Subset a Binary Matrix By Genes Available on Specified Panel
Source:R/subset-by-panel.R
subset_by_panel.RdSubset a Binary Matrix By Genes Available on Specified Panel
Arguments
- gene_binary
A data frame with a row for each sample and column for each alteration. Data frame must have a
sample_idcolumn and columns for each alteration with values of 0, 1 or NA.- panel_id
A character string or vector of the specified panel to subset the genes (see
gnomeR::gene_panelsfor available panels)- other_vars
One or more column names (quoted or unquoted) in data to be retained in resulting data frame. Default is NULL.
Value
a data frame with a sample_id column and columns for
alterations on genes that were sequenced on the specified panel.
Examples
samples <- unique(gnomeR::mutations$sampleId)
gene_binary <- create_gene_binary(
samples = samples, mutation = mutations, cna = cna,
mut_type = "somatic_only",
include_silent = FALSE,
specify_panel = "impact"
)
subset_by_panel(gene_binary = gene_binary, panel_id = "IMPACT468")
#> ℹ There are 244 genes on the IMPACT468 panel that are not altered for any patients; columns for those genes are not included in the resulting data frame. To see the names of the genes that are not mutated for any patients in the sample, To view and compare genes in each panel, see `unnest(gnomeR::gene_panels, cols = c('genes_in_panel'))`
#> # A tibble: 200 × 228
#> sample_id AKT1 AKT3 ALK ANKRD11 APC AR ARAF ARID1A ARID1B ARID2
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 P-0001128-T0… 1 0 0 NA 0 0 0 0 0 0
#> 2 P-0001859-T0… 0 0 0 NA 1 0 0 0 0 0
#> 3 P-0001895-T0… 0 0 1 NA 0 0 0 0 0 0
#> 4 P-0001845-T0… 1 0 0 NA 0 0 0 0 0 0
#> 5 P-0001768-T0… 0 0 1 NA 0 0 0 0 0 0
#> 6 P-0002984-T0… 0 0 1 NA 0 0 0 0 0 0
#> 7 P-0000964-T0… 0 0 0 NA 1 0 0 0 0 0
#> 8 P-0000964-T0… 0 0 0 NA 1 0 0 0 0 0
#> 9 P-0000610-T0… 0 0 0 NA 1 0 0 0 0 0
#> 10 P-0001247-T0… 0 0 0 NA 1 0 0 0 0 0
#> # ℹ 190 more rows
#> # ℹ 217 more variables: ARID5B <dbl>, ASXL1 <dbl>, ASXL2 <dbl>, ATM <dbl>,
#> # ATR <dbl>, ATRX <dbl>, AURKB <dbl>, AXIN1 <dbl>, AXIN2 <dbl>, BAP1 <dbl>,
#> # BARD1 <dbl>, BCL6 <dbl>, BCOR <dbl>, BLM <dbl>, BMPR1A <dbl>, BRAF <dbl>,
#> # BRCA1 <dbl>, BRCA2 <dbl>, BRIP1 <dbl>, CARD11 <dbl>, CBL <dbl>,
#> # CCND1 <dbl>, CD276 <dbl>, CD79B <dbl>, CDC73 <dbl>, CDH1 <dbl>,
#> # CDK12 <dbl>, CDK4 <dbl>, CDK8 <dbl>, CDKN1A <dbl>, CDKN2C <dbl>, …
p_genes <- tidyr::unnest(gnomeR::gene_panels, cols = c("genes_in_panel"))
p_genes <- p_genes[p_genes$gene_panel == 'IMPACT300', ]
setdiff(p_genes$genes_in_panel, names(gene_binary))
#> [1] "ABL1" "ABL2" "AKT2" "ALOX12B" "AMER1" "ARHGAP26"
#> [7] "AURKA" "BCL2L1" "BCL2L11" "BIRC2" "BUB1B" "CBLB"
#> [13] "CBLC" "CCND2" "CCND3" "CCNE1" "CDC42EP2" "CDH11"
#> [19] "CDK6" "CDKN2A" "CDKN2B" "CEBPA" "CHEK1" "CRKL"
#> [25] "CRLF2" "CYLD" "DIS3" "DNMT3A" "DNMT3B" "E2F3"
#> [31] "EIF4EBP1" "EPHA10" "EPHA2" "EPHA4" "EPHA6" "EPHA7"
#> [37] "EPHA8" "EPHB2" "EPHB3" "EPHB4" "EPHB6" "EZH2"
#> [43] "FAS" "FAT4" "FBXO11" "FBXW7" "FGFR3" "FKBP1A"
#> [49] "FLT4" "GATA1" "GATA2" "GLI3" "GNA11" "GNAS"
#> [55] "GOLPH3" "GRM3" "GSK3B" "HDAC2" "HIF1A" "HLA-A"
#> [61] "HMGA2" "HSP90AA1" "IGFBP7" "IKBKE" "IL7R" "INSR"
#> [67] "IRF4" "JUN" "KCNJ5" "KDR" "KEAP1" "KIT"
#> [73] "KLF6" "LDHA" "LGR6" "MAGI2" "MAP2K4" "MAP3K1"
#> [79] "MAP3K8" "MCL1" "MLST8" "MPL" "MYB" "MYCL"
#> [85] "MYCN" "MYD88" "NCOA2" "NF2" "NFE2L2" "NFKB1"
#> [91] "NFKB2" "NOTCH2" "NRAS" "PAK5" "PBRM1" "PHOX2B"
#> [97] "PKM" "PNRC1" "POLE" "PPP6C" "PREX2" "PRKAA2"
#> [103] "PRKAR1A" "PRKCI" "PRKN" "PTPN11" "RAC1" "RAD50"
#> [109] "RAF1" "REL" "RICTOR" "ROR2" "RPS6KB1" "SMARCA4"
#> [115] "SMARCB1" "SOCS1" "SRC" "SRSF2" "SUFU" "SYK"
#> [121] "TBK1" "TEK" "TERT" "TNFRSF14" "TSC1" "TSHR"
#> [127] "WAS" "WNK1" "YES1" "ZRSR2"