muscat_analysis: muscat_analysis

In saeyslab/muscatWrapper: Wrapper functions around muscat for differential expression analysis of multi-sample multi-condition scRNAseq datasets

muscat_analysis

Description

muscat_analysis Perform a multi-sample multi-condition DE analysis with the pseudobulk approach implemented in muscat.

Usage

muscat_analysis(
sce, celltype_id, sample_id, group_id, covariates, contrasts_oi, contrast_tbl, assay_oi_pb ="counts", fun_oi_pb = "sum", de_method_oi = "edgeR", min_cells = 10, verbose = FALSE
)

Arguments

sce	SingleCellExperiment object of the scRNAseq data of interest.
celltype_id	Name of the column in the meta data of sce that indicates the cell type of a cell.
sample_id	Name of the meta data column that indicates from which sample/patient a cell comes from (in sce)
group_id	Name of the meta data column that indicates from which group/condition a cell comes from (in sce)
covariates	NA if no covariates should be corrected for. If there should be corrected for covariates, this argument should be the name(s) of the columns in the meta data that indicate the covariate(s).
contrasts_oi	String indicating the contrasts of interest (= which groups/conditions will be compared) for the differential expression and MultiNicheNet analysis. We will demonstrate here a few examples to indicate how to write this. Check the limma package manuals for more information about defining design matrices and contrasts for differential expression analysis. If wanting to compare group A vs B: ‘contrasts_oi = c("’A-B'")' If wanting to compare group A vs B & B vs A: ‘contrasts_oi = c("’A-B','B-A'")' If wanting to compare group A vs B & A vs C & A vs D: ‘contrasts_oi = c("’A-B','A-C', 'A-D'")' If wanting to compare group A vs B and C: ‘contrasts_oi = c("’A-(B+C)/2'")' If wanting to compare group A vs B, C and D: ‘contrasts_oi = c("’A-(B+C+D)/3'")' If wanting to compare group A vs B, C and D & B vs A,C,D: ‘contrasts_oi = c("’A-(B+C+D)/3', 'B-(A+C+D)/3'")' Note that the groups A, B, ... should be present in the meta data column 'group_id'.
contrast_tbl	Data frame providing names for each of the contrasts in contrasts_oi in the 'contrast' column, and the corresponding group of interest in the 'group' column. Entries in the 'group' column should thus be present in the group_id column in the metadata. Example for ‘contrasts_oi = c("’A-(B+C+D)/3', 'B-(A+C+D)/3'")': 'contrast_tbl = tibble(contrast = c("A-(B+C+D)/3","B-(A+C+D)/3"), group = c("A","B"))'
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'.
de_method_oi	Indicates the DE method that will be used after pseudobulking. Default: "edgeR". See 'muscat::pbDS'.
min_cells	Indicates the minimal number of cells that a sample should have to be considered in the DE analysis. Default: 10. See 'muscat::pbDS'.
verbose	Indicate which different steps of the pipeline are running or not. Default: FALSE.

Value

List containing information and output of the Muscat analysis.
celltype_info: contains average expression value and fraction of each cell type - sample combination,
celltype_de: contains output of the differential expression analysis,
grouping_tbl: data frame showing the group per sample

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
covariates = NA
contrasts_oi = c("'High-Low','Low-High'")
contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
output = muscat_analysis(
     sce = sce,
     celltype_id = celltype_id,
     sample_id = sample_id,
     group_id = group_id,
     covariates = covariates,
     contrasts_oi = contrasts_oi,
     contrast_tbl = contrast_tbl)

## End(Not run)

saeyslab/muscatWrapper documentation built on March 11, 2023, 6:14 p.m.