mut_cna_table.Rnw

<<setup_mut_cna_table, include=FALSE>>=
library(dplyr)
library(magrittr)
library(tidyr)
ar = import('array')
df = import('data_frame')
util = import('./util_2')
config = import('../config')

mut = util$mut_cov %>%
	group_by(m) %>%
    filter(any(adj.p < 1e-7)) %>%
    ungroup()

use_amp = c("EGFR_amp", "KRAS_amp", "ERBB2_amp", "BRAF_amp",
            "TP53_del",
            "PIK3CA_amp", "PTEN_del")

cna = util$cna_cov %>%
	group_by(m) %>%
    filter(m %in% use_amp) %>%
    ungroup()

# at least one pathway per method must have FDR<0.01
tab = bind_rows(mut, cna) %>%
    transmute(Method = config$id2short(method),
              Pathway = scores,
              Subset = m,
              FDR = ifelse(adj.p < 0.001, adj.p, NA)) %>%
    group_by(Method, Subset) %>%
    filter(!all(is.na(FDR))) %>%
    ungroup() %>%
    tidyr::spread(Pathway, FDR) %>%
    arrange(Subset, Method)

na_mask = is.na(tab)
tab = tab %>%
    lapply(format, digits=2) %>%
    as.data.frame()
tab[na_mask] = "n.s."
@

\begin{table}[H]
\centering
\caption{FDR-adjusted p-values for mutation/copy number associations with
    pathway scores obtained by PROGENy. Associations using TCGA data corrected for
        cancer type. Mutated genes included in table if any method has FDR $<10^{-7}$, copy
        number alterations for related genes (e.g. ERBB2\_amp for EGFR).
}
{\tiny
<<mut_cna_table, echo=FALSE>>=
kable(tab, booktabs=TRUE)
@
}
\end{table}