Description Usage Arguments Examples
Count number of predicted synthetic lethal gene partners with synthetic lethal conditions and significance thresholds met.
1 2 | count_of_SL(x, significance = "fdr", threshold = 0.05,
syn_lethal = "strong", ts_sl = TRUE)
|
x |
Data matrix. Synthetic Lethal predictions to process, typically the output of |
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). |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | #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))
#count significant hits
counts <- count_of_SL(sl_table)
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