An object

Arguments passed to other methods and to specific DE methods

Slot to pull data from; note that iftest.use is "negbinom", "poisson", or "DESeq2",slot will be set to "counts"

Count matrix if using scale.data for DE tests. This is used forcomputing pct.1 and pct.2 and for filtering features based on fractionexpressing

Vector of cell names belonging to group 1

Vector of cell names belonging to group 2

Genes to test. Default is to use all genes

Limit testing to genes which show, on average, at leastX-fold difference (log-scale) between the two groups of cells. Default is 0.25Increasing logfc.threshold speeds up the function, but can miss weaker signals.

Denotes which test to use. Available options are:

• "wilcox" : Identifies differentially expressed genes between two groups of cells using a Wilcoxon Rank Sum test (default)

• "bimod" : Likelihood-ratio test for single cell gene expression, (McDavid et al., Bioinformatics, 2013)

• "roc" : Identifies 'markers' of gene expression using ROC analysis. For each gene, evaluates (using AUC) a classifier built on that gene alone, to classify between two groups of cells. An AUC value of 1 means that expression values for this gene alone can perfectly classify the two groupings (i.e. Each of the cells in cells.1 exhibit a higher level than each of the cells in cells.2). An AUC value of 0 also means there is perfect classification, but in the other direction. A value of 0.5 implies that the gene has no predictive power to classify the two groups. Returns a 'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially expressed genes.

• "t" : Identify differentially expressed genes between two groups of cells using the Student's t-test.

• "negbinom" : Identifies differentially expressed genes between two groups of cells using a negative binomial generalized linear model. Use only for UMI-based datasets

• "poisson" : Identifies differentially expressed genes between two groups of cells using a poisson generalized linear model. Use only for UMI-based datasets

• "LR" : Uses a logistic regression framework to determine differentially expressed genes. Constructs a logistic regression model predicting group membership based on each feature individually and compares this to a null model with a likelihood ratio test.

• "MAST" : Identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST package to run the DE testing.

• "DESeq2" : Identifies differentially expressed genes between two groups of cells based on a model using DESeq2 which uses a negative binomial distribution (Love et al, Genome Biology, 2014).This test does not support pre-filtering of genes based on average difference (or percent detection rate) between cell groups. However, genes may be pre-filtered based on their minimum detection rate (min.pct) across both cell groups. To use this method, please install DESeq2, using the instructions at https://bioconductor.org/packages/release/bioc/html/DESeq2.html

only test genes that are detected in a minimum fraction ofmin.pct cells in either of the two populations. Meant to speed up the functionby not testing genes that are very infrequently expressed. Default is 0.1

only test genes that show a minimum difference in thefraction of detection between the two groups. Set to -Inf by default

Print a progress bar once expression testing begins

Only return positive markers (FALSE by default)

Down sample each identity class to a max number.Default is no downsampling. Not activated by default (set to Inf)

Random seed for downsampling

Variables to test, used only whentest.use is one of'LR', 'negbinom', 'poisson', or 'MAST'

Minimum number of cells expressing the feature in at least oneof the two groups, currently only used for poisson and negative binomial tests

Minimum number of cells in one of the groups

Pseudocount to add to averaged expression values whencalculating logFC. 1 by default.

data.frame from FoldChange

Function to use for fold change or average difference calculation.If NULL, the appropriate function will be chose according to the slot used

Name of the fold change, average difference, or custom function columnin the output data.frame. If NULL, the fold change column will be namedaccording to the logarithm base (eg, "avg_log2FC"), or if using the scale.dataslot "avg_diff".

The base with respect to which logarithms are computed.

Identity class to define markers for; pass an object of classphylo or 'clustertree' to find markers for a node in a cluster tree;passing 'clustertree' requiresBuildClusterTree to have been run

A second identity class for comparison; ifNULL,use all other cells for comparison; if an object of classphylo or'clustertree' is passed toident.1, must pass a node to find markers for

Regroup cells into a different identity class prior to performing differential expression (see example)

Subset a particular identity class prior to regrouping. Only relevant if group.by is set (see example)

Assay to use in differential expression testing

Reduction to use in differential expression testing - will test for DE on cell embeddings