NeoEnrichment

R package to do enrichment analysis for neoantigens Install by devtools::install_github("wt12318/NeoEnrichment",ref="dev")

Usage

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.