differentialExpression: Differential expression for cell subpopulations using MAST
In compbiomed/celda: CEllular Latent Dirichlet Allocation
Description
Usage
Arguments
Value
Examples
Description
Uses MAST to find differentially expressed features for specified cell subpopulations.
Usage
1
2
differentialExpression(counts, celda.mod, c1, c2 = NULL, only.pos = FALSE,
log2fc.threshold = NULL, fdr.threshold = 1)
Arguments
counts
Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate 'celda.mod'.
celda.mod
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.
only.pos
Logical. Whether to only return markers with positive log2 fold change. Default FALSE.
log2fc.threshold
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.
fdr.threshold
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
1
cluster.diffexp.res = differentialExpression(celda.CG.sim$counts, celda.CG.mod, c1=c(1,2))
compbiomed/celda documentation built on May 25, 2019, 3:58 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
Description
Uses MAST to find differentially expressed features for specified cell subpopulations.
Usage
1 2 | differentialExpression(counts, celda.mod, c1, c2 = NULL, only.pos = FALSE,
log2fc.threshold = NULL, fdr.threshold = 1)
|
Arguments
counts |
Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate 'celda.mod'. |
celda.mod |
Celda object of class 'celda_C' or 'celda_CG'. |
c1 |
Integer vector. Cell populations to include in group 1 for the differential expression analysis. |
c2 |
Integer vector. Cell populations to include in group 2 for the differential expression analysis. If NULL, the clusters in the c1 group are compared to all other clusters. Default NULL. |
only.pos |
Logical. Whether to only return markers with positive log2 fold change. Default FALSE. |
log2fc.threshold |
Numeric. A number greater than 0 that specifies the absolute log2 fold change threshold. Only features with absolute value above this threshold will be returned. If NULL, this filter will not be applied. Default NULL. |
fdr.threshold |
Numeric. A number between 0 and 1 that specifies the false discovery rate (FDR) threshold. Only features below this threshold will be returned. Default 1. |
Value
Data frame containing MAST results including statistics such as p-value, log2 fold change, and FDR.
Examples
1 | cluster.diffexp.res = differentialExpression(celda.CG.sim$counts, celda.CG.mod, c1=c(1,2))
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.