DESeq2Test: Differential expression using DESeq2
In Coolgenome/iTALK: Characterize and Illustrate Intercellular Communication

Description Usage Arguments Details Value References

Identifies differentially expressed genes between two groups of cells using DESeq2

DESeq2Test(sub_data, min_gene_expressed, min_valid_cells,
  contrast = unique(sub_data$compare_group), test = "Wald",
  fitType = "parametric", sfType = "ratio", betaPrior = FALSE,
  quiet = FALSE, modelMatrixType = "standard",
  minReplicatesForReplace = 7, useT = FALSE, minmu = 0.5,
  parallel = FALSE, BPPARAM = bpparam())

`sub_data`	Count data removed cell_type and selected certain two compare_group
`min_gene_expressed`	Genes expressed in minimum number of cells
`min_valid_cells`	Minimum number of genes detected in the cell
`contrast`	String vector specifying the contrast to be tested against the log2-fold-change threshold
`test`	either "Wald" or "LRT", which will then use either Wald significance tests (defined by `nbinomWaldTest`), or the likelihood ratio test on the difference in deviance between a full and reduced model formula (defined by `nbinomLRT`)
`fitType`	either "parametric", "local", or "mean" for the type of fitting of dispersions to the mean intensity. See `estimateDispersions` for description.
`sfType`	either "ratio", "poscounts", or "iterate" for teh type of size factor estimation. See `estimateSizeFactors` for description.
`betaPrior`	whether or not to put a zero-mean normal prior on the non-intercept coefficients See `nbinomWaldTest` for description of the calculation of the beta prior. In versions `>=1.16`, the default is set to `FALSE`, and shrunken LFCs are obtained afterwards using `lfcShrink`.
`quiet`	whether to print messages at each step
`modelMatrixType`	either "standard" or "expanded", which describe how the model matrix, X of the GLM formula is formed. "standard" is as created by `model.matrix` using the design formula. "expanded" includes an indicator variable for each level of factors in addition to an intercept. for more information see the Description of `nbinomWaldTest`. betaPrior must be set to TRUE in order for expanded model matrices to be fit.
`minReplicatesForReplace`	the minimum number of replicates required in order to use `replaceOutliers` on a sample. If there are samples with so many replicates, the model will be refit after these replacing outliers, flagged by Cook's distance. Set to `Inf` in order to never replace outliers.
`useT`	logical, passed to `nbinomWaldTest`, default is FALSE, where Wald statistics are assumed to follow a standard Normal
`minmu`	lower bound on the estimated count for fitting gene-wise dispersion and for use with `nbinomWaldTest` and `nbinomLRT`
`parallel`	if FALSE, no parallelization. if TRUE, parallel execution using `BiocParallel`, see next argument `BPPARAM`. A note on running in parallel using `BiocParallel`: it may be advantageous to remove large, unneeded objects from your current R environment before calling `DESeq`, as it is possible that R's internal garbage collection will copy these files while running on worker nodes.
`BPPARAM`	an optional parameter object passed internally to `bplapply` when `parallel=TRUE`. If not specified, the parameters last registered with `register` will be used.

This test does not support pre-processed genes. To use this method, please install DESeq2, using the instructions at https://bioconductor.org/packages/release/bioc/html/DESeq2.html

A matrix of differentially expressed genes and related statistics.

Love MI, Huber W and Anders S (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome Biology. https://bioconductor.org/packages/release/bioc/html/DESeq2.html

Coolgenome/iTALK documentation built on Aug. 3, 2019, 3:12 p.m.