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Cell types based on the expression of all genes

Source: R/enrichment.R
clustermole_enrichment.Rd

Score cell type signatures using the full gene expression matrix.

Usage

clustermole_enrichment(expr_mat, species, method = "gsva")

Arguments

expr_mat

Numeric matrix or data frame of logCPMs or logTPMs. Must contain at least 5,000 gene rows and five cluster/population columns.

species

Gene symbol species: hs for human or mm for mouse.

method

Enrichment method: gsva (default), ssgsea, singscore, or all to combine ranks from all three methods. See references below.

Value

A data frame with one row per returned signature and input column:

  • cluster: Input column name.

  • score: Enrichment score (higher means greater enrichment).

  • score_rank: Signature rank (lower means greater enrichment).

  • Signature metadata (see clustermole_markers()).

With method = "all", these columns replace score:

  • score_rank_{method}: The ranks from each method.

  • score_ranks_{stat}: Minimum, mean, and median ranks across methods.

References

Barbie, D., Tamayo, P., Boehm, J. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009). doi:10.1038/nature08460

Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013). doi:10.1186/1471-2105-14-7

Foroutan, M., Bhuva, D.D., Lyu, R. et al. Single sample scoring of molecular phenotypes. BMC Bioinformatics 19, 404 (2018). doi:10.1186/s12859-018-2435-4

Examples

# my_enrichment <- clustermole_enrichment(
#   expr_mat = my_expr_mat, species = "hs"
# )