differentialExpression: Differential expression for cell subpopulations using MAST

Description Usage Arguments Value Examples

View source: R/diffExp.R

Description

Uses MAST to find differentially expressed features for specified cell subpopulations.

Usage

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differentialExpression(
  counts,
  celdaMod,
  c1,
  c2 = NULL,
  onlyPos = FALSE,
  log2fcThreshold = NULL,
  fdrThreshold = 1
)

Arguments

counts

Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate 'celdaMod'.

celdaMod

Celda object of class 'celda_C' or 'celda_CG'.

c1

Integer vector. Cell populations to include in group 1 for the differential expression analysis.

c2

Integer vector. Cell populations to include in group 2 for the differential expression analysis. If NULL, the clusters in the c1 group are compared to all other clusters. Default NULL.

onlyPos

Logical. Whether to only return markers with positive log2 fold change. Default FALSE.

log2fcThreshold

Numeric. A number greater than 0 that specifies the absolute log2 fold change threshold. Only features with absolute value above this threshold will be returned. If NULL, this filter will not be applied. Default NULL.

fdrThreshold

Numeric. A number between 0 and 1 that specifies the false discovery rate (FDR) threshold. Only features below this threshold will be returned. Default 1.

Value

Data frame containing MAST results including statistics such as p-value, log2 fold change, and FDR.

Examples

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data(celdaCGSim, celdaCGMod)
clusterDiffexpRes <- differentialExpression(celdaCGSim$counts,
  celdaCGMod,
  c1 = c(1, 2)
)

celda documentation built on June 9, 2020, 2 a.m.