runAllMirnaModels {mirTarRnaSeq} | R Documentation |
This function runs the "runModel" function for all miRNAs and mRNA combinations of two and returns a list with significant genes and FDR models
runAllMirnaModels( mirnas, DiffExpmRNA, DiffExpmiRNA, miranda_data, prob = 0.75, fdr_cutoff = 0.1, method = "fdr", cutoff = 0.05, all_coeff = FALSE, mode = NULL, family = glm_poisson(), scale = 1 )
mirnas |
vector of unique miRNAs under investigation. |
DiffExpmRNA |
deferentially/expressed mRNAs expression file. |
DiffExpmiRNA |
deferentially/expressed miRNAs expression file. |
miranda_data |
getInputSpecies output file ( use low filters). |
prob |
user defined ratio for miRanda distribution for miRanda score selection default is 0.75. |
fdr_cutoff |
cutoff for FDR selection default is 0.1. |
method |
finInterResult miRNA and mRNA interrelation in two time points results in a dataframe. |
cutoff |
P-value cutoff of the model. |
all_coeff |
if true only models with all negative coefficients will be selected if false at least one negative coefficient should be in the model; default is TRUE. |
mode |
model mode, default is Null, can be changed to "multi" and "inter". |
family |
Default is glm_poisson(), for zero inflated negative binomial NB option use glm_zeroinfl(dist="negbin"). |
scale |
if normalized data (FPKM,RPKM,TPM,CPM), scale to 10 etc., however the higher you go on #scale the less accuracy your p-value estimate will be. |
List of run models
mirnas <- c("ebv-mir-bart9-5p", "ebv-mir-bart6-3p") x <- runAllMirnaModels(mirnas, mRNA, miRNA, miRanda, prob = 0.90, fdr_cutoff = 0.1, method = "fdr", all_coeff = TRUE, mode = "multi", family = glm_poisson(), scale = 100 )