalar of the ARI value

References

L. Hubert and P. Arabie (1985) Comparing Partitions, Journal ofthe Classification, 2, pp. 193-218.

Examples

# Assume the true cluster in `pbmcPlot` is "leiden_cluster"# generate fake new labelingfake <- sample(1:7, ncol(pbmcPlot), replace = TRUE)# Insert into cellMetapbmcPlot$new <- factor(fake)calcARI(pbmcPlot, trueCluster = "leiden_cluster", useCluster = "new")# Now assume we got existing base line annotation only for "stim" datasetnStim <- ncol(dataset(pbmcPlot, "stim"))stimTrueLabel <- factor(fake[1:nStim])# Insert into cellMetacellMeta(pbmcPlot, "stim_true_label", useDatasets = "stim") <- stimTrueLabel# Assume "leiden_cluster" is the clustering result we got and need to be# evaluatedcalcARI(pbmcPlot, trueCluster = "stim_true_label",        useCluster = "leiden_cluster", useDatasets = "stim")# Comparison of the same labeling should always yield 1.calcARI(pbmcPlot, trueCluster = "leiden_cluster", useCluster = "leiden_cluster")

Calculate agreement metric after integration

Description

This metric quantifies how much the factorization and alignment distorts thegeometry of the original datasets. The greater the agreement, the lessdistortion of geometry there is. This is calculated by performingdimensionality reduction on the original and integrated (factorized or plusaligned) datasets, and measuring similarity between the k nearestneighbors for each cell in original and integrated datasets. The Jaccardindex is used to quantify similarity, and is the final metric averages acrossall cells.

Note that for most datasets, the greater the chosennNeighbor, thegreater the agreement in general. Although agreement can theoreticallyapproach 1, in practice it is usually no higher than 0.2-0.3.

Usage

calcAgreement(  object,  ndims = 40,  nNeighbors = 15,  useRaw = FALSE,  byDataset = FALSE,  seed = 1,  dr.method = NULL,  k = nNeighbors,  use.aligned = NULL,  rand.seed = seed,  by.dataset = byDataset)

Arguments

Value

A numeric vector of agreement metric. A single value ifbyDataset = FALSE or each dataset a value otherwise.

Examples

if (requireNamespace("RcppPlanc", quietly = TRUE)) {    pbmc <- pbmc %>%    normalize %>%    selectGenes %>%    scaleNotCenter %>%    runINMF %>%    alignFactors    calcAgreement(pbmc)}

Calculate alignment metric after integration

Description

This metric quantifies how well-aligned two or more datasets are. We randomlydownsample all datasets to have as many cells as the smallest one. Weconstruct a nearest-neighbor graph and calculate for each cell how many ofits neighbors are from the same dataset. We average across all cells andcompare to the expected value for perfectly mixed datasets, and scale thevalue from 0 to 1. Note that in practice, alignment can be greater than 1occasionally.

Usage

calcAlignment(  object,  clustersUse = NULL,  clusterVar = NULL,  nNeighbors = NULL,  cellIdx = NULL,  cellComp = NULL,  resultBy = c("all", "dataset", "cell"),  seed = 1,  k = nNeighbors,  rand.seed = seed,  cells.use = cellIdx,  cells.comp = cellComp,  clusters.use = clustersUse,  by.cell = NULL,  by.dataset = NULL)

Arguments

Details

\bar{x} is the average number of neighbors belonging to any cells' samedataset,N is the number of datasets,k is the number ofneighbors in the KNN graph.

1 - \frac{\bar{x} - \frac{k}{N}}{k - \frac{k}{N}}

The selection on cells to be measured can be done in various way andrepresent different scenarios:

1. By default, all cells are considered and the alignment across alldatasets will be calculated.

2. SelectclustersUse fromclusterVar to use cells from theclusters of interests. This measures the alignment across all covereddatasets within the specified clusters.

3. Only SpecifycellIdx for flexible selection. This measures thealignment across all covered datasets within the specified cells. A none-NULLcellIdx privileges overclustersUse.

4. SpecifycellIdx andcellComp at the same time, so thatthe original dataset source will be ignored and cells specified by eachargument will be regarded as from each a dataset. This measures the alignmentbetween cells specified by the two arguments.cellComp can containcells already specified incellIdx.

Value

The alignment metric.

Examples

if (requireNamespace("RcppPlanc", quietly = TRUE)) {    pbmc <- pbmc %>%    normalize %>%    selectGenes %>%    scaleNotCenter %>%    runINMF %>%    alignFactors    calcAlignment(pbmc)}

Calculate a dataset-specificity score for each factor

Description

This score represents the relative magnitude of thedataset-specific components of each factor's gene loadings compared to theshared components for two datasets. First, for each dataset we calculate thenorm of the sum of each factor's shared loadings (W) anddataset-specific loadings (V). We then determine the ratio of these twovalues and subtract from 1... TODO: finish description.

Usage

calcDatasetSpecificity(  object,  dataset1,  dataset2,  doPlot = FALSE,  do.plot = doPlot)

Arguments

Value

List containing three elements.

Calculate Normalized Mutual Information (NMI) by comparing two clusterlabeling variables

Description

This function aims at calculating the Normalized Mutual Information for theclustering result obtained with LIGER and the external clustering (existing"true" annotation). NMI ranges from 0 to 1, with a score of 0 indicating noagreement between clusterings and 1 indicating perfect agreement. Themathematical definition of NMI is as follows:

H(X) = -\sum_{x \in X}P(X=x)\log_2 P(X=x)

H(X|Y) = -\sum_{y \in Y}P(Y=y)\sum_{x \in X}P(X=x|Y=y)\log_2 P(X=x|Y=y)

I(X;Y) = H(X) - H(X|Y)

NMI(X;Y) = \frac{I(X;Y)}{\sqrt{H(X)H(Y)}}

WhereX is the cluster variable to be evaluated andY is the truecluster variable.x andy are the cluster labels inX andY respectively.H is the entropy andI is the mutualinformation.

The true clustering annotation must be specified as the base line. We suggestsetting it to the object cellMeta so that it can be easily used for manyother visualization and evaluation functions.

The NMI can be calculated for only specified datasets, since true annotationmight not be available for all datasets. Evaluation for only one or a fewdatasets can be done by specifyinguseDatasets. IfuseDatasetsis specified, the argument checking fortrueCluster anduseCluster will be enforced to match the cells in the specifieddatasets.

Usage

calcNMI(  object,  trueCluster,  useCluster = NULL,  useDatasets = NULL,  verbose = getOption("ligerVerbose", TRUE))

Arguments

Value

A numeric scalar of the NMI value

Examples

# Assume the true cluster in `pbmcPlot` is "leiden_cluster"# generate fake new labelingfake <- sample(1:7, ncol(pbmcPlot), replace = TRUE)# Insert into cellMetapbmcPlot$new <- factor(fake)calcNMI(pbmcPlot, trueCluster = "leiden_cluster", useCluster = "new")# Now assume we got existing base line annotation only for "stim" datasetnStim <- ncol(dataset(pbmcPlot, "stim"))stimTrueLabel <- factor(fake[1:nStim])# Insert into cellMetacellMeta(pbmcPlot, "stim_true_label", useDatasets = "stim") <- stimTrueLabel# Assume "leiden_cluster" is the clustering result we got and need to be# evaluatedcalcNMI(pbmcPlot, trueCluster = "stim_true_label",        useCluster = "leiden_cluster", useDatasets = "stim")# Comparison of the same labeling should always yield 1.calcNMI(pbmcPlot, trueCluster = "leiden_cluster", useCluster = "leiden_cluster")

Calculate purity by comparing two cluster labeling variables

Description

This function aims at calculating the purity for the clustering resultobtained with LIGER and the external clustering (existing "true" annotation).Purity can sometimes be a more useful metric when the clustering to be testedcontains more subgroups or clusters than the true clusters. Purity rangesfrom 0 to 1, with a score of 1 representing a pure, accurate clustering.

The true clustering annotation must be specified as the base line. We suggestsetting it to the object cellMeta so that it can be easily used for manyother visualization and evaluation functions.

The purity can be calculated for only specified datasets, since trueannotation might not be available for all datasets. Evaluation for only oneor a few datasets can be done by specifyinguseDatasets. IfuseDatasets is specified, the argument checking fortrueClusteranduseCluster will be enforced to match the cells in the specifieddatasets.

Usage

calcPurity(  object,  trueCluster,  useCluster = NULL,  useDatasets = NULL,  verbose = getOption("ligerVerbose", TRUE),  classes.compare = trueCluster)

Arguments

Value

A numeric scalar, the purity of the clustering result indicated byuseCluster compared totrueCluster.

Examples

# Assume the true cluster in `pbmcPlot` is "leiden_cluster"# generate fake new labelingfake <- sample(1:7, ncol(pbmcPlot), replace = TRUE)# Insert into cellMetapbmcPlot$new <- factor(fake)calcPurity(pbmcPlot, trueCluster = "leiden_cluster", useCluster = "new")# Now assume we got existing base line annotation only for "stim" datasetnStim <- ncol(dataset(pbmcPlot, "stim"))stimTrueLabel <- factor(fake[1:nStim])# Insert into cellMetacellMeta(pbmcPlot, "stim_true_label", useDatasets = "stim") <- stimTrueLabel# Assume "leiden_cluster" is the clustering result we got and need to be# evaluatedcalcPurity(pbmcPlot, trueCluster = "stim_true_label",           useCluster = "leiden_cluster", useDatasets = "stim")

Cell cycle gene set for human

Description

Copied from Seurat::cc.genes

Usage

ccGeneHuman

Format

A list of two character vectors:

s.genes

Genes associated with S-phase

g2m.genes

Genes associated with G2M-phase

Source

https://www.science.org/doi/abs/10.1126/science.aad0501

Align factor loading by centroid alignment (beta)

Description

This process treats the factor loading of each dataset as the low dimensionalembedding as well as the cluster assignment probability, i.e. the softclustering result. Then the method aligns the embedding by linearly movingthe centroids of the same cluster but within each dataset towards each other.

ATTENTION: This method is still under development while has shownencouraging results in benchmarking tests. The arguments and their defaultvalues reflect the best scored parameters in the tests and some of them maybe subject to change in the future.

Usage

centroidAlign(object, ...)## S3 method for class 'liger'centroidAlign(  object,  lambda = 1,  useDims = NULL,  scaleEmb = TRUE,  centerEmb = TRUE,  scaleCluster = FALSE,  centerCluster = FALSE,  shift = FALSE,  diagnosis = FALSE,  ...)## S3 method for class 'Seurat'centroidAlign(  object,  reduction = "inmf",  lambda = 1,  useDims = NULL,  scaleEmb = TRUE,  centerEmb = TRUE,  scaleCluster = FALSE,  centerCluster = FALSE,  shift = FALSE,  diagnosis = FALSE,  ...)

Arguments

Details

Diagnostic information include:

• object$raw_which.max: The index of the factor with the maximum valuein the raw factor loading.

• object$R_which.max: The index of the factor with the maximum value inthe soft clustering probability matrix used for correction.

• object$Z_which.max: The index of the factor with the maximum value inthe aligned factor loading.

Value

Returns the updated input object

• liger method

  • Update theH.norm slot for the aligned cell factorloading, ready for running graph based community detection clusteringor dimensionality reduction for visualization.

  • Update thecellMata slot with diagnostic information ifdiagnosis = TRUE.

• Seurat method

  • Update thereductions slot with a newDimReducobject containing the aligned cell factor loading.

  • Update the metadata with diagnostic information ifdiagnosis = TRUE.

Examples

pbmc <- centroidAlign(pbmcPlot)

Close all links (to HDF5 files) of a liger object

Description

When need to interact with the data embedded in HDF5 files outof the currect R session, the HDF5 files has to be closed in order to beavailable to other processes.

Usage

closeAllH5(object)## S3 method for class 'liger'closeAllH5(object)## S3 method for class 'ligerDataset'closeAllH5(object)

Arguments

Value

Nothing is returned.

Check difference of two liger command

Description

Check difference of two liger command

Usage

commandDiff(object, cmd1, cmd2)

Arguments

Value

If any difference found, a character vector summarizing alldifferences

Examples

pbmc <- normalize(pbmc)pbmc <- normalize(pbmc, log = TRUE, scaleFactor = 1e4)cmds <- commands(pbmc)commandDiff(pbmc, cmds[1], cmds[2])

Convert old liger object to latest version

Description

Convert old liger object to latest version

Usage

convertOldLiger(  object,  dimredName,  clusterName = "clusters",  h5FilePath = NULL)

Arguments

Examples

## Not run: # Suppose you have a liger object of old version (<1.99.0)newLig <- convertOldLiger(oldLig)## End(Not run)

Access ligerSpatialDataset coordinate data

Description

Similar as how defaultligerDataset data isaccessed.

Usage

coordinate(x, dataset)coordinate(x, dataset, check = TRUE) <- value## S4 method for signature 'liger,character'coordinate(x, dataset)## S4 replacement method for signature 'liger,character'coordinate(x, dataset, check = TRUE) <- value## S4 method for signature 'ligerSpatialDataset,missing'coordinate(x, dataset = NULL)## S4 replacement method for signature 'ligerSpatialDataset,missing'coordinate(x, dataset = NULL, check = TRUE) <- value

Arguments

Value

The retrieved coordinate matrix or the updatedx object.

Create on-disk ligerDataset Object

Description

For convenience, the defaultformatType = "10x" directly fits thestructure of cellranger output.formatType = "anndata" works forcurrent AnnData H5AD file specification (see Details). If a customized H5file structure is presented, any of therawData,indicesName,indptrName,genesName,barcodesNameshould be specified accordingly to override theformatType preset.

DO make a copy of the H5AD files because rliger functions write tothe files and they will not be able to be read back to Python. This will befixed in the future.

Usage

createH5LigerDataset(  h5file,  formatType = "10x",  rawData = NULL,  normData = NULL,  scaleData = NULL,  barcodesName = NULL,  genesName = NULL,  indicesName = NULL,  indptrName = NULL,  anndataX = "X",  modal = c("default", "rna", "atac", "spatial", "meth"),  featureMeta = NULL,  ...)

Arguments

Details

For H5AD file written from an AnnData object, we allow usingformatType = "anndata" for the function to infer the proper structure.However, while a typical AnnData-based analysis tends to in-place update theadata.X attribute and there is no standard/forced convention for wherethe raw count data, as needed from LIGER, is stored. Therefore, we exposeargumentanndataX for specifying this information. The default value"X" looks foradata.X. If the raw data is stored in a layer,e.g.adata.layers['count'], thenanndataX = "layers/count".If it is stored toadata.raw.X, thenanndataX = "raw/X". Ifyour AnnData object does not have the raw count retained, you will have togo back to the Python work flow to have it inserted at desired object spaceand re-write the H5AD file, or just go from upstream source files with whichthe AnnData was originally created.

Value

H5-basedligerDataset object

Examples

h5Path <- system.file("extdata/ctrl.h5", package = "rliger")tempPath <- tempfile(fileext = ".h5")file.copy(from = h5Path, to = tempPath)ld <- createH5LigerDataset(tempPath)

Create liger object

Description

This function allows creatingliger object frommultiple datasets of various forms (SeerawData).

DO make a copy of the H5AD files because rliger functions write tothe files and they will not be able to be read back to Python. This will befixed in the future.

Usage

createLiger(  rawData,  modal = NULL,  organism = "human",  cellMeta = NULL,  removeMissing = TRUE,  addPrefix = "auto",  formatType = "10X",  anndataX = "X",  dataName = NULL,  indicesName = NULL,  indptrName = NULL,  genesName = NULL,  barcodesName = NULL,  newH5 = TRUE,  verbose = getOption("ligerVerbose", TRUE),  ...,  raw.data = rawData,  take.gene.union = NULL,  remove.missing = removeMissing,  format.type = formatType,  data.name = dataName,  indices.name = indicesName,  indptr.name = indptrName,  genes.name = genesName,  barcodes.name = barcodesName)

Arguments

See Also

createLigerDataset,createH5LigerDataset

Examples

# Create from raw count matricesctrl.raw <- rawData(pbmc, "ctrl")stim.raw <- rawData(pbmc, "stim")pbmc1 <- createLiger(list(ctrl = ctrl.raw, stim = stim.raw))# Create from H5 filesh5Path <- system.file("extdata/ctrl.h5", package = "rliger")tempPath <- tempfile(fileext = ".h5")file.copy(from = h5Path, to = tempPath)lig <- createLiger(list(ctrl = tempPath))# Create from other container objectif (requireNamespace("SeuratObject", quietly = TRUE)) {    ctrl.seu <- SeuratObject::CreateSeuratObject(ctrl.raw)    stim.seu <- SeuratObject::CreateSeuratObject(stim.raw)    pbmc2 <- createLiger(list(ctrl = ctrl.seu, stim = stim.seu))}

Create in-memory ligerDataset object

Description

Create in-memory ligerDataset object

Usage

createLigerDataset(  rawData = NULL,  modal = c("default", "rna", "atac", "spatial", "meth"),  normData = NULL,  scaleData = NULL,  featureMeta = NULL,  ...)

Arguments

See Also

ligerDataset,ligerATACDataset,ligerSpatialDataset,ligerMethDataset

Examples

ctrl.raw <- rawData(pbmc, "ctrl")ctrl.ld <- createLigerDataset(ctrl.raw)

Data frame for example marker DEG test result

Description

The data frame is the direct output of marker detection DEG test applied onexample dataset which can be loaded withdata("pbmc"). The DEG testwas done with:

defaultCluster(pbmc) <- pbmcPlot$leiden_clusterdeg.marker <- runMarkerDEG(    pbmc,    minCellPerRep = 5)

The result is for the marker detection test for 8 clusters in the dataset bycomparing each cluster against all other clusters.

Usage

deg.marker

Format

data.frame object of 1992 rows with columns:

• feature: gene names, 249 unique genes repeated 8 times for the testsdone for 8 clusters.

• group: cluster names, 8 unique cluster names, dividing the tests.

• logFC: log fold change of the gene expression between the cluster ofinterest against all other clusters.

• pval: p-value of the DEG test.

• padj: adjusted p-value of the DEG test.

• pct_in: percentage of cells in the cluster of interest expressing thegene.

• pct_out: percentage of cells in all other clusters expressing the gene.

See Also

runMarkerDEG()

Data frame for example pairwise DEG test result

Description

The data frame is the direct output of pairwise DEG test applied on exampledataset which can be loaded withimportPBMC(). Cell type annotationwas obtained from SeuratData package, "ifnb" dataset, since they are thesame. Use the following command to reproduce the same result:

library(rliger)library(Seurat)library(SeuratData)lig <- importPBMC()ifnb <- LoadData("ifnb")lig$cell_type <- ifnb$seurat_annotationslig$condition_cell_type <- interaction(lig$dataset, lig$cell_type, drop = FALSE)deg.pw <- runPairwiseDEG(    object = lig,    groupTest = 'stim.CD14 Mono',    groupCtrl = 'ctrl.CD14 Mono',    variable1 = 'condition_cell_type')deg.pw <- deg.pw[order(deg.pw$padj)[1:1000],]```The result represents the statistics of DEG test between stim dataset againstctrl dataset, within the CD14 monocytes. The result is randomly sampled to1000 entries for minimum demonstration.[1:1000]: R:1:1000

Usage

deg.pw

Format

data.frame object of 1000 rows with columns:

• feature: gene names.

• group: class name within the variable being used for the test condition.

• logFC: log fold change of the gene expression between the condition ofinterest against the control condition.

• pval: p-value of the DEG test.

• padj: adjusted p-value of the DEG test.

• pct_in: percentage of cells in the condition of interest expressing thegene.

• pct_out: percentage of cells in the control condition expressing thegene.

See Also

runPairwiseDEG()

Downsample datasets

Description

This function mainly aims at downsampling datasets to a sizesuitable for plotting or expensive in-memmory calculation.

Users can balance the sample size of categories of interests withbalance. Multi-variable specification tobalance is supported,so that at mostmaxCells cells will be sampled from each combinationof categories from the variables. For example, when two datasets arepresented and three clusters labeled across them, there would then be at most2 \times 3 \times maxCells cells being selected. Note that"dataset" will automatically be added as one variable when balancingthe downsampling. However, if users want to balance the downsampling solelybasing on dataset origin, users have to explicitly setbalance ="dataset".

Usage

downsample(  object,  balance = NULL,  maxCells = 1000,  useDatasets = NULL,  seed = 1,  returnIndex = FALSE,  ...)

Arguments

Value

By default, a subset ofligerobject.Alternatively whenreturnIndex = TRUE, a numeric vector to be usedwith the original object.

Examples

# Subsetting an objectpbmc <- downsample(pbmc)# Creating a subsetting indexsampleIdx <- downsample(pbmcPlot, balance = "leiden_cluster",                        maxCells = 10, returnIndex = TRUE)plotClusterDimRed(pbmcPlot, cellIdx = sampleIdx)

Export predicted gene-pair interaction

Description

Export the predicted gene-pair interactions calculated byupstream functionlinkGenesAndPeaks into an Interact Track filewhich is compatible withUCSCGenome Browser.

Usage

exportInteractTrack(  corrMat,  pathToCoords,  useGenes = NULL,  outputPath = getwd())

Arguments

Value

No return value. A file located atoutputPath will be created.

Examples

bmmc <- normalize(bmmc)bmmc <- selectGenes(bmmc)bmmc <- scaleNotCenter(bmmc)if (requireNamespace("RcppPlanc", quietly = TRUE) &&    requireNamespace("GenomicRanges", quietly = TRUE) &&    requireNamespace("IRanges", quietly = TRUE) &&    requireNamespace("psych", quietly = TRUE)) {    bmmc <- runINMF(bmmc)    bmmc <- alignFactors(bmmc)    bmmc <- normalizePeak(bmmc)    bmmc <- imputeKNN(bmmc, reference = "atac", queries = "rna")    corr <- linkGenesAndPeaks(        bmmc, useDataset = "rna",        pathToCoords = system.file("extdata/hg19_genes.bed", package = "rliger")    )    resultPath <- tempfile()    exportInteractTrack(        corrMat = corr,        pathToCoords = system.file("extdata/hg19_genes.bed", package = "rliger"),        outputPath = resultPath    )    head(read.table(resultPath, skip = 1))}

Test all factors for enrichment in a gene set

Description

This function takes the factorizedW matrix, with gene loading infactors, to get the ranked gene list for each factor. Then it runs simplyimplemented GSEA against given gene sets. So if genes in the given gene setare top loaded in a factor, this function will return high positiveenrichment score (ES) as well as significant p-value.

For the returned result object, useprint() orsummary() toshow concise results, and useplot() to visualize the GSEA statistics.

This function can be useful in various scenarios:

For example, when clusters with strong cell cycle activity are detected,users can apply this function with cell cycle gene sets to identify if anyfactor is enriched with such genes. Then in the downstream when aligning theiNMF factor loadings, users can simply opt to exclude these factors so thevariation in cell cycle is regressed out. Objectscc.gene.human andcc.gene.mouse are deliverered in package for convenience.

In other cases, this function can also be used to understand the biologicalmeaning of each cluster. Since the downstream clustering result is largelydetermined by the top loaded factor in each cell, understanding whatgenes are loaded in the top factor helps understand the identity and activityof the cell. This will require users to have there own gene sets prepared.

Usage

factorGSEA(  object,  geneSet,  nPerm = 1000,  seed = 1,  verbose = getOption("ligerVerbose", TRUE))

Arguments

Value

IfgeneSet is a single character vector, returns a data framewith enrichment score (ES), normalized enrichment score (NES), and p-valuefor the test in each factor. IfgeneSet is a list, returns a list ofsuch data frames.

Examples

pbmc <- pbmc %>%    selectBatchHVG() %>%    scaleNotCenter() %>%    runINMF()factorGSEAres <- factorGSEA(pbmc, ccGeneHuman)# Print summary of significant resultsprint(factorGSEAres)summary(factorGSEAres)# Make GSEA plot for certain gene set and factorplot(factorGSEAres, geneSetName = 'g2m.genes', useFactor = 'Factor_1')

Find shared and dataset-specific markers

Description

Applies various filters to genes on the shared (W) anddataset-specific (V) components of the factorization, before selectingthose which load most significantly on each factor (in a shared ordataset-specific way).

Usage

getFactorMarkers(  object,  dataset1,  dataset2,  factorShareThresh = 10,  datasetSpecificity = NULL,  logFCThresh = 1,  pvalThresh = 0.05,  nGenes = 30,  printGenes = FALSE,  verbose = getOption("ligerVerbose", TRUE),  factor.share.thresh = factorShareThresh,  dataset.specificity = datasetSpecificity,  log.fc.thresh = logFCThresh,  pval.thresh = pvalThresh,  num.genes = nGenes,  print.genes = printGenes)

Arguments

Value

A list object consisting of the following entries:

Examples

library(dplyr)result <- getFactorMarkers(pbmcPlot, dataset1 = "ctrl", dataset2 = "stim")print(class(result))print(names(result))result$shared %>% group_by(factor_num) %>% top_n(2, logFC)

Calculate proportion mitochondrial contribution

Description

Calculates proportion of mitochondrial contribution based on raw ornormalized data.

Usage

getProportionMito(object, use.norm = FALSE, pattern = "^mt-")

Arguments

Value

Named vector containing proportion of mitochondrial contribution foreach cell.

Note

getProportionMito will be deprecated becauserunGeneralQC generally covers and expands its use case.

Examples

# Example dataset does not contain MT genes, expected to see a messagepbmc$mito <- getProportionMito(pbmc)

Import prepared dataset publically available

Description

These are functions to download example datasets that are subset from publicdata.

• PBMC - Downsampled from GSE96583, Kang et al, NatureBiotechnology, 2018. Contains two scRNAseq datasets.

• BMMC - Downsampled from GSE139369, Granja et al, NatureBiotechnology, 2019. Contains two scRNAseq datasets and one scATAC data.

• CGE - Downsampled from GSE97179, Luo et al, Science, 2017.Contains one scRNAseq dataset and one DNA methylation data.

Usage

importPBMC(  dir = getwd(),  overwrite = FALSE,  method = "libcurl",  verbose = getOption("ligerVerbose", TRUE),  ...)importBMMC(  dir = getwd(),  overwrite = FALSE,  method = "libcurl",  verbose = getOption("ligerVerbose", TRUE),  ...)importCGE(  dir = getwd(),  overwrite = FALSE,  method = "libcurl",  verbose = getOption("ligerVerbose", TRUE),  ...)

Arguments

Value

Constructedliger object with QC performed and missingdata removed.

Examples

pbmc <- importPBMC()bmmc <- importBMMC()cge <- importCGE()

Impute the peak counts from gene expression data referring to an ATAC datasetafter integration

Description

This function is designed for creating peak data for a dataset with only geneexpression. This function uses aligned cell factor loading to find nearestneighbors between cells from the queried dataset (without peak) and cellsfrom reference dataset (with peak). And then impute the peak for the formerbasing on the weight. Therefore, the reference dataset selected must be of"atac" modality setting.

Usage

imputeKNN(  object,  reference,  queries = NULL,  nNeighbors = 20,  weight = TRUE,  norm = TRUE,  scale = FALSE,  verbose = getOption("ligerVerbose", TRUE),  ...,  knn_k = nNeighbors)

Arguments

Value

The inputobject where queriedligerDatasetobjects indatasets slot are replaced. These datasets will all beconverted toligerATACDataset class with an additional slotrawPeak to store the imputed peak counts, andnormPeak fornormalized imputed peak counts ifnorm = TRUE.

Examples

bmmc <- normalize(bmmc)bmmc <- selectGenes(bmmc, datasets.use = "rna")bmmc <- scaleNotCenter(bmmc)if (requireNamespace("RcppPlanc", quietly = TRUE)) {    bmmc <- runINMF(bmmc, k = 20)    bmmc <- alignFactors(bmmc)    bmmc <- normalizePeak(bmmc)    bmmc <- imputeKNN(bmmc, reference = "atac", queries = "rna")}

Check if given liger object if under new implementation

Description

Check if given liger object if under new implementation

Usage

is.newLiger(object)

Arguments

Value

TRUE if the version ofobject is later than or equal to1.99.0. OtherwiseFALSE. It raises an error if input object is not ofliger class.

Examples

is.newLiger(pbmc) # TRUE

Check if a liger or ligerDataset object is made of HDF5 file

Description

Check if a liger or ligerDataset object is made of HDF5 file

Usage

isH5Liger(object, dataset = NULL)

Arguments

Value

TRUE orFALSE for the specified check.

Examples

isH5Liger(pbmc)isH5Liger(pbmc, "ctrl")ctrl <- dataset(pbmc, "ctrl")isH5Liger(ctrl)

liger class

Description

liger object is the main data container for LIGERanalysis in R. The slotdatasets is a list where each element shouldbe aligerDataset object containing dataset specificinformation, such as the expression matrices. The other parts of liger objectstores information that can be shared across the analysis, such as the cellmetadata.

This manual provides explanation to theliger object structure as wellas usage of class-specific methods. Please see detail sections for moreinformation.

Forliger objects created with older versions of rliger package,please try updating the objects individually withconvertOldLiger.

Usage

datasets(x, check = NULL)datasets(x, check = TRUE) <- valuedataset(x, dataset = NULL)dataset(x, dataset, type = NULL, qc = TRUE) <- valuecellMeta(  x,  columns = NULL,  useDatasets = NULL,  cellIdx = NULL,  as.data.frame = FALSE,  ...)cellMeta(  x,  columns = NULL,  useDatasets = NULL,  cellIdx = NULL,  inplace = FALSE,  check = FALSE) <- valuedefaultCluster(x, useDatasets = NULL, ...)defaultCluster(x, name = NULL, useDatasets = NULL, ...) <- valuedimReds(x)dimReds(x) <- valuedimRed(x, name = NULL, useDatasets = NULL, cellIdx = NULL, ...)dimRed(x, name = NULL, useDatasets = NULL, cellIdx = NULL, ...) <- valuedefaultDimRed(x, useDatasets = NULL, cellIdx = NULL)defaultDimRed(x) <- valuevarFeatures(x)varFeatures(x, check = TRUE) <- valuevarUnsharedFeatures(x, dataset = NULL)varUnsharedFeatures(x, dataset, check = TRUE) <- valuecommands(x, funcName = NULL, arg = NULL)## S4 method for signature 'liger'show(object)## S4 method for signature 'liger'dim(x)## S4 method for signature 'liger'dimnames(x)## S4 replacement method for signature 'liger,list'dimnames(x) <- value## S4 method for signature 'liger'datasets(x, check = NULL)## S4 replacement method for signature 'liger,logical'datasets(x, check = TRUE) <- value## S4 replacement method for signature 'liger,missing'datasets(x, check = TRUE) <- value## S4 method for signature 'liger,character_OR_NULL'dataset(x, dataset = NULL)## S4 method for signature 'liger,missing'dataset(x, dataset = NULL)## S4 method for signature 'liger,numeric'dataset(x, dataset = NULL)## S4 replacement method for signature 'liger,character,missing,ANY,ligerDataset'dataset(x, dataset, type = NULL, qc = TRUE) <- value## S4 replacement method for signature 'liger,character,ANY,ANY,matrixLike'dataset(x, dataset, type = c("rawData", "normData"), qc = FALSE) <- value## S4 replacement method for signature 'liger,character,missing,ANY,NULL'dataset(x, dataset, type = NULL, qc = TRUE) <- value## S3 method for class 'liger'names(x)## S3 replacement method for class 'liger'names(x) <- value## S3 method for class 'liger'length(x)## S3 method for class 'liger'lengths(x, use.names = TRUE)## S4 method for signature 'liger,NULL'cellMeta(  x,  columns = NULL,  useDatasets = NULL,  cellIdx = NULL,  as.data.frame = FALSE,  ...)## S4 method for signature 'liger,character'cellMeta(  x,  columns = NULL,  useDatasets = NULL,  cellIdx = NULL,  as.data.frame = FALSE,  ...)## S4 method for signature 'liger,missing'cellMeta(  x,  columns = NULL,  useDatasets = NULL,  cellIdx = NULL,  as.data.frame = FALSE,  ...)## S4 replacement method for signature 'liger,missing'cellMeta(x, columns = NULL, useDatasets = NULL, cellIdx = NULL, check = FALSE) <- value## S4 replacement method for signature 'liger,character'cellMeta(  x,  columns = NULL,  useDatasets = NULL,  cellIdx = NULL,  inplace = TRUE,  check = FALSE) <- value## S4 method for signature 'liger'rawData(x, dataset = NULL)## S4 replacement method for signature 'liger,ANY,ANY,matrixLike_OR_NULL'rawData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'liger,ANY,ANY,H5D'rawData(x, dataset = NULL, check = TRUE) <- value## S4 method for signature 'liger'normData(x, dataset = NULL)## S4 replacement method for signature 'liger,ANY,ANY,matrixLike_OR_NULL'normData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'liger,ANY,ANY,H5D'normData(x, dataset = NULL, check = TRUE) <- value## S4 method for signature 'liger,ANY'scaleData(x, dataset = NULL)## S4 replacement method for signature 'liger,ANY,ANY,matrixLike_OR_NULL'scaleData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'liger,ANY,ANY,H5D'scaleData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'liger,ANY,ANY,H5Group'scaleData(x, dataset = NULL, check = TRUE) <- value## S4 method for signature 'liger,character'scaleUnsharedData(x, dataset = NULL)## S4 method for signature 'liger,numeric'scaleUnsharedData(x, dataset = NULL)## S4 replacement method for signature 'liger,ANY,ANY,matrixLike_OR_NULL'scaleUnsharedData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'liger,ANY,ANY,H5D'scaleUnsharedData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'liger,ANY,ANY,H5Group'scaleUnsharedData(x, dataset = NULL, check = TRUE) <- value## S4 method for signature 'liger,ANY,ANY,ANY'getMatrix(  x,  slot = c("rawData", "normData", "scaleData", "scaleUnsharedData", "H", "V", "U", "A",    "B", "W", "H.norm", "rawPeak", "normPeak"),  dataset = NULL,  returnList = FALSE)## S4 method for signature 'liger,ANY'getH5File(x, dataset = NULL)## S3 replacement method for class 'liger'x[[i]] <- value## S3 method for class 'liger'x$name## S3 replacement method for class 'liger'x$name <- value## S4 method for signature 'liger'defaultCluster(x, useDatasets = NULL, droplevels = FALSE, ...)## S4 replacement method for signature 'liger,ANY,ANY,character'defaultCluster(x, name = NULL, useDatasets = NULL, ...) <- value## S4 replacement method for signature 'liger,ANY,ANY,factor'defaultCluster(x, name = NULL, useDatasets = NULL, droplevels = TRUE, ...) <- value## S4 replacement method for signature 'liger,ANY,ANY,NULL'defaultCluster(x, name = NULL, useDatasets = NULL, ...) <- value## S4 method for signature 'liger'dimReds(x)## S4 replacement method for signature 'liger,list'dimReds(x) <- value## S4 method for signature 'liger,missing_OR_NULL'dimRed(x, name = NULL, useDatasets = NULL, cellIdx = NULL, ...)## S4 method for signature 'liger,index'dimRed(x, name = NULL, useDatasets = NULL, cellIdx = NULL, ...)## S4 replacement method for signature 'liger,index,ANY,ANY,NULL'dimRed(x, name = NULL, useDatasets = NULL, cellIdx = NULL, ...) <- value## S4 replacement method for signature 'liger,character,ANY,ANY,matrixLike'dimRed(  x,  name = NULL,  useDatasets = NULL,  cellIdx = NULL,  asDefault = NULL,  inplace = FALSE,  ...) <- value## S4 method for signature 'liger'defaultDimRed(x, useDatasets = NULL, cellIdx = NULL)## S4 replacement method for signature 'liger,character'defaultDimRed(x) <- value## S4 method for signature 'liger'varFeatures(x)## S4 replacement method for signature 'liger,ANY,character'varFeatures(x, check = TRUE) <- value## S4 method for signature 'liger,ANY'varUnsharedFeatures(x, dataset = NULL)## S4 replacement method for signature 'liger,ANY,ANY,character'varUnsharedFeatures(x, dataset, check = TRUE) <- value## S3 method for class 'liger'fortify(model, data, ...)## S3 method for class 'liger'c(...)## S4 method for signature 'liger'commands(x, funcName = NULL, arg = NULL)## S4 method for signature 'ligerDataset,missing'varUnsharedFeatures(x, dataset = NULL)## S4 replacement method for signature 'ligerDataset,missing,ANY,character'varUnsharedFeatures(x, dataset = NULL, check = TRUE) <- value

Arguments

Value

See detailed sections for explanetion.

Input liger object updated with replaced/new variable incellMeta(x).

Slots

datasets

list ofligerDataset objects. Use genericdataset,dataset<-,datasets ordatasets<- tointeract with. See detailed section accordingly.

cellMeta

DFrame object for cell metadata. Pre-existingmetadata, QC metrics, cluster labeling and etc. are all stored here. UsegenericcellMeta,cellMeta<-,$,[[]] or[[]]<- to interact with. See detailed section accordingly.

varFeatures

Character vector of names of variable features. Use genericvarFeatures orvarFeatures<- to interact with. See detailedsection accordingly.

W

iNMF output matrix of shared gene loadings for each factor. SeerunIntegration.

H.norm

Matrix of aligned factor loading for each cell. SeealignFactors andrunIntegration.

commands

List ofligerCommand objects. Record ofanalysis. Usecommands to retrieve information. See detailed sectionaccordingly.

uns

List for unstructured meta-info of analyses or presets.

version

Record of version of rliger package

Dataset access

datasets() method only accesses thedatasets slot, the list ofligerDataset objects.dataset() method accesses a singledataset, with subsequent cell metadata updates and checks bonded when addingor modifying a dataset. Therefore, when users want to modify something insidealigerDataset while no cell metadata change should happen, it isrecommended to use:datasets(x)[[name]] <- ligerD for efficiency,though the result would be the same asdataset(x, name) <- ligerD.

length() andnames() methods are implemented to access thenumber and names of datasets.names<- method is supported formodifying dataset names, with taking care of the "dataset" variable in cellmetadata.

Matrix access

Forliger object,rawData(),normData,scaleData() andscaleUnsharedData() methods are exported forusers to access the corresponding feature expression matrix withspecification of one dataset. For retrieving a type of matrix from multipledatasets, please usegetMatrix() method.

When only one matrix is expected to be retrieved bygetMatrix(), thematrix itself will be returned. A list will be returned if multiple matricesis requested (by querying multiple datasets) orreturnList is set toTRUE.

Cell metadata access

Three approaches are provided for access of cell metadata. A generic functioncellMeta is implemented with plenty of options and multi-variableaccessibility. Besides, users can use double-bracket (e.g.ligerObj[[varName]]) or dollor-sign (e.g.ligerObj$nUMI) toaccess or modify single variables.

For users' convenience of generating a customized ggplot with available cellmetadata, the S3 methodfortify.liger is implemented. With this underthe hook, users can create simple ggplots by directly starting withggplot(ligerObj, aes(...)) where cell metadata variables can bedirectly thrown intoaes().

Special partial metadata insertion is implemented specifically for mappingcategorical annotation from sub-population (subset object) back to originalexperiment (full-size object). For example, when sub-clustering andannotation is done for a specific cell-type of cells (stored insubobj) subset from an experiment (stored asobj), users can docellMeta(obj, "sub_ann", cellIdx = colnames(subobj)) <- subobj$sub_annto map the value back, leaving other cells non-annotated with NAs. Plottingwith this variable will then also show NA cells with default grey color.Furthermore, sub-clustering labels for other cell types can also be mappedto the same variable. For example,cellMeta(obj, "sub_ann",cellIdx = colnames(subobj2)) <- subobj2$sub_ann. As long as the labelingvariables are stored as factor class (categorical), the levels (categorynames) will be properly handled and merged. Other situations follow the Rdefault behavior (e.g. categories might be converted to integer numbers ifmapped to numerical variable in the original object). Note that this featureis only available with using the generic functioncellMeta but notwith the`[[` or`$` accessing methods due to syntax reasons.

The genericdefaultCluster works as both getter and setter. As asetter, users can dodefaultCluster(obj) <- "existingVariableName" toset a categorical variable as default cluster used for visualization ordownstream analysis. Users can also dodefaultCluster(obj,"newVarName") <- factorOfLabels to push new labeling into the object and setas default. For getter method, the function returns a factor object of thedefault cluster labeling. ArgumentuseDatasets can be used forrequiring that given or retrieved labeling should match with cells inspecified datasets. We generally don't recommend setting"dataset" asa default cluster because it is a preserved (always existing) field inmetadata and can lead to meaningless result when running analysis thatutilizes both clustering information and the dataset source information.

Dimension reduction access

Currently, low-dimensional representaion of cells, presented as densematrices, are all stored indimReds slot, and can totally be accessedwith genericsdimRed anddimRed<-. Adding a dimRed to theobject looks as simple asdimRed(obj, "name") <- matrixLike. It canbe retrieved back withdimRed(obj, "name"). Similar to having adefault cluster labeling, we also constructed the feature of default dimRed.It can be set withdefaultDimRed(obj) <- "existingMatLikeVar" and thematrix can be retrieved withdefaultDimRed(obj).

Variable feature access

ThevarFeatures slot allows for character vectors of gene names.varFeatures(x) returns this vector andvalue forvarFeatures<- method has to be a character vector orNULL.The replacement method, whencheck = TRUE performs checks on genename consistency check across thescaleData,H,V slotsof innerligerDataset objects as well as theW andH.norm slots of the inputliger object.

Command records

rliger functions, that perform calculation and update theligerobject, will be recorded in aligerCommand object and stored in thecommands slot, a list, ofliger object. Methodcommands() is implemented to retrieve or show the log history.Running withfuncName = NULL (default) returns all command labels.SpecifyingfuncName allows partial matching to all command labelsand returns a subset list (ofligerCommand object) of matches (ortheligerCommand object if only one match found). Ifarg isfurther specified, a subset list of parameters from the matches will bereturned. For example, requesting a list of resolution values used inall louvain cluster attempts:commands(ligerObj, "louvainCluster","resolution")

Dimensionality

For aliger object, the column orientation is assigned forcells. Due to the data structure, it is hard to define a row index for theliger object, which might contain datasets that vary in number ofgenes.

Therefore, forliger objects,dim anddimnames returnsNA/NULL for rows and total cell counts/barcodes for thecolumns.

For direct call ofdimnames<- method,value should be a listwithNULL as the first element and valid cell identifiers as thesecond element. Forcolnames<- method, the character vector of cellidentifiers.rownames<- method is not applicable.

Subsetting

For more detail of subsetting aliger object or aligerDataset object, please check outsubsetLigerandsubsetLigerDataset. Here, we set the S4 method"single-bracket"[ as a quick wrapper to subset aliger object.Note thatj serves as cell subscriptor which can be any valid indexrefering the collection of all cells (i.e.rownames(cellMeta(obj))).Whilei, the feature subscriptor can only be character vector becausethe features for each dataset can vary.... arugments are passed tosubsetLiger so that advanced options are allowed.

Combining multiple liger object

The list ofdatasets slot,the rows ofcellMeta slot and the list ofcommands slot willbe simply concatenated. Variable features invarFeatures slot will betaken a union. TheW andH.norm matrices are not taken intoaccount for now.

Examples

# Methods for base genericspbmcPlotprint(pbmcPlot)dim(pbmcPlot)ncol(pbmcPlot)colnames(pbmcPlot)[1:5]pbmcPlot[varFeatures(pbmcPlot)[1:10], 1:10]names(pbmcPlot)length(pbmcPlot)# rliger generics## Retrieving dataset(s), replacement methods availabledatasets(pbmcPlot)dataset(pbmcPlot, "ctrl")dataset(pbmcPlot, 2)## Retrieving cell metadata, replacement methods availablecellMeta(pbmcPlot)head(pbmcPlot[["nUMI"]])## Retrieving dimemtion reduction matrixhead(dimRed(pbmcPlot, "UMAP"))## Retrieving variable features, replacement methods availablevarFeatures(pbmcPlot)## Command record/historypbmcPlot <- scaleNotCenter(pbmcPlot)commands(pbmcPlot)commands(pbmcPlot, funcName = "scaleNotCenter")# S3 methodspbmcPlot2 <- pbmcPlotnames(pbmcPlot2) <- paste0(names(pbmcPlot), 2)c(pbmcPlot, pbmcPlot2)library(ggplot2)ggplot(pbmcPlot, aes(x = UMAP_1, y = UMAP_2)) + geom_point()cellMeta(pbmc)# Add new variablepbmc[["newVar"]] <- 1cellMeta(pbmc)# Change existing variablepbmc[["newVar"]][1:3] <- 1:3cellMeta(pbmc)

Subclass of ligerDataset for ATAC modality

Description

Inherits fromligerDataset class. Contained slotscan be referred with the link.

Slots

rawPeak

sparse matrix

normPeak

sparse matrix

ligerCommand object: Record the input and time of a LIGER function call

Description

ligerCommand object: Record the input and time of a LIGER function call

Usage

## S4 method for signature 'ligerCommand'show(object)

Arguments

Slots

funcName

Name of the function

time

A time stamp object

call

A character string converted from system call

parameters

List of all arguments except theliger object.Large object are summarized to short string.

objSummary

List of attributes of theliger object as asnapshot when command is operated.

ligerVersion

Character string converted frompackageVersion("rliger").

dependencyVersion

Named character vector of version number, if anydependency library has a chance to be included by the function. Adependency might only be invoked under certain conditions, such as usingan alternative algorithm, which a call does not actually reach to, but itwould still be included for this call.

Examples

pbmc <- normalize(pbmc)cmd <- commands(pbmc, "normalize")cmd

ligerDataset class

Description

Object for storing dastaset specific information. Will be embedded within ahigher levelliger object

Usage

rawData(x, dataset = NULL)rawData(x, dataset = NULL, check = TRUE) <- valuenormData(x, dataset = NULL)normData(x, dataset = NULL, check = TRUE) <- valuescaleData(x, dataset = NULL)scaleData(x, dataset = NULL, check = TRUE) <- valuescaleUnsharedData(x, dataset = NULL)scaleUnsharedData(x, dataset = NULL, check = TRUE) <- valuegetMatrix(x, slot = "rawData", dataset = NULL, returnList = FALSE)h5fileInfo(x, info = NULL)h5fileInfo(x, info = NULL, check = TRUE) <- valuegetH5File(x, dataset = NULL)## S4 method for signature 'ligerDataset,missing'getH5File(x, dataset = NULL)featureMeta(x, check = NULL)featureMeta(x, check = TRUE) <- value## S4 method for signature 'ligerDataset'show(object)## S4 method for signature 'ligerDataset'dim(x)## S4 method for signature 'ligerDataset'dimnames(x)## S4 replacement method for signature 'ligerDataset,list'dimnames(x) <- value## S4 method for signature 'ligerDataset'rawData(x, dataset = NULL)## S4 replacement method for signature 'ligerDataset,ANY,ANY,matrixLike_OR_NULL'rawData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'ligerDataset,ANY,ANY,H5D'rawData(x, dataset = NULL, check = TRUE) <- value## S4 method for signature 'ligerDataset'normData(x, dataset = NULL)## S4 replacement method for signature 'ligerDataset,ANY,ANY,matrixLike_OR_NULL'normData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'ligerDataset,ANY,ANY,H5D'normData(x, dataset = NULL, check = TRUE) <- value## S4 method for signature 'ligerDataset,missing'scaleData(x, dataset = NULL)## S4 replacement method for signature 'ligerDataset,ANY,ANY,matrixLike_OR_NULL'scaleData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'ligerDataset,ANY,ANY,H5D'scaleData(x, dataset = NULL, check = TRUE) <- value## S4 replacement method for signature 'ligerDataset,ANY,ANY,H5Group'scaleData(x, dataset = NULL, check = TRUE) <- value## S4 method for signature 'ligerDataset,missing'scaleUnsharedData(x, dataset = NULL)## S4 replacement method for signature 'ligerDataset,missing,ANY,matrixLike_OR_NULL'scaleUnsharedData(x, check = TRUE) <- value## S4 replacement method for signature 'ligerDataset,missing,ANY,H5D'scaleUnsharedData(x, check = TRUE) <- value## S4 replacement method for signature 'ligerDataset,missing,ANY,H5Group'scaleUnsharedData(x, check = TRUE) <- value## S4 method for signature 'ligerDataset,ANY,missing,missing'getMatrix(  x,  slot = c("rawData", "normData", "scaleData", "scaleUnsharedData", "H", "V", "U", "A",    "B"),  dataset = NULL)## S4 method for signature 'ligerDataset'h5fileInfo(x, info = NULL)## S4 replacement method for signature 'ligerDataset'h5fileInfo(x, info = NULL, check = TRUE) <- value## S4 method for signature 'ligerDataset'featureMeta(x, check = NULL)## S4 replacement method for signature 'ligerDataset'featureMeta(x, check = TRUE) <- value## S3 method for class 'ligerDataset'cbind(x, ..., deparse.level = 1)## S4 method for signature 'ligerATACDataset,ANY,missing,missing'getMatrix(  x,  slot = c("rawData", "normData", "scaleData", "scaleUnsharedData", "H", "V", "U", "A",    "B", "rawPeak", "normPeak"),  dataset = NULL)

Arguments

Slots

rawData

Raw data. Feature by cell matrix. Most of the time, sparsematrix of integer numbers for RNA and ATAC data.

normData

Normalized data. Feature by cell matrix. Sparse if therawData it is normalized from is sparse.

scaleData

Scaled data, usually with subset shared variable features, bycells. Most of the time sparse matrix of float numbers. This is the data usedfor iNMF factorization.

scaleUnsharedData

Scaled data of variable features not shared withother datasets. This is the data used for UINMF factorization.

varUnsharedFeatures

Variable features not shared with other datasets.

V

iNMF output matrix holding the dataset specific gene loading of eachfactor. Feature by factor matrix.

A

Online iNMF intermediate product matrix.

B

Online iNMF intermediate product matrix.

H

iNMF output matrix holding the factor loading of each cell. Factor bycell matrix.

U

UINMF output matrix holding the unshared variable gene loading ofeach factor. Feature by factor matrix.

h5fileInfo

list of meta information of HDF5 file used for constructingthe object.

featureMeta

Feature metadata, DataFrame object.

colnames

Character vector of unique cell identifiers.

rownames

Character vector of unique feature names.

Matrix access

ForligerDataset object,rawData(),normData,scaleData() andscaleUnsharedData() methods are exported forusers to access the corresponding feature expression matrix. Replacementmethods are also available to modify the slots.

For other matrices, such as theH andV, which are datasetspecific, please usegetMatrix() method with specifying slot name.Directly accessing slot with@ is generally not recommended.

H5 file and information access

AligerDataset object has a slot calledh5fileInfo, which is alist object. The first element is called$H5File, which is anH5File class object and is the connection to the input file. Thesecond element is$filename which stores the absolute path of the H5file in the current machine. The third element$formatType stores thename of preset being used, if applicable. The other following keys pair withpaths in the H5 file that point to specific data for constructing a featureexpression matrix.

h5fileInfo() method access the list described above and simplyretrieves the corresponding value. Wheninfo = NULL, returns the wholelist. Whenlength(info) == 1, returns the requested list value. Whenmore info requested, returns a subset list.

The replacement method modifies the list elements and corresponding slotvalue (if applicable) at the same time. For example, runningh5fileInfo(obj, "rawData") <- newPath not only updates the list, butalso updates therawData slot with theH5D class data at"newPath" in theH5File object.

getH5File() is a wrapper and is equivalent toh5fileInfo(obj, "H5File").

Feature metadata access

A slotfeatureMeta is included for eachligerDataset object.This slot requires aDataFrame-class object, whichis the same ascellMeta slot of aliger object. However,the associated S4 methods only include access to the whole table for now.Internal information access follows the same way as data.frame operation.For example,featureMeta(ligerD)$nCell orfeatureMeta(ligerD)[varFeatures(ligerObj), "gene_var"].

Dimensionality

For aligerDataset object, the column orientation is assigned forcells and rows are for features. Therefore, forligerDataset objects,dim() returns a numeric vector of two numbers which are number offeatures and number of cells.dimnames() returns a list of twocharacter vectors, which are the feature names and the cell barcodes.

For direct call ofdimnames<- method,value should be a listwith a character vector of feature names as the first element and cellidentifiers as the second element. Forcolnames<- method, thecharacter vector of cell identifiers. Forrownames<- method, thecharacter vector of feature names.

Subsetting

For more detail of subsetting aliger object or aligerDataset object, please check outsubsetLigerandsubsetLigerDataset. Here, we set the S3 method"single-bracket"[ as a quick wrapper to subset aligerDatasetobject.i andj serves as feature and cell subscriptor,respectively, which can be any valid index refering the available featuresand cells in a dataset.... arugments are passed tosubsetLigerDataset so that advanced options are allowed.

Concatenate ligerDataset

cbind() method is implemented for concatenatingligerDatasetobjects by cells. When applying, all feature expression matrix will be mergedwith taking a union of all features for the rows.

Examples

ctrl <- dataset(pbmc, "ctrl")# Methods for base genericsctrlprint(ctrl)dim(ctrl)ncol(ctrl)nrow(ctrl)colnames(ctrl)[1:5]rownames(ctrl)[1:5]ctrl[1:5, 1:5]# rliger generics## raw datam <- rawData(ctrl)class(m)dim(m)## normalized datapbmc <- normalize(pbmc)ctrl <- dataset(pbmc, "ctrl")m <- normData(ctrl)class(m)dim(m)## scaled datapbmc <- selectGenes(pbmc)pbmc <- scaleNotCenter(pbmc)ctrl <- dataset(pbmc, "ctrl")m <- scaleData(ctrl)class(m)dim(m)n <- scaleData(pbmc, "ctrl")identical(m, n)## Any other matricesif (requireNamespace("RcppPlanc", quietly = TRUE)) {    pbmc <- runOnlineINMF(pbmc, k = 20, minibatchSize = 100)    ctrl <- dataset(pbmc, "ctrl")    V <- getMatrix(ctrl, "V")    V[1:5, 1:5]    Vs <- getMatrix(pbmc, "V")    length(Vs)    names(Vs)    identical(Vs$ctrl, V)}

Subclass of ligerDataset for Methylation modality

Description

Inherits fromligerDataset class. Contained slotscan be referred with the link.scaleNotCenter applied ondatasets of this class will automatically be taken by reversing thenormalized data instead of scaling the variable features.

Subclass of ligerDataset for RNA modality

Description

Inherits fromligerDataset class. Contained slotscan be referred with the link. This subclass does not have any different fromthe defaultligerDataset class except the class name.

Subclass of ligerDataset for Spatial modality

Description

Inherits fromligerDataset class. Contained slotscan be referred with the link.

Slots

coordinate

dense matrix

Convert between liger and Seurat object

Description

For converting aliger object to a Seurat object, therawData,normData, andscaleData from each dataset,thecellMeta,H.norm andvarFeatures slot will beincluded. Compatible with V4 and V5. It is not recommended to use thisconversion if yourliger object contains datasets fromvarious modalities.

Usage

ligerToSeurat(  object,  assay = NULL,  identByDataset = FALSE,  merge = FALSE,  nms = NULL,  renormalize = NULL,  use.liger.genes = NULL,  by.dataset = identByDataset)

Arguments

Value

Always returns Seurat object(s) of the latest version. By default aSeurat object with split layers, e.g. with layers like "counts.ctrl" and"counts.stim". Ifmerge = TRUE, return a single Seurat object withlayers for all datasets merged.

Examples

if (requireNamespace("SeuratObject", quietly = TRUE) &&    requireNamespace("Seurat", quietly = TRUE)) {    seu <- ligerToSeurat(pbmc)}

Linking genes to putative regulatory elements

Description

Evaluate the relationships between pairs of genes and peaksbased on specified distance metric. Usually used for inferring thecorrelation between gene expression and imputed peak counts for datasetswithout the modality originally (i.e. applied toimputeKNNresult).

Usage

linkGenesAndPeaks(  object,  useDataset,  pathToCoords,  useGenes = NULL,  method = c("spearman", "pearson", "kendall"),  alpha = 0.05,  verbose = getOption("ligerVerbose", TRUE),  path_to_coords = pathToCoords,  genes.list = useGenes,  dist = method)

Arguments

Value

A sparse matrix with peak names as rows and gene names as columns,with each element indicating the correlation between peak i and gene j, 0 ifthe gene and peak are not significantly linked.

See Also

imputeKNN

Examples

if (requireNamespace("RcppPlanc", quietly = TRUE) &&    requireNamespace("GenomicRanges", quietly = TRUE) &&    requireNamespace("IRanges", quietly = TRUE) &&    requireNamespace("psych", quietly = TRUE)) {    bmmc <- normalize(bmmc)    bmmc <- selectGenes(bmmc)    bmmc <- scaleNotCenter(bmmc)    bmmc <- runINMF(bmmc, miniBatchSize = 100)    bmmc <- alignFactors(bmmc)    bmmc <- normalizePeak(bmmc)    bmmc <- imputeKNN(bmmc, reference = "atac", queries = "rna")    corr <- linkGenesAndPeaks(        bmmc, useDataset = "rna",        pathToCoords = system.file("extdata/hg19_genes.bed", package = "rliger")    )}

Louvain algorithm for community detection

Description

After quantile normalization, users can additionally run the Louvainalgorithm for community detection, which is widely used in single-cellanalysis and excels at merging small clusters into broad cell classes.

Arguments

Value

object with refined cluster assignment updated in"louvain_cluster" variable incellMeta slot. Can be fetchedwithobject$louvain_cluster

See Also

rliger-deprecated

Fast calculation of feature count matrix

Description

Fast calculation of feature count matrix

Usage

makeFeatureMatrix(bedmat, barcodes)

Arguments

Value

A feature count matrix with features as rows and barcodes ascolumns

Examples

## Not run: gene.counts <- makeFeatureMatrix(genes.bc, barcodes)promoter.counts <- makeFeatureMatrix(promoters.bc, barcodes)samnple <- gene.counts + promoter.counts## End(Not run)

Deprecated functions in packagerliger.

Description

The functions listed below are deprecated and will be defunct inthe near future. When possible, alternative functions with similarfunctionality or a replacement are also mentioned. Help pages fordeprecated functions are available athelp("<function>-deprecated").

Usage

makeInteractTrack(  corr.mat,  path_to_coords,  genes.list = NULL,  output_path = getwd())louvainCluster(  object,  resolution = 1,  k = 20,  prune = 1/15,  eps = 0.1,  nRandomStarts = 10,  nIterations = 100,  random.seed = 1,  verbose = getOption("ligerVerbose", TRUE),  dims.use = NULL)optimizeALS(  object,  k,  lambda = 5,  thresh = NULL,  max.iters = 30,  nrep = 1,  H.init = NULL,  W.init = NULL,  V.init = NULL,  use.unshared = FALSE,  rand.seed = 1,  print.obj = NULL,  verbose = TRUE,  ...)online_iNMF(  object,  X_new = NULL,  projection = FALSE,  W.init = NULL,  V.init = NULL,  H.init = NULL,  A.init = NULL,  B.init = NULL,  k = 20,  lambda = 5,  max.epochs = 5,  miniBatch_max_iters = 1,  miniBatch_size = 5000,  h5_chunk_size = 1000,  seed = 123,  verbose = TRUE)quantile_norm(  object,  quantiles = 50,  ref_dataset = NULL,  min_cells = 20,  knn_k = 20,  dims.use = NULL,  do.center = FALSE,  max_sample = 1000,  eps = 0.9,  refine.knn = TRUE,  clusterName = "H.norm_cluster",  rand.seed = 1,  verbose = getOption("ligerVerbose", TRUE))makeRiverplot(  object,  cluster1,  cluster2,  cluster_consensus = NULL,  min.frac = 0.05,  min.cells = 10,  river.yscale = 1,  river.lty = 0,  river.node_margin = 0.1,  label.cex = 1,  label.col = "black",  lab.srt = 0,  river.usr = NULL,  node.order = "auto")

makeInteractTrack

FormakeInteractTrack, useexportInteractTrack.

louvainCluster

ForlouvainCluster, userunCluster(method = "louvain")as the replacement, whilerunCluster with defaultmethod = "leiden" is more recommended.

optimizeALS

ForoptimizeALS, userunIntegration orrunINMF. For the case ofoptimizeALS(use.unshared = TRUE), userunIntegrationwithmethod = "UINMF" orrunUINMF instead.

online_iNMF

Foronline_iNMF, userunIntegration withmethod = "online" orrunOnlineINMF.

quantile_norm

Forquantile_norm, usequantileNorm.

makeRiverplot

FormakeRiverplot, useplotSankey as the replacement.

Export predicted gene-pair interaction

Description

Export the predicted gene-pair interactions calculated byupstream functionlinkGenesAndPeaks into an Interact Track filewhich is compatible withUCSCGenome Browser.

Arguments

Value

No return value. A file located atoutputPath will be created.

See Also

rliger-deprecated,exportInteractTrack

Generate a river (Sankey) plot

Description

Creates a riverplot to show how separate cluster assignments from twodatasets map onto a joint clustering. The joint clustering is by default theobject clustering, but an external one can also be passed in. Uses theriverplot package to construct riverplot object and then plot.

Arguments

Value

object with refined cluster assignment updated in"louvain_cluster" variable incellMeta slot. Can be fetchedwithobject$louvain_cluster

See Also

rliger-deprecated

Create new variable from categories in cellMeta

Description

Designed for fast variable creation when a new variable is going to becreated from existing variable. For example, multiple samples can be mappedto the same study design condition, clusters can be mapped to cell types.

Usage

mapCellMeta(object, from, newTo = NULL, ...)

Arguments

Value

WhennewTo = NULL, a factor object of the new variable.Otherwise, the input object with variablenewTo updated incellMeta(object).

Examples

pbmc <- mapCellMeta(pbmc, from = "dataset", newTo = "modal",                    ctrl = "rna", stim = "rna")

Merge hdf5 files

Description

This function merges hdf5 files generated from differentlibraries (cell ranger by default) before they are preprocessed through Ligerpipeline.

Usage

mergeH5(  file.list,  library.names,  new.filename,  format.type = "10X",  data.name = NULL,  indices.name = NULL,  indptr.name = NULL,  genes.name = NULL,  barcodes.name = NULL)

Arguments

Value

Directly generates newly merged hdf5 file.

Examples

## Not run: # For instance, we want to merge two datasets saved in HDF5 files (10X# CellRanger) paths to datasets: "library1.h5","library2.h5"# dataset names: "lib1", "lib2"# name for output HDF5 file: "merged.h5"mergeH5(list("library1.h5","library2.h5"), c("lib1","lib2"), "merged.h5")## End(Not run)

Merge matrices while keeping the union of rows

Description

mergeSparseAll takes in a list of DGEs, with genes asrows and cells as columns, and merges them into a single DGE. Also addslibraryNames to colnames from each DGE if expected to be overlap(common with 10X barcodes). Values inrawData ornormDataslot of aligerDataset object can be processed with this.

For a list of dense matrices, usually the values inscaleData slot ofaligerDataset object, please usemergeDenseAll whichworks in the same way.

Usage

mergeSparseAll(  datalist,  libraryNames = NULL,  mode = c("union", "intersection"))mergeDenseAll(datalist, libraryNames = NULL)

Arguments

Value

dgCMatrix or matrix with all barcodes indatalist as columnsand the union of genes indatalist as rows.

Examples

rawDataList <- getMatrix(pbmc, "rawData")merged <- mergeSparseAll(rawDataList, libraryNames = names(pbmc))

Return preset modality of a ligerDataset object or that of all datasets in aliger object

Description

Return preset modality of a ligerDataset object or that of all datasets in aliger object

Usage

modalOf(object)

Arguments

Value

A single character of modality setting value forligerDatasetobject, or a named vector forliger object, where the names are dataset names.

Examples

modalOf(pbmc)ctrl <- dataset(pbmc, "ctrl")modalOf(ctrl)ctrl.atac <- as.ligerDataset(ctrl, modal = "atac")modalOf(ctrl.atac)

Normalize raw counts data

Description

Perform library size normalization on raw counts input. As forthe preprocessing step of iNMF integration, by default we don't multiply thenormalized values with a scale factor, nor do we take the log transformation.Applicable S3 methods can be found in Usage section.

normalizePeak is designed for datasets of "atac" modality, i.e. storedinligerATACDataset. S3 method for various container object isnot supported yet due to difference in architecture design.

Usage

normalize(object, ...)## S3 method for class 'matrix'normalize(object, log = FALSE, scaleFactor = NULL, ...)## S3 method for class 'dgCMatrix'normalize(object, log = FALSE, scaleFactor = NULL, ...)## S3 method for class 'DelayedArray'normalize(  object,  log = FALSE,  scaleFactor = NULL,  chunk = getOption("ligerChunkSize", 20000),  overwrite = FALSE,  returnStats = FALSE,  verbose = getOption("ligerVerbose", TRUE),  ...)## S3 method for class 'ligerDataset'normalize(  object,  chunk = getOption("ligerChunkSize", 20000),  verbose = getOption("ligerVerbose", TRUE),  ...)## S3 method for class 'liger'normalize(  object,  useDatasets = NULL,  verbose = getOption("ligerVerbose", TRUE),  format.type = NULL,  remove.missing = NULL,  ...)## S3 method for class 'Seurat'normalize(object, assay = NULL, layer = "counts", save = "ligerNormData", ...)normalizePeak(  object,  useDatasets = NULL,  verbose = getOption("ligerVerbose", TRUE),  ...)

Arguments

Value

Updatedobject.

• dgCMatrix method - Returns processed dgCMatrix object

• ligerDataset method - Updates thenormData slot of the object

• liger method - Updates thenormData slot of chosen datasets

• Seurat method - Adds a named layer in chosen assay (V5), or update thedata slot of the chosen assay (<=V4)

• normalizePeak - Updates thenormPeak slot of chosendatasets.

Examples

pbmc <- normalize(pbmc)

Perform online iNMF on scaled datasets

Description

Please turn torunOnlineINMF orrunIntegration.

Perform online integrative non-negative matrix factorization to representmultiple single-cell datasets in terms of H, W, and V matrices. It optimizesthe iNMF objective function using online learning (non-negative least squaresfor H matrix, hierarchical alternating least squares for W and V matrices),where the number of factors is set by k. The function allows online learningin 3 scenarios: (1) fully observed datasets; (2) iterative refinement usingcontinually arriving datasets; and (3) projection of new datasets withoutupdating the existing factorization. All three scenarios require fixed memoryindependent of the number of cells.

For each dataset, this factorization produces an H matrix (cells by k), a Vmatrix (k by genes), and a shared W matrix (k by genes). The H matricesrepresent the cell factor loadings. W is identical among all datasets, as itrepresents the shared components of the metagenes across datasets. The Vmatrices represent the dataset-specific components of the metagenes.

Arguments

Value

liger object with H, W, V, A and B slots set.

Perform iNMF on scaled datasets

Description

Please turn torunINMF orrunIntegration.

Perform integrative non-negative matrix factorization to return factorized H,W, and V matrices. It optimizes the iNMF objective function using blockcoordinate descent (alternating non-negative least squares), where the numberof factors is set by k. TODO: include objective function equation here indocumentation (using deqn)

For each dataset, this factorization produces an H matrix (cells by k), a Vmatrix (k by genes), and a shared W matrix (k by genes). The H matricesrepresent the cell factor loadings. W is held consistent among all datasets,as it represents the shared components of the metagenes across datasets. TheV matrices represent the dataset-specific components of the metagenes.

Arguments

Value

liger object with H, W, and V slots set.

See Also

rliger-deprecated

Perform factorization for new data

Description

Uses an efficient strategy for updating that takes advantage ofthe information in the existing factorization. Assumes that variable featuresare presented in the new datasets. Two modes are supported (controlled bymerge):

• Append new data to existing datasets specified byuseDatasets.Here the existingV matrices for the target datasets will directly beused as initialization, and newH matrices for the merged matrices willbe initialized accordingly.

• Set new data as new datasets. InitialV matrices for them willbe copied from datasets specified byuseDatasets, and newHmatrices will be initialized accordingly.

Usage

optimizeNewData(  object,  dataNew,  useDatasets,  merge = TRUE,  lambda = NULL,  nIteration = 30,  seed = 1,  verbose = getOption("ligerVerbose"),  new.data = dataNew,  which.datasets = useDatasets,  add.to.existing = merge,  max.iters = nIteration,  thresh = NULL)

Arguments

Value

object withW slot updated with the newWmatrix, and theH andV slots of eachligerDataset object in thedatasets slot updated withthe new dataset specificH andV matrix, respectively.

See Also

runINMF,optimizeNewK,optimizeNewLambda

Examples

pbmc <- normalize(pbmc)pbmc <- selectGenes(pbmc)pbmc <- scaleNotCenter(pbmc)# Only running a few iterations for fast examplesif (requireNamespace("RcppPlanc", quietly = TRUE)) {    pbmc <- runINMF(pbmc, k = 20, nIteration = 2)    # Create fake new data by increasing all non-zero count in "ctrl" by 1,    # and make unique cell identifiers    ctrl2 <- rawData(dataset(pbmc, "ctrl"))    ctrl2@x <- ctrl2@x + 1    colnames(ctrl2) <- paste0(colnames(ctrl2), 2)    pbmcNew <- optimizeNewData(pbmc, dataNew = list(ctrl2 = ctrl2),                               useDatasets = "ctrl", nIteration = 2)}

Perform factorization for new value of k

Description

This uses an efficient strategy for updating that takesadvantage of the information in the existing factorization. It is mostrecommended for values ofkNew smaller than current value (k,which is set when runningrunINMF), where it is more likely tospeed up the factorization.

Usage

optimizeNewK(  object,  kNew,  lambda = NULL,  nIteration = 30,  seed = 1,  verbose = getOption("ligerVerbose"),  k.new = kNew,  max.iters = nIteration,  rand.seed = seed,  thresh = NULL)

Arguments

Value

object withW slot updated with the newWmatrix, and theH andV slots of eachligerDataset object in thedatasets slot updated withthe new dataset specificH andV matrix, respectively.

See Also

runINMF,optimizeNewLambda,optimizeNewData

Examples

pbmc <- normalize(pbmc)pbmc <- selectGenes(pbmc)pbmc <- scaleNotCenter(pbmc)# Only running a few iterations for fast examplesif (requireNamespace("RcppPlanc", quietly = TRUE)) {    pbmc <- runINMF(pbmc, k = 20, nIteration = 2)    pbmc <- optimizeNewK(pbmc, kNew = 25, nIteration = 2)}

Perform factorization for new lambda value

Description

Uses an efficient strategy for updating that takes advantage ofthe information in the existing factorization; always uses previous k.Recommended mainly when re-optimizing for higher lambda and when new lambdavalue is significantly different; otherwise may not return optimal results.

Usage

optimizeNewLambda(  object,  lambdaNew,  nIteration = 30,  seed = 1,  verbose = getOption("ligerVerbose"),  new.lambda = lambdaNew,  max.iters = nIteration,  rand.seed = seed,  thresh = NULL)

Arguments

Value

Inputobject with optimized factorization values updated.including theW matrix inliger object, andH andV matrices in eachligerDataset object in thedatasets slot.

See Also

runINMF,optimizeNewK,optimizeNewData

Examples

pbmc <- normalize(pbmc)pbmc <- selectGenes(pbmc)pbmc <- scaleNotCenter(pbmc)if (requireNamespace("RcppPlanc", quietly = TRUE)) {    # Only running a few iterations for fast examples    pbmc <- runINMF(pbmc, k = 20, nIteration = 2)    # pbmc <- optimizeNewLambda(pbmc, lambdaNew = 5.5, nIteration = 2)}

Perform factorization for subset of data

Description

Uses an efficient strategy for updating that takes advantage ofthe information in the existing factorization.

Usage

optimizeSubset(  object,  clusterVar = NULL,  useClusters = NULL,  lambda = NULL,  nIteration = 30,  cellIdx = NULL,  scaleDatasets = NULL,  seed = 1,  verbose = getOption("ligerVerbose"),  cell.subset = cellIdx,  cluster.subset = useClusters,  max.iters = nIteration,  datasets.scale = scaleDatasets,  thresh = NULL)

Arguments

Value

Subsetobject with factorization matrices optimized, includingtheW matrix inliger object, andW andVmatrices in eachligerDataset object in thedatasetsslot.scaleData in theligerDataset objects ofdatasets specified byscaleDatasets will also be updated to reflectthe subset.

Examples

pbmc <- normalize(pbmc)pbmc <- selectGenes(pbmc)pbmc <- scaleNotCenter(pbmc)if (requireNamespace("RcppPlanc", quietly = TRUE)) {    # Only running a few iterations for fast examples    pbmc <- runINMF(pbmc, k = 20, nIteration = 2)    pbmc <- optimizeSubset(pbmc, cellIdx = sort(sample(ncol(pbmc), 200)),                           nIteration = 2)}

liger object of PBMC subsample data with Control and Stimulated datasets

Description

liger object of PBMC subsample data with Control and Stimulated datasets

Usage

pbmc

Format

liger object with two datasets named by "ctrl" and"stim".

Source

https://www.nature.com/articles/nbt.4042

References

Hyun Min Kang and et. al., Nature Biotechnology, 2018

liger object of PBMC subsample data with plotting information available

Description

This data was generated from data"pbmc" with defaultparameter integration pipeline: normalize, selectGenes, scaleNotCenter,runINMF, runCluster, runUMAP. To minimize the object size distributed withthe package, rawData and scaleData were removed. Genes are downsampled tothe top 50 variable genes, for smaller normData andW matrix.

Usage

pbmcPlot

Format

liger object with two datasets named by "ctrl" and"stim".

Source

https://www.nature.com/articles/nbt.4042

References

Hyun Min Kang and et. al., Nature Biotechnology, 2018

GSEA plot for specific gene set and factor using factorGSEA results

Description

GSEA plot for specific gene set and factor using factorGSEA results

Usage

## S3 method for class 'factorGSEA'plot(  x,  y,  geneSetName,  useFactor,  xTitleSize = 10,  xTextSize = 8,  yTitleSize = 10,  yTextSize = 8,  titleSize = 12,  captionTextSize = 8,  ESLineColor = "green",  ESLinewidth = 1,  hitsLineColor = "black",  hitsLinewidth = 0.5,  loadingBarColor = "grey",  ...)

Arguments

Value

ggplot object

Create barcode-rank plot for each dataset

Description

This function ranks the total count of each cell within each dataset and makeline plot. This function is simply for examining the input raw count dataand does not infer any recommended cutoff for removing non-cell barcodes.

Usage

plotBarcodeRank(object, ...)

Arguments

Value

A list object of ggplot for each dataset

Examples

plotBarcodeRank(pbmc)

Generate violin/box plot(s) using liger object

Description

This function allows for using available cell metadata, featureexpression or factor loading to generate violin plot, and grouping the datawith available categorical cell metadata. Available categorical cell metadatacan be used to form the color annotation. When it is different from thegrouping, it forms a nested grouping. Multiple y-axis variables are allowedfrom the same specification ofslot, and this returns a list of violinplot for each. Users can further split the plot(s) by grouping on cells (e.g.datasets).

Usage

plotCellViolin(  object,  y,  groupBy = NULL,  slot = c("cellMeta", "rawData", "normData", "scaleData", "H.norm", "H"),  yFunc = NULL,  cellIdx = NULL,  colorBy = NULL,  splitBy = NULL,  titles = NULL,  ...)

Arguments

Details

Available option forslot include:"cellMeta","rawData","normData","scaleData","H.norm"and"H". When"rawData","normData" or"scaleData",y has to be a character vector of feature names.When"H.norm" or"H",colorBy can be any valid index toselect one factor of interests. Note that character index follows"Factor_[k]" format, with replacing[k] with an integer.

When"cellMeta",y has to be an available column name inthe table. Note that, fory as well asgroupBy,colorByandsplitBy since a matrix object is feasible incellMetatable, using a column (e.g. named as"column1" in a certain matrix(e.g. named as"matrixVar") should follow the syntax of"matrixVar.column1". When the matrix does not have a "colname"attribute, the subscription goes with"matrixVar.V1","matrixVar.V2" and etc. These are based on the nature ofas.data.frame method on aDataFrame object.

groupBy is basically send toggplot2::aes(x), whilecolorBy is for the "colour" aesthetics. SpecifyingcolorBywithoutgroupBy visually creates grouping but there will not bevarying values on the x-axis, soboxWidth will be forced to the samevalue asviolinWidth under this situation.

Value

A ggplot object when a single plot is intended. A list of ggplotobjects, when multipley variables and/orsplitBy are set. Whenplotly = TRUE, all ggplot objects become plotly (htmlwidget) objects.

Examples

plotCellViolin(pbmcPlot, y = "nUMI", groupBy = "dataset", slot = "cellMeta")plotCellViolin(pbmcPlot, y = "nUMI", groupBy = "leiden_cluster",               slot = "cellMeta", splitBy = "dataset",               colorBy = "leiden_cluster",               box = TRUE, dot = TRUE,               ylab = "Total counts per cell")plotCellViolin(pbmcPlot, y = "S100A8", slot = "normData",               yFunc = function(x) log2(10000*x + 1),               groupBy = "dataset", colorBy = "leiden_cluster",               box = TRUE, ylab = "S100A8 Expression")

Make dot plot of factor loading in cell groups

Description

This function produces dot plots. Each column represent a groupof cells specified bygroupBy, each row is a factor specified byuseDims. The color of dots reflects mean of factor loading ofspecified factors in each cell group and sizes reflects the percentage ofcells that have loadings of a factor in a group. We utilizeComplexHeatmapfor simplified management of adding annotation and slicing subplots. This wasinspired by the implementation inscCustomize.

Usage

plotClusterFactorDot(  object,  groupBy = NULL,  useDims = NULL,  useRaw = FALSE,  splitBy = NULL,  factorScaleFunc = NULL,  cellIdx = NULL,  legendColorTitle = "Mean Factor\nLoading",  legendSizeTitle = "Percent\nLoaded",  viridisOption = "viridis",  verbose = FALSE,  ...)

Arguments

Details

For..., please notice that argumentscolorMat,sizeMat,featureAnnDF,cellSplitVar,cellLabelsandviridisOption from.complexHeatmapDotPlot arealready occupied by this function internally. A lot of arguments fromHeatmap have also been occupied:matrix,name, heatmap_legend_param, rect_gp, col, layer_fun, km, border, border_gp,column_gap, row_gap, cluster_row_slices, cluster_rows, row_title_gp,row_names_gp, row_split, row_labels, cluster_column_slices, cluster_columns,column_split, column_title_gp, column_title, column_labels, column_names_gp,top_annotation.

Value

HeatmapList object.

Examples

plotClusterFactorDot(pbmcPlot)

Make dot plot of gene expression in cell groups

Description

This function produces dot plots. Each column represent a groupof cells specified bygroupBy, each row is a gene specified byfeatures. The color of dots reflects mean of normalized expression ofspecified genes in each cell group and sizes reflects the percentage of cellsexpressing each gene in a group. We utilizeComplexHeatmapfor simplified management of adding annotation and slicing subplots. This wasinspired by the implementation inscCustomize.

Usage

plotClusterGeneDot(  object,  features,  groupBy = NULL,  splitBy = NULL,  featureScaleFunc = function(x) log2(10000 * x + 1),  cellIdx = NULL,  legendColorTitle = "Mean\nExpression",  legendSizeTitle = "Percent\nExpressed",  viridisOption = "magma",  verbose = FALSE,  ...)

Arguments

Details

For..., please notice that argumentscolorMat,sizeMat,featureAnnDF,cellSplitVar,cellLabelsandviridisOption from.complexHeatmapDotPlot arealready occupied by this function internally. A lot of arguments fromHeatmap have also been occupied:matrix,name, heatmap_legend_param, rect_gp, col, layer_fun, km, border, border_gp,column_gap, row_gap, cluster_row_slices, cluster_rows, row_title_gp,row_names_gp, row_split, row_labels, cluster_column_slices, cluster_columns,column_split, column_title_gp, column_title, column_labels, column_names_gp,top_annotation.

Value

HeatmapList object.

Examples

# Use character vector of genesfeatures <- varFeatures(pbmcPlot)[1:10]plotClusterGeneDot(pbmcPlot, features = features)# Use data.frame with grouping information, with more tweak on plotfeatures <- data.frame(features, rep(letters[1:5], 2))plotClusterGeneDot(pbmcPlot, features = features,                   clusterFeature = TRUE, clusterCell = TRUE, maxDotSize = 6)

Create violin plot for multiple genes grouped by clusters

Description

Make violin plots for each given gene grouped by cluster variable and stackalong y axis.

Usage

plotClusterGeneViolin(  object,  gene,  groupBy = NULL,  colorBy = NULL,  box = FALSE,  boxAlpha = 0.1,  yFunc = function(x) log1p(x * 10000),  showLegend = !is.null(colorBy),  xlabAngle = 40,  ...)

Arguments

Details

Ifxlab need to be set, setxlabAngle at the same time. This isdue to that the argument parsing mechanism will partially match it to mainfunction arguments before matching the... arguments.

Value

A ggplot object.

Examples

plotClusterGeneViolin(pbmcPlot, varFeatures(pbmcPlot)[1:10])

Create density plot basing on specified coordinates

Description

This function shows the cell density presented in a 2Ddimensionality reduction coordinates. Density is shown with coloring andcontour lines. A scatter plot of the dimensionality reduction is added aswell. The density plot can be splitted by categorical variables (e.g."dataset"), while the scatter plot will always be shown for all cellsin subplots as a reference of the global structure.

Usage

plotDensityDimRed(  object,  useDimRed = NULL,  splitBy = NULL,  combinePlot = TRUE,  minDensity = 8,  contour = TRUE,  contourLineWidth = 0.3,  contourBins = 5,  dot = TRUE,  dotColor = "grey",  dotSize = 0.6,  dotAlpha = 0.3,  dotRaster = NULL,  title = NULL,  legendFillTitle = "Density",  colorPalette = "magma",  colorDirection = -1,  ...)

Arguments

Value

A ggplot object when only one plot is generated, A ggplot objectcombined withplot_grid when multiple plots andcombinePlot = TRUE. A list of ggplot when multiple plots andcombinePlot = FALSE.

Examples

# Example dataset has small number of cells, thus cutoff adjusted.plotDensityDimRed(pbmcPlot, minDensity = 1)

Generate scatter plot(s) using liger object

Description

This function allows for using available cell metadata to buildthe x-/y-axis. Available per-cell data can be used to form the color/shapeannotation, including cell metadata, raw or processed gene expression, andunnormalized or aligned factor loading. Multiple coloring variable is allowedfrom the same specification ofslot, and this returns a list of plotswith different coloring values. Users can further split the plot(s) bygrouping on cells (e.g. datasets).

Usage

plotDimRed(  object,  colorBy = NULL,  useDimRed = NULL,  slot = c("cellMeta", "rawData", "normData", "scaleData", "H.norm", "H", "normPeak",    "rawPeak"),  colorByFunc = NULL,  cellIdx = NULL,  splitBy = NULL,  shapeBy = NULL,  titles = NULL,  ...)plotClusterDimRed(object, useCluster = NULL, useDimRed = NULL, ...)plotDatasetDimRed(object, useDimRed = NULL, ...)plotByDatasetAndCluster(  object,  useDimRed = NULL,  useCluster = NULL,  combinePlot = TRUE,  ...)plotGeneDimRed(  object,  features,  useDimRed = NULL,  log = TRUE,  scaleFactor = 10000,  zeroAsNA = TRUE,  colorPalette = "C",  ...)plotPeakDimRed(  object,  features,  useDimRed = NULL,  log = TRUE,  scaleFactor = 10000,  zeroAsNA = TRUE,  colorPalette = "C",  ...)plotFactorDimRed(  object,  factors,  useDimRed = NULL,  trimHigh = 0.03,  zeroAsNA = TRUE,  colorPalette = "D",  ...)

Arguments

Details

Available option forslot include:"cellMeta","rawData","normData","scaleData","H.norm"and"H". When"rawData","normData" or"scaleData",colorBy has to be a character vector of featurenames. When"H.norm" or"H",colorBy can be any validindex to select one factor of interests. Note that character index follows"Factor_[k]" format, with replacing[k] with an integer.

When"cellMeta",colorBy has to be an available column name inthe table. Note that, forcolorBy as well asx,y,shapeBy andsplitBy, since a matrix object is feasible incellMeta table, using a column (e.g. named as"column1" in acertain matrix (e.g. named as"matrixVar") should follow the syntax of"matrixVar.column1". When the matrix does not have a "colname"attribute, the subscription goes with"matrixVar.V1","matrixVar.V2" and etc. Use"UMAP.1","UMAP.2","TSNE.1" or"TSNE.2" for the 2D embeddings generated withrliger package. These are based on the nature ofas.data.frame methodon aDataFrame object.

Value

A ggplot object when a single plot is intended. A list of ggplotobjects, when multiplecolorBy variables and/orsplitBy areset. Whenplotly = TRUE, all ggplot objects become plotly (htmlwidget)objects.

ggplot object when only one feature (e.g. cluster variable, gene,factor) is set. List object when multiple of those are specified.

Examples

plotDimRed(pbmcPlot, colorBy = "dataset", slot = "cellMeta",           labelText = FALSE)plotDimRed(pbmcPlot, colorBy = "S100A8", slot = "normData",           dotOrder = "ascending", dotSize = 2)plotDimRed(pbmcPlot, colorBy = 2, slot = "H.norm",           dotOrder = "ascending", dotSize = 2, colorPalette = "viridis")plotClusterDimRed(pbmcPlot)plotDatasetDimRed(pbmcPlot)plotByDatasetAndCluster(pbmcPlot)plotGeneDimRed(pbmcPlot, varFeatures(pbmcPlot)[1])plotFactorDimRed(pbmcPlot, 2)

Create volcano plot with EnhancedVolcano

Description

Create volcano plot with EnhancedVolcano

Usage

plotEnhancedVolcano(result, group, ...)

Arguments

Value

ggplot

Examples

if (requireNamespace("EnhancedVolcano", quietly = TRUE)) {    defaultCluster(pbmc) <- pbmcPlot$leiden_cluster    # Test the DEG between "stim" and "ctrl", within each cluster    result <- runPairwiseDEG(        pbmc,        groupTest = "stim",        groupCtrl = "ctrl",        variable1 = "dataset",        splitBy = "defaultCluster"    )    plotEnhancedVolcano(result, "0.stim")}

Visualize GO enrichment test result in dot plot

Description

Visualize GO enrichment test result in dot plot

Usage

plotGODot(  result,  group = NULL,  query = NULL,  pvalThresh = 0.05,  n = 20,  minDotSize = 3,  maxDotSize = 7,  termIDMatch = "^GO",  colorPalette = "E",  colorDirection = -1,  ...)

Arguments

Value

A ggplot object.

Examples

if (requireNamespace("gprofiler2", quietly = TRUE)) {   go <- runGOEnrich(deg.pw)   plotGODot(go)}

Plot Heatmap of Gene Expression or Factor Loading

Description

Plot Heatmap of Gene Expression or Factor Loading

Usage

plotGeneHeatmap(  object,  features,  cellIdx = NULL,  slot = c("normData", "rawData", "scaleData", "scaleUnsharedData"),  useCellMeta = NULL,  cellAnnotation = NULL,  featureAnnotation = NULL,  cellSplitBy = NULL,  featureSplitBy = NULL,  viridisOption = "C",  ...)plotFactorHeatmap(  object,  factors = NULL,  cellIdx = NULL,  slot = c("H.norm", "H"),  useCellMeta = NULL,  cellAnnotation = NULL,  factorAnnotation = NULL,  cellSplitBy = NULL,  factorSplitBy = NULL,  trim = c(0, 0.03),  viridisOption = "D",  ...)

Arguments

Value

HeatmapList-class object

Examples

plotGeneHeatmap(pbmcPlot, varFeatures(pbmcPlot))plotGeneHeatmap(pbmcPlot, varFeatures(pbmcPlot),                useCellMeta = c("leiden_cluster", "dataset"),                cellSplitBy = "leiden_cluster")plotFactorHeatmap(pbmcPlot)plotFactorHeatmap(pbmcPlot, cellIdx = pbmcPlot$leiden_cluster %in% 1:3,                  useCellMeta = c("leiden_cluster", "dataset"),                  cellSplitBy = "leiden_cluster")

Visualize factor expression and gene loading

Description

Visualize factor expression and gene loading

Usage

plotGeneLoadings(  object,  markerTable,  useFactor,  useDimRed = NULL,  nLabel = 15,  nPlot = 30,  ...)plotGeneLoadingRank(  object,  markerTable,  useFactor,  nLabel = 15,  nPlot = 30,  ...)

Arguments

Examples

result <- getFactorMarkers(pbmcPlot, "ctrl", "stim")plotGeneLoadings(pbmcPlot, result, useFactor = 2)

Visualize gene expression or cell metadata with violin plot

Description

Visualize gene expression or cell metadata with violin plot

Usage

plotGeneViolin(object, gene, byDataset = TRUE, groupBy = NULL, ...)plotTotalCountViolin(object, groupBy = "dataset", ...)plotGeneDetectedViolin(object, groupBy = "dataset", ...)

Arguments

Value

ggplot if using a single gene and not splitting by dataset.Otherwise, list of ggplot.

Examples

plotGeneViolin(pbmcPlot, varFeatures(pbmcPlot)[1],               groupBy = "leiden_cluster")plotTotalCountViolin(pbmc)plotGeneDetectedViolin(pbmc, dot = TRUE, box = TRUE, colorBy = "dataset")

Comprehensive group splited cluster plot on dimension reduction withproportion

Description

This function produces combined plot on group level (e.g. dataset, othermetadata variable like biological conditions). Scatter plot of dimensionreduction with cluster labeled is generated per group. Furthermore, a stackedbarplot of cluster proportion within each group is also combined with thesubplot of each group.

Usage

plotGroupClusterDimRed(  object,  useGroup = "dataset",  useCluster = NULL,  useDimRed = NULL,  combinePlot = TRUE,  droplevels = TRUE,  relHeightMainLegend = c(5, 1),  relHeightDRBar = c(10, 1),  mainNRow = NULL,  mainNCol = NULL,  legendNRow = 1,  ...)

Arguments

Value

ggplot object when only one feature (e.g. cluster variable, gene,factor) is set. List object when multiple of those are specified.

Examples

plotGroupClusterDimRed(pbmcPlot)

Create heatmap for showing top marker expression in conditions

Description

Create heatmap for showing top marker expression in conditions

Usage

plotMarkerHeatmap(  object,  result,  topN = 5,  lfcThresh = 1,  padjThresh = 0.05,  pctInThresh = 50,  pctOutThresh = 50,  dedupBy = c("logFC", "padj"),  groupBy = NULL,  groupSize = 50,  column_title = NULL,  ...)

Arguments

Value

AHeatmapList-class object.

Examples

defaultCluster(pbmc) <- pbmcPlot$leiden_clusterpbmc <- normalize(pbmc)plotMarkerHeatmap(pbmc, deg.marker)

Create heatmap for pairwise DEG analysis result

Description

Create heatmap for pairwise DEG analysis result

Usage

plotPairwiseDEGHeatmap(  object,  result,  group = NULL,  topN = 20,  absLFCThresh = 1,  padjThresh = 0.05,  pctInThresh = 50,  pctOutThresh = 50,  downsampleSize = 200,  useCellMeta = NULL,  column_title = NULL,  seed = 1,  ...)

Arguments

Value

AHeatmapList-class object.

Examples

defaultCluster(pbmc) <- pbmcPlot$leiden_clusterpbmc$condition_cluster <- interaction(pbmc$dataset, pbmc$defaultCluster)deg <- runPairwiseDEG(pbmc, 'stim.0', 'stim.1', 'condition_cluster')pbmc <- normalize(pbmc)plotPairwiseDEGHeatmap(pbmc, deg, 'stim.0')

Visualize proportion across two categorical variables

Description

plotProportionBar creates bar plots comparing thecross-category proportion.plotProportionDot creates dot plots.plotClusterProportions has variable pre-specified and calls the dotplot.plotProportion produces a combination of both bar plots and dotplot.

Having package "ggrepel" installed can help adding tidier percentageannotation on the pie chart. Runoptions(ggrepel.max.overlaps = n)before plotting to set allowed label overlaps.

Usage

plotProportion(  object,  class1 = NULL,  class2 = "dataset",  method = c("stack", "group", "pie"),  ...)plotProportionDot(  object,  class1 = NULL,  class2 = "dataset",  showLegend = FALSE,  panelBorder = TRUE,  ...)plotProportionBar(  object,  class1 = NULL,  class2 = "dataset",  method = c("stack", "group"),  inclRev = FALSE,  panelBorder = TRUE,  combinePlot = TRUE,  ...)plotClusterProportions(object, useCluster = NULL, return.plot = FALSE, ...)plotProportionPie(  object,  class1 = NULL,  class2 = "dataset",  labelSize = 4,  labelColor = "black",  circleColors = NULL,  ...)

Arguments

Value

ggplot or list of ggplot

Examples

plotProportion(pbmcPlot)plotProportionBar(pbmcPlot, method = "group")plotProportionPie(pbmcPlot)

Box plot of cluster proportion in each dataset, grouped by condition

Description

This function calculate the proportion of each category (e.g. cluster, celltype) within each dataset, and then make box plot grouped by condition. Theproportion of all categories within one dataset sums up to 1. The conditionvariable must be a variable of dataset, i.e. each dataset must belong to onlyone condition.

Usage

plotProportionBox(  object,  useCluster = NULL,  conditionBy = NULL,  sampleBy = "dataset",  splitByCluster = FALSE,  dot = FALSE,  dotSize = getOption("ligerDotSize", 1),  dotJitter = FALSE,  ...)

Arguments

Value

A ggplot object or a list of ggplot objects ifsplitByCluster = TRUE.

Examples

# "boxes" are expected to appear as horizontal lines, because there's no# "condition" variable that groups the datasets in the example object, and# thus only one value exists for each "box".plotProportionBox(pbmcPlot, conditionBy = "dataset")

Make Riverplot/Sankey diagram that shows label mapping across datasets

Description

Creates a riverplot/Sankey diagram to show how independent clusterassignments from two datasets map onto a joint clustering. Prior knowledge ofcell annotation for the given datasets is required to make sense from thevisualization. Dataset original annotation can be added with the syntax shownin example code in this manual. The joint clustering could be generated withrunCluster or set by any other metadata annotation.

Dataset original annotation can be inserted before running this functionusingcellMeta<- method. Please see example below.

This function depends on CRAN available package "sankey" and it has to beinstalled in order to make this function work.

Usage

plotSankey(  object,  cluster1,  cluster2,  clusterConsensus = NULL,  minFrac = 0.01,  minCell = 10,  titles = NULL,  prefixes = NULL,  labelCex = 1,  titleCex = 1.1,  colorValues = scPalette,  mar = c(2, 2, 4, 2))

Arguments

Value

No returned value. The sankey diagram will be displayed instead.

Note

This function works as a replacement of the functionmakeRiverplotin rliger <1.99. We decide to make a new function because the dependencyadopted by the older version is archived on CRAN and will be no longeravailable.

Examples

# Make fake dataset specific labels from joint clustering resultcellMeta(pbmcPlot, "ctrl_cluster", "ctrl") <-    cellMeta(pbmcPlot, "leiden_cluster", "ctrl")cellMeta(pbmcPlot, "stim_cluster", "stim") <-    cellMeta(pbmcPlot, "leiden_cluster", "stim")if (requireNamespace("sankey", quietly = TRUE)) {    plotSankey(pbmcPlot, "ctrl_cluster", "stim_cluster",               titles = c("control", "LIGER", "stim"),               prefixes = c("c", NA, "s"))}

Visualize a spatial dataset

Description

Simple visualization of spatial coordinates. See example code for how to haveinformation preset in the object. Arguments to the liger object method arepassed down to ligerDataset method.

Usage

plotSpatial2D(object, ...)## S3 method for class 'liger'plotSpatial2D(object, dataset, useCluster = NULL, legendColorTitle = NULL, ...)## S3 method for class 'ligerSpatialDataset'plotSpatial2D(  object,  useCluster = NULL,  legendColorTitle = NULL,  useDims = c(1, 2),  xlab = NULL,  ylab = NULL,  labelText = FALSE,  panelBorder = TRUE,  ...)

Arguments