Calculate condition-specific cell-cell communication mediated by secreted proteins from scRNA-Seq data.
Usage
SecAct.CCC.scRNAseq(
Seurat_obj,
cellType_meta,
condition_meta,
conditionCase,
conditionControl,
scale.factor = 1e+05,
act_diff_cutoff = 2,
exp_logFC_cutoff = 0.2,
exp_mean_all_cutoff = 2,
exp_fraction_case_cutoff = 0.1,
padj_cutoff = 0.01,
sigMatrix = "SecAct",
is.group.sig = TRUE,
is.group.cor = 0.9,
lambda = 5e+05,
nrand = 1000
)Arguments
- Seurat_obj
A Seurat object.
- cellType_meta
Column name in meta data that includes cell-type annotations.
- condition_meta
Column name in meta data that includes condition information.
- conditionCase
Case condition.
- conditionControl
Control condition.
- scale.factor
Sets the scale factor for cell-level normalization in step2.
- act_diff_cutoff
Cut off for activity change (i.e., z score) in step 1.
- exp_logFC_cutoff
Cut off for log fold change in step 2.
- exp_fraction_case_cutoff
Cut off for the fraction of cells expressing secreted protein-coding genes in step 2.
- padj_cutoff
Adjusted p value cut off.
- sigMatrix
Secreted protein signature matrix.
- is.group.sig
A logical indicating whether to group similar signatures.
- is.group.cor
Correlation cutoff of similar signatures.
- lambda
Penalty factor in the ridge regression.
- nrand
Number of randomization in the permutation test, with a default value 1000.