pegasus.deseq2¶
- pegasus.deseq2(pseudobulk, design, contrast, de_key='deseq2', replaceOutliers=True)[source]¶
Perform Differential Expression (DE) Analysis using DESeq2 on pseduobulk data. This function calls R package DESeq2, requiring DESeq2 in R installed.
DE analysis will be performed on all pseudo-bulk matrices in pseudobulk.
- Parameters
pseudobulk (
UnimodalData
) – Pseudobulk data with rows for samples and columns for genes. If pseudobulk contains multiple matrices, DESeq2 will apply to all matrices.design (
str
) – Design formula that will be passed to DESeq2contrast (
Tuple[str, str, str]
) – A tuple of three elements passing to DESeq2: a factor in design formula, a level in the factor as numeritor of fold change, and a level as denominator of fold change.de_key (
str
, optional, default:"deseq2"
) – Key name of DE analysis results stored. For cluster.X, stored key will be cluster.de_keyreplaceOutliers (
bool
, optional, default:True
) – If execute DESeq2’s replaceOutliers step. If set toFalse
, we will set minReplicatesForReplace=Inf inDESeq
function and set cooksCutoff=False inresults
function.
- Return type
None
- Returns
None
Update
pseudobulk.varm
–pseudobulk.varm[de_key]
: DE analysis result for pseudo-bulk count matrix.pseudobulk.varm[cluster.de_key]
: DE results for cluster-specific pseudo-bulk count matrices.
Examples
>>> pg.deseq2(pseudobulk, '~gender', ('gender', 'female', 'male'))