View source: R/expression_processing.R
get_pseudobulk_logCPM_exprs | R Documentation |
get_pseudobulk_logCPM_exprs
Calculate the 'library-size' normalized pseudbulk counts per sample for each gene - returned values are similar to logCPM.
get_pseudobulk_logCPM_exprs(sce, sample_id, celltype_id, group_id, batches = NA, assay_oi_pb = "counts", fun_oi_pb = "sum")
sce |
SingleCellExperiment object of the scRNAseq data of interest. Contains both sender and receiver cell types. |
sample_id |
Name of the meta data column that indicates from which sample/patient a cell comes from |
celltype_id |
Name of the column in the meta data of sce that indicates the cell type of a cell. |
group_id |
Name of the meta data column that indicates from which group/condition a cell comes from |
batches |
NA if no batches should be corrected for. If there should be corrected for batches during DE analysis and pseudobulk expression calculation, this argument should be the name(s) of the columns in the meta data that indicate the batch(s). Should be categorical. Pseudobulk expression values will be corrected for the first element of this vector. |
assay_oi_pb |
Indicates which information of the assay of interest should be used (counts, scaled data,...). Default: "counts". See 'muscat::aggregateData'. |
fun_oi_pb |
Indicates way of doing the pseudobulking. Default: "sum". See 'muscat::aggregateData'. |
Data frame with logCPM-like values of the library-size corrected pseudobulked counts ('pb_sample') per gene per sample. pb_sample = log2( ((pb_raw/effective_library_size) \* 1000000) + 1). effective_library_size = lib.size \* norm.factors (through edgeR::calcNormFactors).
## Not run:
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
pseudobulk_logCPM_exprs = get_pseudobulk_logCPM_exprs(sce = sce, sample_id = sample_id, celltype_id = celltype_id, group_id = group_id)
## End(Not run)
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