Perform differential expression analysis on a SCtkExperiment object

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")
#> Warning: Zero sample variances detected, have been offset away from zero
#> Removing intercept from test coefficients
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")
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the #> function: y = a/x + b, and a local regression fit was automatically substituted. #> specify fitType='local' or 'mean' to avoid this message next time.
#> final dispersion estimates
#> fitting model and testing
data("mouseBrainSubsetSCE") res <- scDiffExlimma(mouseBrainSubsetSCE, condition = "level1class")
#> Warning: Zero sample variances detected, have been offset away from zero
#> Removing intercept from test coefficients
data("mouseBrainSubsetSCE") res <- scDiffExANOVA(mouseBrainSubsetSCE, condition = "level1class")