| Type: | Package |
| Title: | Noise Quantification in High Throughput Sequencing Output |
| Version: | 1.0.0 |
| Maintainer: | Ilias Moutsopoulos <im383@cam.ac.uk> |
| Description: | Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are used, one based on the count matrix, and one using the alignment BAM files directly. Contains several options for every step of the process, as well as tools to quality check and assess the stability of output. |
| Depends: | R (≥ 3.1.2) |
| Imports: | utils, grDevices, tibble, dplyr, magrittr, ggplot2,preprocessCore, IRanges, GenomicRanges, Rsamtools, philentropy,doParallel, foreach |
| Suggests: | testthat, roxygen2, knitr, rmarkdown |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| URL: | https://github.com/Core-Bioinformatics/noisyR |
| BugReports: | https://github.com/Core-Bioinformatics/noisyR/issues |
| RoxygenNote: | 7.1.1 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2021-04-16 08:59:10 UTC; Ilias Moutsopoulos |
| Author: | Ilias Moutsopoulos [aut, cre], Irina Mohorianu [aut, ctb], Hajk-Georg Drost [ctb], Elze Lauzikaite [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2021-04-16 10:20:02 UTC |
Calculate the expression profile of a gene
Description
This function calculates the expression profile of an exon in a selectionof BAM files. The expression profile is defined as the number of reads overlappingwith each position of the exon's transcript.
Usage
calculate_expression_profile( gene, bams, unique.only = TRUE, mapq.unique = c(50, 255), slack = 200)Arguments
gene | The exon for which the expression profile is calculated; this should bea row from the tibble generated by |
bams | a vector of paths to the BAM files from which the profile is extracted |
unique.only | whether only uniquely mapped reads should contribute to the profile;default is TRUE |
mapq.unique | The values of the mapping quality field in the BAM file that correspondsto uniquely mapped reads; by default, values of 50 and 255 are used as these correspond tothe most popular aligners, but an adjustment might be needed |
slack | slack needs to be >=readLength, adjust for efficiency; the default is 200,as it is higher than most modern sequencing experiments |
Value
The function outputs a list: the first element is a matrix of expression profiles.Rows correspond to positions in the exon transcript and each column corresponds to an inputBAM file. Each read is counted for all the positions with which it overlaps (so a read oflength 100 that completely overlaps with the exon would be counted for all 100 positions).The second list element is a vector of raw expression of the gene in the different BAM files
Examples
bams <- rep(system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE), 2)genes <- data.frame("id" = 1:2, "gene_id" = c("gene1", "gene2"), "seqid" = c("seq1", "seq2"), "start" = 1, "end" = 100)profile <- calculate_expression_profile( gene = genes[1,], bams = bams, mapq.unique = 99)ggplot2::ggplot(tibble::tibble(y = profile$profile[,1], x = seq_along(y))) +ggplot2::geom_bar(ggplot2::aes(x, y), stat = "identity") +ggplot2::theme_minimal()Calcualate the expression levels and expression levels similarity matrices using the count matrix
Description
This function generates an average similarity (correlation/inverse distance) coefficientfor every sliding window, for each sample in the expression matrix.That is done by comparing the distribution of genes in each window across samples.
Usage
calculate_expression_similarity_counts( expression.matrix, similarity.measure = "correlation_pearson", n.elements.per.window = NULL, n.step = NULL, n.step.fraction = 0.05, ...)Arguments
expression.matrix | the expression matrix, can be normalized or not |
similarity.measure | one of the correlation or distance metrics to be used,defaults to pearson correlation; list of all methods in |
n.elements.per.window | number of elements to have in a window,default 10% of the number of rows |
n.step | step size to slide across, default 1% of n.elements.per.window |
n.step.fraction | an alternative way to specify the step size, as a fraction ofthe window length; default is 5% |
... | arguments passed on to other methods |
Value
A list with three elements: the first element is the expression matrix,as supplied; the other two are the expression levels matrix andexpression levels similarity matrix;they have the same # of columns as the expression matrix,and n.elements.per.window * n.step rows.
See Also
calculate_expression_similarity_transcript
Examples
calculate_expression_similarity_counts( expression.matrix = matrix(1:100, ncol = 5), similarity.measure = "correlation_pearson", n.elements.per.window = 3)Calcualte the distance matrices using the BAM files
Description
This function generates an average correlation/distance coefficientfor every exon present in the BAM files. This is done by calculatingthe point-to-point correlation/distance of the distribution of readsacross the transcript of each exon and comparing it across samples.The reason why exons are used instead of full length genes is that longintronic regions artificially increase the correlation since there isconsistently no expression there, across samples. The user has theoption to use genes instead, by runningcast_gtf_to_genes separately,with non default parameters.
Usage
calculate_expression_similarity_transcript( bams = NULL, path.bams = ".", genes = NULL, path.gtf = list.files(".", pattern = "\\.g[tf]f$"), expression.matrix = NULL, subsample.genes = FALSE, make.index = FALSE, unique.only = TRUE, mapq.unique = 255, slack = 200, similarity.measure = "correlation_pearson", save.image.every.1000 = FALSE, ncores = 1, ...)Arguments
bams,path.bams | either a path to the directory where the BAM files areor a vector of paths to each individual file; if a path is specified,it extracts all files that end in .bam; looks in the working directory by default |
genes | a tibble of the exons extracted from the gtf file;this is meant for speed if the output of |
path.gtf | the path to the gtf/gff annotation file (only used if genes is notprovided); if unspecified, looks for one in the working directory |
expression.matrix | expression matrix; not necessary but is used to filter thegtf to fewer entries and for subsampling if subsample.genes=TRUE;if not provided, raw read counts per exon are extracted from the BAM files |
subsample.genes | logical, whether to subsample low abundance genes to decreasecomputational time; the first minimum of the distribution of abundances is calculated,and genes lower than it are subsampled to match the number of genes higher than it;the expression matrix needs to be provided for this calculation;a plot is generated to show that minimum |
make.index | whether a BAM index should be generated; if this is FALSE (the default)and no index exists, the function will exit with an error; the index needs to havethe same name as each BAM file, but ending with .bam.bai |
unique.only | whether only uniquely mapped reads should contribute to the expressionof a gene/exon; default is TRUE |
mapq.unique | The values of the mapping quality field in the BAM file that correspondsto uniquely mapped reads; by default, values of 255 are used as these correspond tothe most popular aligners, but an adjustment might be needed;the mapq scores should be as follows: 255 for STAR, 60 for hisat2,255 for bowtie in -k mode, 40 for bowtie2 default, 50 for tophat |
slack | slack needs to be >=readLength, adjust for efficiency; the default is 200,as it is higher than most modern sequencing experiments |
similarity.measure | one of the similarity metrics to be used, defaults to pearson correlation;currently, only correlation is supported |
save.image.every.1000 | whether to save a workspace image after every 1000 exonsare processed; default is FALSE |
ncores | Number of cores for parallel computation; defaults to sequential computation,but parallelisation is highly encouraged; it is set to detectCores() if higher |
... | arguments passed on to other methods |
Value
A list with three elements: the first element is the expression matrix,as supplied or calculated; the other two are the expression levels matrix andexpression levels similarity matrix;they have the same # of columns as the expression matrix,and as many rows as exons processed.
See Also
calculate_expression_similarity_counts
Examples
bams <- rep(system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE), 2)genes <- data.frame("id" = 1:2, "gene_id" = c("gene1", "gene2"), "seqid" = c("seq1", "seq2"), "start" = 1, "end" = 1600)expression.summary <- calculate_expression_similarity_transcript( bams = bams, genes = genes, mapq.unique = 99)Function to find the first local minimum of the density of a vector
Description
This function is used to estimate the first local minimum of thedensity of a vector.It is meant to be used on the distribution of expression of genes in a sample;since the distribution tails off, finding the global minimum is not appropriate.The plot option can be used to visualise the process.
Usage
calculate_first_minimum_density( mat, log.transform = TRUE, adjust = 2, makeplots = FALSE)Arguments
mat | matrix whose columns will be used; usually an expression matrix; itcan also be a vector |
log.transform | whether to log-transform the data before the density estimation;default is TRUE |
adjust | adjust factor for the smoothing, passed to density(); default is 2 |
makeplots | a logical value of whether a plot with a vertical line on the minimum foundshould be printed for each column of the matrix. |
Value
The function outputs a single value corresponding to the median of the minima calculatedfor each column of the matrix. floor() is taken as a conservative estimate
Examples
calculate_first_minimum_density( matrix(c(rep(0,100),rep(3,30),rep(10,50),12,13,15,20),ncol=1), log.transform=FALSE, makeplots=TRUE)Function to calculate the noise threshold for a given expression matrix and parameters
Description
This function is used to calculate the noise threshold for a given expression matrix.It uses as input an expression profile, or just an expression matrix for a simple calculation based on density.A variety of methods are available to obtain a noise threshold using an input similarity threshold.
Usage
calculate_noise_threshold( expression, similarity.threshold = 0.25, method.chosen = "Boxplot-IQR", binsize = 0.1, minimum.observations.per.bin = NULL, ...)Arguments
expression | either an expression summary (as calculated by |
similarity.threshold | similarity (correlation or inverse distance) threshold to be usedto find corresponding noise threshold; the default, 0.25 is usually suitable for thePearson correlation (the default similarity measure) |
method.chosen | method to use to obtain a vector of noise thresholds,must be one of |
binsize | size of each bin in the boxplot methods; defaults to 0.1 (on a log-scale) |
minimum.observations.per.bin | minumum number of observations allowed in each bin of the boxplot;if a bin has fewer observations, it is merged with the one to its left; default is calculated as:ceiling(number of observations / number of bins / 10) |
... | arguments passed on to other methods |
Value
The output is a vector of noise thresholds, the same length as the number of columns inthe expression matrix, or a single value in the case of density based methods.
See Also
calculate_noise_threshold_method_statistics
Examples
expression.summary <- calculate_expression_similarity_counts( expression.matrix = matrix(1:100, ncol=5), method = "correlation_pearson", n.elements.per.window = 3)calculate_noise_threshold(expression.summary)Function to tabulate statistics for different methods of calculating the noise threshold
Description
This function is used to tabulate and compare different combinations of similaritythreshold and method to calculate the noise threshold for a given expression matrix.
Usage
calculate_noise_threshold_method_statistics( expression, similarity.threshold.sequence = 0.25, method.chosen.sequence = noisyr::get_methods_calculate_noise_threshold(), dump.stats = NULL, ...)Arguments
expression | either an expression summary (as calculated by |
similarity.threshold.sequence | similarity (correlation or inverse distance) threshold(s) to be usedto find corresponding noise threshold; can be a single value or a numeric vector;the default, 0.25 is usually suitable for the Pearson correlation (the default similarity measure) |
method.chosen.sequence | methods to use to calculate the noise thresholds,must be a subset of |
dump.stats | name of csv to export different thresholds calculated (optional) |
... | other arguments (for the boxplot methods) passed to |
Value
A tibble containing information on noise thresholds calculated using the inputsimilarity thresholds and methods (optionally written in a csv file).The columns list the chosen method and similarity threshold, the minimum, mean,coefficient of variation, and maximum of the noise thresholds, and all the noise thresholdsconcatenated as a string.
See Also
Examples
expression.summary <- calculate_expression_similarity_counts( expression.matrix = matrix(1:100, ncol=5), method = "correlation_pearson", n.elements.per.window = 3)calculate_noise_threshold_method_statistics(expression.summary)Function to extract exon names and positions from a gtf file
Description
This function is used to extract all exons and their positionsin the genome from an input gtf file.
Usage
cast_gtf_to_genes(filename, feature = "exon", att_of_interest = "gene_id", ...)Arguments
filename | path to the gtf file |
feature | the feature type name to filter the feature (3rd) column of the gtf/gff file;default is exon |
att_of_interest | the attribute to extract from the last column of the gtf/gff file;default in gene_id |
... | arguments passed on to other methods |
Value
A tibble of the ids, gene names, chromosomes, start and end positionsof each exon found in the gtf file.
Examples
fl <- system.file("extdata", "example.gtf.gz", package="Rsamtools", mustWork=TRUE)cast_gtf_to_genes(fl)Cast a matrix of any type to numeric
Description
Transforms values in the expression matrix to numeric,to make it compatible with the rest of the functions.
Usage
cast_matrix_to_numeric(expression.matrix)Arguments
expression.matrix | The expression matrix (usually read from a file) |
Value
The expression matrix transformed to numeric, preserving row and column names.Any values that are not coercible to numeric are replaced by 0.
Examples
cast_matrix_to_numeric(matrix( c(1, "2", 3.0, 4), ncol=2, dimnames=list(paste0("X", 1:2), paste0("Y", 1:2))))Function to filter the gene table for the transcript approach
Description
This function is used to filter the gene table (usually created withcast_gtf_to_genes), only keeping genes above the noise thresholds.It uses as input the gene table (usually containing individual exons),an expression matrix for each of these and a vector of abundance thresholds.This function is used internally byremove_noise_from_bams to determinewhich genes to retain.
Usage
filter_genes_transcript( genes, expression.matrix, noise.thresholds, filter.by = c("gene", "exon"), ...)Arguments
genes | a tibble of the exons extracted from the gtf file;(usually the the output of |
expression.matrix | the expression matrix, usuallycalculated by |
noise.thresholds | a vector of expression thresholds by sample |
filter.by | Either "gene" (default) or "exon"; if filter.by="gene", a gene(as determined by its ENSEMBL id) is removedif and only if all of its exons are below the corresponding noise thresholds;if filter.by="exon", then each exon is individually removedif it is below the corresponding noise thresholds. |
... | arguments passed on to other methods |
Value
Returns a filtered tibble of exons, with the noise removed.
Examples
bams <- rep(system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE), 2)genes <- data.frame("id" = 1:2, "gene_id" = c("gene1", "gene2"), "seqid" = c("seq1", "seq2"), "start" = 1, "end" = 1600)noise.thresholds <- c(0, 1)expression.summary = calculate_expression_similarity_transcript( bams = bams, genes = genes, mapq.unique = 99)filter_genes_transcript( genes = genes, expression.matrix = expression.summary$expression.matrix, noise.thresholds = noise.thresholds,)Show the methods for calculating a noise threshold
Description
This function outputs the methods available for the calculation ofthe noise threshold. To be used as input incalculate_noise_threshold.
Usage
get_methods_calculate_noise_threshold()Value
A character vector of options for the method.chosen arguement ofcalculate_noise_threshold
Examples
get_methods_calculate_noise_threshold()Show the methods for calculating correlation or distance
Description
This function outputs the methods available for the calculation ofthe correlation or distance. The standard correlation methods use stats::cor anda wide variety of distance methods are available using the philentropy package.To be used as input incalculate_expression_similarity_counts orcalculate_expression_similarity_transcript.
Usage
get_methods_correlation_distance(names = TRUE)Arguments
names | whether to output names (default) or characterisationas similarity or dissimilarity (used internally to invert dissimilarity measures) |
Value
A character vector of options for the method arguement of the similarity calculation;if names=FALSE, a vector of types (similarity/dissimilarity measure) of the same length
Examples
get_methods_correlation_distance()Run the noisyR pipeline
Description
Calls one of noisyr_counts or noisyr_transcript, with the specified parameters.See the individual function documentation for more details and required arguments:noisyr_counts,noisyr_transcript
Usage
noisyr(approach.for.similarity.calculation = c("counts", "transcript"), ...)Arguments
approach.for.similarity.calculation | which approach to use for the similarity calculation;defaults to counts |
... | arguments to be passed on to noisyr_counts or noisyr_transcript; see their documentationfor more details and required arguments |
Value
For the counts approach, the denoised expression matrix. For the transcript approach,the numeric vector of noise thresholds per sample. For more details, see their respective documentation.
Examples
noisyr(approach.for.similarity.calculation = "counts", expression.matrix = matrix(1:100, ncol = 5))Run the noisyR pipeline for the count matrix approach
Description
Calls the functions to run each of the three steps of the pipeline(similarity calculation, noise quantification, noise removal), with the specified parameters.See the individual function documentation for more details and required arguments.Required steps:calculate_expression_similarity_counts,calculate_noise_threshold.remove_noise_from_matrix.Optional steps:optimise_window_length,calculate_noise_threshold_method_statistics
Usage
noisyr_counts( expression.matrix, n.elements.per.window = NULL, optimise.window.length.logical = FALSE, similarity.threshold = 0.25, method.chosen = "Boxplot-IQR", ...)Arguments
expression.matrix | the expression matrix used as input for the similarity calculation;this argument is required |
n.elements.per.window | number of elements to have in a window passed tocalculate_expression_similarity_counts(); default 10% of the number of rows |
optimise.window.length.logical | whether to call optimise_window_length to try andoptimise the value of n.elements.per.window |
similarity.threshold,method.chosen | parameters passed on to |
... | arguments to be passed on to individual pipeline steps |
Value
The denoised expression matrix.
See Also
Examples
noisyr_counts( expression.matrix = matrix(1:100, ncol = 5), similarity.measure = "correlation_pearson", n.elements.per.window = 3)Run the noisyR pipeline for the transcript approach
Description
Calls the functions to run each of the three steps of the pipeline(similarity calculation, noise quantification, noise removal), with the specified parameters.See the individual function documentation for more details and required arguments.Required steps:calculate_expression_similarity_transcript,calculate_noise_threshold.remove_noise_from_bams.Optional steps:calculate_noise_threshold_method_statistics
Usage
noisyr_transcript( bams = NULL, path.bams = ".", genes = NULL, path.gtf = list.files(".", pattern = "\\.g[tf]f$"), ncores = 1, similarity.threshold = 0.25, method.chosen = "Boxplot-IQR", ...)Arguments
bams,path.bams | either a path to the directory where the BAM files areor a vector of paths to each individual file; if a path is specified,it extracts all files that end in .bam; looks in the working directory by default |
genes | a tibble of the exons extracted from the gtf file;this is meant for speed if the output of |
path.gtf | the path to the gtf/gff annotation file (only used if genes is notprovided); if unspecified, looks for one in the working directory |
ncores | Number of cores for parallel computation; defaults to sequential computation,but parallelisation is highly encouraged; it is set to detectCores() if higher |
similarity.threshold,method.chosen | parameters passed on to |
... | arguments to be passed on to individual pipeline steps; see their documentationfor more details and required arguments |
Value
The denoised BAM files are created, as specified by the destination.files argumentof remove_noise_from_bams()
See Also
Examples
bams <- rep(system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE), 2)genes <- data.frame("id" = 1:2, "gene_id" = c("gene1", "gene2"), "seqid" = c("seq1", "seq2"), "start" = 1, "end" = 1600)noisyr_transcript( bams = bams, genes = genes, destination.files = paste0(tempdir(), "/", basename(bams), ".noisefiltered.bam"))Optimise the elements per window for the count matrix approach
Description
This function optimises the number of elements per windowthat is used incalculate_expression_similarity_counts, by requiringthe distribution of correlations/distances to stabilise to auniform distribution. The Jensen-Shannon divergence is used to assessthe stability.
Usage
optimise_window_length( expression.matrix, similarity.measure = "correlation_pearson", window.length.min = NULL, window.length.max = NULL, window.length.by = NULL, n.step.fraction = 0.05, iteration.number = 50, minimum.similar.windows = 3, save.plot = NULL)Arguments
expression.matrix | expression matrix, can be normalized or not |
similarity.measure | one of the correlation or distance metrics to be used,defaults to pearson correlation; list of all methods in |
window.length.min,window.length.max,window.length.by | definition of the parameter search space;default is between 1% and 33% of the number of rows in the expression matrix,incremented by 1% |
n.step.fraction | step size to slide across, as a fraction ofthe window length; default is 5% |
iteration.number | number of iterations for the subsampling and calculation of JSE;subsampling is needed because shorter windows have fewer points; default is 100 |
minimum.similar.windows | number of windows that a window needs to be similar to (including itself)in order to be accepted as optimal; default is 3, but can be reduced to 2 if no optimum is found |
save.plot | name of the pdf in which to print the output plotshowing the distribution of JSE by window; output to the console by default |
Value
A single value of the optimal number of elements per window.If no optimal value was found, this function returns NULL.
Examples
optimise_window_length( matrix(1:100+runif(100), ncol=5, byrow=TRUE), window.length.min=3, window.length.max=5, iteration.number=5)Plot the similarity against expression levels
Description
Creates the expression-similarity line and box plots for each sample.
Usage
plot_expression_similarity( expression.summary, sample.names = NULL, similarity.name = "Pearson correlation", log.transform = TRUE, min.y = NULL, max.y = NULL, smooth.span = 0.1, only.boxplot = FALSE, binsize = 1, last.together = 0, show.counts = TRUE, add.threshold = NULL, file.name = NULL)Arguments
expression.summary | list containing expression_levels and expression_levels_similaritymatrices, as calculated by |
sample.names | names for the plots, defaults to the column names of the expression matrix |
similarity.name | similarity metric used (for the y-axis title) |
log.transform | should the count matrix be log-transformed? If not, boxplot is skipped |
min.y,max.y | limits for the y axis. If unset default to symmetric including all valuesin expression.levels.similarity; min is set to 0 if there are no negative values |
smooth.span | span to be used for smoothing in the line plot; defaults to 0.1 |
only.boxplot | option to skip the line plot (usually a good idea if there are too many pointsand lines are too erratic); sets log.transform to TRUE |
binsize | size of each bin in the boxplot; defaults to 0.5 |
last.together | groups observations so the highest abundance bin has at least this many |
show.counts | whether to show how many observations are in each bin |
add.threshold | adds a horizontal line at this value |
file.name | name of pdf to output the plots; if not provided (default), no printing is done |
Value
A list of all the plots (returned silently)
Examples
plots <- plot_expression_similarity( expression.summary=list( "expression.levels" = matrix(2^(10*seq(0,1,length.out=100))), "expression.levels.similarity" = matrix(seq(0,1,length.out=100)+(runif(100)/5))))plots[[1]]plots[[2]]Function to remove the noisy reads from the BAM files
Description
This function is used to remove the noisy reads from the BAM files.It uses as input the BAM file names, a gene table (usually containing individual exons,made usingcast_gtf_to_genes), an expression matrix for each of these genes anda vector of abundance thresholds.
Usage
remove_noise_from_bams( bams, genes, expression, noise.thresholds, destination.files = base::paste0(base::basename(bams), ".noisefiltered.bam"), filter.by = c("gene", "exon"), make.index = FALSE, unique.only = TRUE, mapq.unique = 255, ...)Arguments
bams | a character vector of the BAM file names |
genes | a tibble of the exons extracted from the gtf file;(usually the the output of |
expression | the expression matrix or expression summary list,as calculated by |
noise.thresholds | a vector of expression thresholds by sample;must be the same length as the number of BAM files,or a singular value to be used as a fixed noise threshold |
destination.files | names for the output denoised BAM files; by default the same asthe original files, appended with ".noisefiltered.bam", but created in the working directory |
filter.by | Either "gene" (default) or "exon"; if filter.by="gene", a gene is removed from all BAM filesif and only if all of its exons are below the corresponding noise thresholds;if filter.by="exon", then each exon is individually removed (from all samples)if it is below the corresponding noise thresholds. |
make.index | whether a BAM index should be generated; if this is FALSE (the default)and no index exists, the function will exit with an error; the index needs to havethe same name as each BAM file, but ending with .bam.bai |
unique.only | whether only uniquely mapped reads should contribute to the expressionof a gene/exon; default is TRUE |
mapq.unique | The values of the mapping quality field in the BAM file that correspondsto uniquely mapped reads; by default, values of 255 are used as these correspond tothe most popular aligners, but an adjustment might be needed;the mapq scores should be as follows: 255 for STAR, 60 for hisat2,255 for bowtie in -k mode, 40 for bowtie2 default, 50 for tophat |
... | arguments passed on to other methods |
Value
Returns a matrix of the same dims as the expression matrix, with the noise removed.This matrix has no entries remaining below the noise threshold.
See Also
Examples
bams <- rep(system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE), 2)genes <- data.frame("id" = 1:2, "gene_id" = c("gene1", "gene2"), "seqid" = c("seq1", "seq2"), "start" = 1, "end" = 1600)noise.thresholds <- c(0, 1)expression.summary = calculate_expression_similarity_transcript( bams = bams, genes = genes, mapq.unique = 99)remove_noise_from_bams( bams = bams, genes = genes, expression = expression.summary, noise.thresholds = noise.thresholds, destination.files = paste0(tempdir(), "/", basename(bams), ".noisefiltered.bam"), mapq.unique = 99)Function to remove the noisy reads from the expression matrix
Description
This function is used to remove the noisy reads from the expression matrix.It uses as input a vector of abundance thresholds;all entries below the noise threshold are replaced with the noise threshold.
Usage
remove_noise_from_matrix( expression.matrix, noise.thresholds, add.threshold = TRUE, average.threshold = TRUE, remove.noisy.features = TRUE, export.csv = NULL, ...)Arguments
expression.matrix | the expression matrix |
noise.thresholds | a vector of expression thresholds by sample;must be the same length as the number of columns of the expression matrix,or a singular value to be used as a fixed noise threshold |
add.threshold | whether to add the noise threshold to all values in the expression matrix(default), or set entries below the threshold to the threshold |
average.threshold | if TRUE (default), uses tthe average of the vector of thresholds across all samples;if FALSE, uses the thresholds as supplied |
remove.noisy.features | logical, whether rows of the expression matrix that arefully under the noise threshold should be removed (default TRUE) |
export.csv | option to write the matrix into a csv after the noise removal;should be NULL or the name of the output file |
... | arguments passed on to other methods |
Value
Returns the expression matrix with the noise removed. Under default parameters,the denoised matrix will have fewer rows than the input matrix and will haveno entries remaining below the noise threshold.
See Also
Examples
expression.matrix <- matrix(1:100, ncol=5)noise.thresholds <- c(5,30,45,62,83)remove_noise_from_matrix( expression.matrix = expression.matrix, noise.thresholds = noise.thresholds)