differentialExpression {celda} | R Documentation

## Differential expression for cell subpopulations using MAST

### Description

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

### Usage

    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

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

[Package celda version 1.0.4 Index]