DEBRA: DEBRA - DESeq-based Barcode Representation Analysis

In YevhenAkimov/DEBRA_1.01:

DEBRA - DESeq-based Barcode Representation Analysis

Description

DEBRA - DESeq-based Barcode Representation Analysis

Usage

DEBRA(counts, control_names, condition_names, beta = -Inf,
  method = "DESeq", trended = T, filter_FDR = 0.2,
  default_beta = 0, shrunkLFC = F, modified = T)

Arguments

counts	a data frame of non-negative read counts with columns of samples and rownames of barcode IDs; samples not included into analysis are allowed
control_names	a character vector specifying the control samples (colnames of the counts data frame)
condition_names	a character vector specifying the condition samples (colnames of the counts data frame)
beta	a numeric specifying beta value providing a lower read count threshold value for an independent filtering step; if beta = -Inf (default), the beta will be estimated from the read counts of condition samples
method	a character specifying the method used for inferring differentially represented barcodes
trended	a logical specifying if the trended dispersion estimates should be used; if trended=FALSE, the shrunken dispersion estimates (as estimated by DESeq2) are used
default_beta	a numeric specifying the beta value used if the beta estimation is failed
modified	a logical, if modified = F then the non-modified version of the correspondig method (DESeq, DESeq2(Wald) or DESeq2(LRT)) will be run; note independent filtering using beta threshold can still be applied
shrinkLFC	a logical specifying if the logFC values should be shrunken using "apeglm" shrinkage estimator

YevhenAkimov/DEBRA_1.01 documentation built on April 4, 2022, 6:19 a.m.