Description Usage Arguments Details Value Examples
This is the pathway-based analysis pipeline.
1 2 3 4 | perform_main_pathway(df_user_mut, df_user_exp, sid_pattern = c("-01$",
"-11$"), mcutoff = 2, fraction = 0.6, padj = 0.01,
log2_fold_change = 2, jobname = "tcga", raw_count = TRUE,
use_cache = FALSE)
|
df_user_mut |
Mutation dataframe. |
df_user_exp |
Gene expression dataframe. |
sid_pattern |
A regex string to match case and control IDs. |
mcutoff |
A float number between 0 and 6, default is 2. |
fraction |
A float number between 0 and 1, default is 0.6. |
padj |
Adjusted p value threshold for differential analysis, default is 0.01. |
log2_fold_change |
log2 transformed fold change threshold for differential analysis, default is 2. |
jobname |
unique job name. |
raw_count |
Logical value, set TRUE to process RSEM-based expression data. |
use_cache |
Logical value, set TRUE to save intermediate result. |
df_user_mut
is a dataframe with 7 columns, which are "Hugo_Symbol",
"Chromosome", "Start_position", "End_position", "Reference_Allele",
"Tumor_Seq_Allele2", "Tumor_Sample_Barcode". df_user_exp
is a
dataframe, row names are gene symbols, and column names are sample IDs.
sid_pattern
is a regex string that can distingish case ID and control
ID.
A list.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Not run:
mut_data <- firehose_get("HNSC", "mutation", run_date = "2015_08_21", run_type = "stddata")
mut_data <- mut_data[[1]]
mut_sample_ids <- unique(mut_data[[7]])
exp_data <- firehose_get("HNSC", "expression", run_date = "2015_08_21", run_type = "stddata")
exp_data <- exp_data[[1]]
exp_sample_ids <- colnames(exp_data)
common_case <- intersect(mut_sample_ids, exp_sample_ids)
exp_control <- grepl("-11$", exp_sample_ids)
hnsc_mut <- mut_data[mut_data[[7]] %in% common_case, ]
hnsc_exp <- exp_data[, (exp_sample_ids %in% common_case) | exp_control]
prepare_ma()
res <- perform_main_pathway(hnsc_mut, hnsc_exp, jobname = "HNSC", use_cache = TRUE)
## End(Not run)
|
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