Run MAYA for unsupervised pathway acitivity analysis

Run MAYA for unsupervised pathway acitivity analysis

MAYA_pathway_analysis(
  expr_mat,
  modules_list = NULL,
  min_cells_pct = 0.05,
  is_logcpm = T,
  nCores = 1,
  min_genes = 10,
  max_contrib = 0.5,
  compute_umap = T,
  scale_before_pca = T,
  all_PCs_in_range = F
)

Arguments

expr_mat

Raw count matrix. If normalized, set is_logcpm to T

modules_list

List with pathways associated with their genes. Can also set to "hallmark" or "kegg" to load corresponding MSigDB lists.

min_cells_pct

Numeric between 0 and 1, minimum percentage of cells that should be above informativity threshold to consider activity score interesting.

is_logcpm

Set to TRUE if data already normalized

nCores

Number of cores to use. Set to 1 if working in a Windows environment, otherwise you can use the function detectCores() to find out how many cores are available.

max_contrib

Numeric between 0 and 1, representing the maximum contribution to a PC allowed for a gene. Can influence how stringent mode selection is.

thr

Minimum average activity by cluster required to be assigned an annotation. Default 0, increase to be more stringent on assignation confidence.

Value

List containing cell type annotation, matrix of average score by Leiden cluster and cell type used for attribution, cell annotation with MLeiden clusters, activity matrix and UMAp computed on activity matrix.