scDiffEx: Perform differential expression analysis on a SCtkExperiment...

In mmkhan19/singleCellTK: Interactive Analysis of Single Cell RNA-Seq Data

Perform differential expression analysis on a SCtkExperiment object

Description

Perform differential expression analysis on a SCtkExperiment object

Usage

scDiffEx(
  inSCE,
  useAssay = "logcounts",
  condition,
  covariates = NULL,
  significance = 0.05,
  ntop = 500,
  usesig = TRUE,
  diffexmethod,
  levelofinterest = NULL,
  analysisType = NULL,
  controlLevel = NULL,
  adjust = "fdr"
)

scDiffExDESeq2(
  inSCE,
  useAssay = "counts",
  condition,
  analysisType = "biomarker",
  levelofinterest = NULL,
  controlLevel = NULL,
  covariates = NULL,
  adjust = "fdr"
)

scDiffExlimma(
  inSCE,
  useAssay = "logcounts",
  condition,
  analysisType = "biomarker",
  levelofinterest = NULL,
  covariates = NULL,
  adjust = "fdr"
)

scDiffExANOVA(
  inSCE,
  useAssay = "logcounts",
  condition,
  covariates = NULL,
  adjust = "fdr"
)

Arguments

inSCE	Input SCtkExperiment object. Required
useAssay	Indicate which assay to use. Default is "logcounts" for limma and ANOVA, and "counts" for DESeq2.
condition	The name of the condition to use for differential expression. Must be a name of a column from colData that contains at least two labels. Required
covariates	Additional covariates to add to the model. Default is NULL
significance	FDR corrected significance cutoff for differentially expressed genes. Required
ntop	Number of top differentially expressed genes to display in the heatmap. Required
usesig	If TRUE, only display genes that meet the significance cutoff, up to ntop genes. Required
diffexmethod	The method for performing differential expression analysis. Available options are DESeq2, limma, and ANOVA. Required
levelofinterest	If the condition has more than two labels, levelofinterest should contain one factor for condition. The differential expression results will use levelofinterest depending on the analysisType parameter.
analysisType	For conditions with more than two levels, limma and DESeq2 can be run using multiple methods. For DESeq2, choose "biomarker" to compare the levelofinterest to all other samples. Choose "contrast" to compare the levelofinterest to a controlLevel (see below). Choose "fullreduced" to perform DESeq2 in LRT mode. For limma, Choose "biomarker" to compare the levelofinterest to all other samples. Choose "coef" to select a coefficient of interest with levelofinterest (see below). Choose "allcoef" to test if any coefficient is different from zero.
controlLevel	If the condition has more than two labels, controlLevel should contain one factor from condition to use as the control.
adjust	Method for p-value correction. See options in p.adjust(). The default is fdr.

Value

A data frame of gene names and adjusted p-values

Functions

• scDiffExDESeq2: Perform differential expression analysis with DESeq2

• scDiffExlimma: Perform differential expression analysis with limma

• scDiffExANOVA: Perform differential expression analysis with ANOVA

Examples

data("mouseBrainSubsetSCE")
res <- scDiffEx(mouseBrainSubsetSCE,
                useAssay = "logcounts",
                "level1class",
                diffexmethod = "limma")

data("mouseBrainSubsetSCE")
#sort first 100 expressed genes
ord <- rownames(mouseBrainSubsetSCE)[
  order(rowSums(assay(mouseBrainSubsetSCE, "counts")),
        decreasing = TRUE)][1:100]
#subset to those first 100 genes
subset <- mouseBrainSubsetSCE[ord, ]
res <- scDiffExDESeq2(subset, condition = "level1class")

data("mouseBrainSubsetSCE")
res <- scDiffExlimma(mouseBrainSubsetSCE, condition = "level1class")

data("mouseBrainSubsetSCE")
res <- scDiffExANOVA(mouseBrainSubsetSCE, condition = "level1class")

mmkhan19/singleCellTK documentation built on March 22, 2022, 7:43 a.m.