R package to do enrichment analysis for neoantigens
Install by devtools::install_github("wt12318/NeoEnrichment",ref="dev")
For calculating ESccf, we need supply a dataframe with at least 3 columns:
- sample, sample name
- neo, indicating whether a mutation (a row of the dataframe) is neoantigentic mutation; value can be "yes" or "no"
- ccf, indicating the CCF value of mutations, range from 0 to 1.
Then we can use the cal_nes_new_test function to calculate ESccf for one sample:
a <- NeoEnrichment::cal_nes_new_test(dt = data, sample_counts = 1000, need_p = FALSE)
There are three parameters of the function cal_nes_new_test:
- dt, the mutation dataframe mentioned above
- need_p, whether need calculated p values
- sample_counts, the number of random sampling when calculate p values.
For calculating ESrna, we also need supply a dataframe with at least 3 columns:
- sample, sample name
- neo, indicating whether a mutation (a row of the dataframe) is neoantigentic mutation; value can be "neo" or "not_neo"
- exp, indicating the expression of gene which the mutation located (often in TPM unit)
Then we can use the cales_t function to calculate ESexp for samples (can be used for multiple samples):
a <- NeoEnrichment::cales_t(data = dt,barcode = x,type = "II",
calp = FALSE,sample_counts = 1000,
cal_type = "exp")
There are six parameters of the function cales_t:
- data, the mutation dataframe mentioned above
- barcode, the barcode of the sample needed to run
- type, "I" or "II", "I" means put more weight on neoantigentic mutations, while "II" means put equal weights, we used "II" in our paper
- calp, whether need calculated p values,
- sample_counts, the number of random sampling when calculate p values
- cal_type, the ES type we need calculate, the "CCF" was discarded.