table_of_SL: A Subsetting Function to process SLIPT predictions

In TomKellyGenetics/slipt: Synthetic Lethal Interaction Prediction Tool (SLIPT) from Cancer Genomics Data

Description

Extract the predictions synthetic lethal gene partners with synthetic lethal conditions and significance thresholds met.

Usage

table_of_SL(x, significance = "fdr", threshold = 0.05,
  syn_lethal = "strong", ts_sl = TRUE)

Arguments

x	Data matrix. Synthetic Lethal predictions to process, typically the output of detect_SL
significance	String. Significance condition for p-values to use: none, raw, or adjusted with a valid method for p.adjust() Defaults to fdr (false discovery rate / BH).
threshold	Numeric. Significance threshold (alpha, type I error rate) to cut-off (raw or adjusted) p-values. Defaults to 0.05.
syn_lethal	String. Stringency of synthetic lethal directional condition. Defaults to 'strong': symmetric replications. Other options are 'query' or 'candidate' or one-directional conditions (which gene is low in a low-high condition) or 'weak' for either one-directional condition.
ts_sl	Logical. Defaults to TRUE. Whether to test for synthetic lethality against low or high gene perturbations for tumour supressor gene inactivation (default) or oncogenic overactivation (alternative).

Examples

#prepare data
 data <- c()
for(i in 1:100){
  data <- cbind(data, rnorm(1000))
}
rm(i)
rownames(data) <- paste("gene", 1:1000)
colnames(data) <- paste("sample", 1:100)
partitioned_data <- prep_data_for_SL(data, n = 3)

#run SLIPT
sl_table <- detect_SL("gene 1", partitioned_data)
dim(sl_table)
head(dim(sl_table))

sl_table <- detect_SL("gene 1", prep_data_for_SL(data))
dim(sl_table)
head(dim(sl_table))

#extract significant hits
short_table <- table_of_SL(sl_table)

TomKellyGenetics/slipt documentation built on May 8, 2019, 9:28 a.m.