differentialExpression {celda} | R Documentation |
Uses MAST to find differentially expressed features for specified cell subpopulations.
differentialExpression( counts, celdaMod, c1, c2 = NULL, onlyPos = FALSE, log2fcThreshold = NULL, fdrThreshold = 1 )
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. |
Data frame containing MAST results including statistics such as p-value, log2 fold change, and FDR.
data(celdaCGSim, celdaCGMod) clusterDiffexpRes = differentialExpression(celdaCGSim$counts, celdaCGMod, c1 = c(1, 2))