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library(viper)
library(aracne.networks)
library(dplyr)
library(plyr)
library(stringr)
library(Biobase)
library(EnsDb.Hsapiens.v86)
dir.create("output/DEG/",showWarnings = FALSE)
dir.create("output/expr/",showWarnings = FALSE)read_exp <- function(file_name) {
expr <- read.table(file_name, header = TRUE, sep = "\t", row.names = 1,
as.is = TRUE)
expr[is.na(expr)] <- 0
n_0 <- count_zeros_in_rows(as.matrix(expr))
# Remove features with more 20% zero/missing values
expr <- expr[n_0 >= ncol(expr)/5, ]
# Split rownames by |
meta <- data.frame(rownames(expr))
colnames(meta) <- c("ENSG")
meta$ENSG <- sub("\\..*$", "", meta$ENSG)
geneID <- ensembldb::select(EnsDb.Hsapiens.v86, keys = meta$ENSG, keytype = "GENEID",
columns = c("SYMBOL", "UNIPROTID", "GENEID"))
meta$UNIPROTID <- plyr::mapvalues(meta$ENSG, from = geneID$GENEID,
to = geneID$UNIPROTID, warn_missing = FALSE)
meta$SYMBOL <- plyr::mapvalues(meta$ENSG, from = geneID$GENEID, to = geneID$SYMBOL,
warn_missing = FALSE)
# Remove duplicated indexs
binary_unique_index <- (!duplicated(meta$SYMBOL)) & (!is.na(meta$SYMBOL) &
(!sapply(meta$SYMBOL, function(x) startsWith(x, "ENSG"))))
print(table(binary_unique_index))
meta <- meta[binary_unique_index, ]
expr <- expr[binary_unique_index, ]
rownames(expr) <- meta$SYMBOL
rownames(meta) <- rownames(expr)
return(list(expr, meta))
}
calculate_log_fold_change_and_pvalue <- function(data_matrix, group, adjust_method = "BH") {
# Check if the length of the group variable matches the number of
# columns in the data matrix
if (length(group) != ncol(data_matrix)) {
stop("The length of the group variable must match the number of columns in the data matrix.")
}
# Ensure the group variable contains only 'normal' and 'tumor'
if (!all(group %in% c("normal", "tumor"))) {
stop("The group variable must only contain 'normal' and 'tumor' values.")
}
# Calculate the mean for each row in the tumor and normal groups
mean_tumor <- rowMeans(data_matrix[, group == "tumor"], na.rm = TRUE)
mean_normal <- rowMeans(data_matrix[, group == "normal"], na.rm = TRUE)
# Calculate the fold change
fold_change <- mean_tumor - mean_normal
# Initialize a vector to store p-values
p_values <- numeric(nrow(data_matrix))
# Perform t-test for each row
for (i in 1:nrow(data_matrix)) {
normal_values <- data_matrix[i, group == "normal"]
tumor_values <- data_matrix[i, group == "tumor"]
wilcox_test_result <- wilcox.test(normal_values, tumor_values,
paired = FALSE)
p_values[i] <- wilcox_test_result$p.value
}
# Adjust the p-values
adjusted_p_values <- p.adjust(p_values, method = adjust_method)
# Create a data frame with fold change, p-values, and adjusted
# p-values
results <- data.frame(Fold_Change = fold_change, P_Value = p_values,
Adjusted_P_Value = adjusted_p_values)
# Return the results data frame
return(results)
}
count_zeros_in_rows <- function(mat) {
# Ensure the input is a matrix
if (!is.matrix(mat)) {
stop("Input must be a matrix.")
}
# Use rowSums to count zeros in each row
zero_counts <- rowSums(mat != 0)
return(zero_counts)
}df <- read.csv("data/omics_regulon_pairs.csv")
labels <- c("kirc", "kirc", "hnsc", "hnsc", "lusc", "lusc", "luad", "luad",
"paad", "paad")
for (i in c(1, 3, 5, 7, 9)) {
normal <- read_exp(df$RNA[i])
expr_n <- normal[[1]]
meta_n <- normal[[2]]
tumor <- read_exp(df$RNA[i+1])
expr_t <- tumor[[1]]
meta_t <- tumor[[2]]
common_terms <- intersect(rownames(expr_t), rownames(expr_n))
expr_t[!is.finite(as.matrix(expr_t))] <- 0
expr_n[!is.finite(as.matrix(expr_n))] <- 0
expr <- cbind(expr_t[common_terms, ], expr_n[common_terms, ])
saveRDS(expr_n[common_terms, ], paste("output/expr/count_matrix_", labels[i], "_normal.RDS",
sep = ""))
saveRDS(expr_t[common_terms, ], paste("output/expr/count_matrix_", labels[i], "_tumor.RDS",
sep = ""))
meta <- meta_n[common_terms, ]
fc <- calculate_log_fold_change_and_pvalue(data_matrix = as.matrix(expr),
group = c(rep("tumor", ncol(expr_t)), rep("normal", ncol(expr_n))))
fc <- cbind(meta, fc)
write.csv(fc, paste("output/DEG/", labels[i],
"_fc.csv", sep = ""), row.names = F)
fc <- fc[fc$Adjusted_P_Value < 0.05, ]
write.csv(fc, paste("output/DEG/", labels[i],
"_fc_0.05.csv", sep = ""), row.names = F)
}binary_unique_index
FALSE TRUE
2782 39778
binary_unique_index
FALSE TRUE
2790 39468
binary_unique_index
FALSE TRUE
2781 39771
binary_unique_index
FALSE TRUE
2920 40572
binary_unique_index
FALSE TRUE
2791 39829
binary_unique_index
FALSE TRUE
2929 40903
binary_unique_index
FALSE TRUE
2788 39628
binary_unique_index
FALSE TRUE
2864 40400
binary_unique_index
FALSE TRUE
2926 40861
binary_unique_index
FALSE TRUE
2857 40341
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.28.0
[3] AnnotationFilter_1.28.0 GenomicFeatures_1.56.0
[5] AnnotationDbi_1.66.0 GenomicRanges_1.56.0
[7] GenomeInfoDb_1.40.0 IRanges_2.38.0
[9] S4Vectors_0.42.0 stringr_1.5.1
[11] plyr_1.8.9 dplyr_1.1.4
[13] aracne.networks_1.30.0 viper_1.38.0
[15] Biobase_2.64.0 BiocGenerics_0.50.0
[17] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] bitops_1.0-7 DBI_1.2.3
[3] rlang_1.1.4 magrittr_2.0.3
[5] git2r_0.33.0 matrixStats_1.3.0
[7] e1071_1.7-14 compiler_4.4.0
[9] RSQLite_2.3.7 getPass_0.2-4
[11] png_0.1-8 callr_3.7.6
[13] vctrs_0.6.5 ProtGenerics_1.36.0
[15] pkgconfig_2.0.3 crayon_1.5.2
[17] fastmap_1.2.0 XVector_0.44.0
[19] utf8_1.2.4 Rsamtools_2.20.0
[21] promises_1.3.0 rmarkdown_2.27
[23] UCSC.utils_1.0.0 ps_1.7.6
[25] purrr_1.0.2 bit_4.0.5
[27] xfun_0.44 zlibbioc_1.50.0
[29] cachem_1.1.0 jsonlite_1.8.8
[31] blob_1.2.4 later_1.3.2
[33] DelayedArray_0.30.1 BiocParallel_1.38.0
[35] parallel_4.4.0 R6_2.5.1
[37] bslib_0.7.0 stringi_1.8.4
[39] rtracklayer_1.64.0 jquerylib_0.1.4
[41] SummarizedExperiment_1.34.0 Rcpp_1.0.12
[43] knitr_1.47 mixtools_2.0.0
[45] httpuv_1.6.15 Matrix_1.7-0
[47] splines_4.4.0 tidyselect_1.2.1
[49] abind_1.4-5 rstudioapi_0.16.0
[51] yaml_2.3.8 codetools_0.2-20
[53] curl_5.2.1 processx_3.8.4
[55] lattice_0.22-6 tibble_3.2.1
[57] KEGGREST_1.44.0 evaluate_0.24.0
[59] survival_3.7-0 proxy_0.4-27
[61] kernlab_0.9-32 Biostrings_2.72.1
[63] pillar_1.9.0 MatrixGenerics_1.16.0
[65] whisker_0.4.1 KernSmooth_2.23-24
[67] plotly_4.10.4 generics_0.1.3
[69] RCurl_1.98-1.14 rprojroot_2.0.4
[71] ggplot2_3.5.1 munsell_0.5.1
[73] scales_1.3.0 class_7.3-22
[75] glue_1.7.0 lazyeval_0.2.2
[77] tools_4.4.0 BiocIO_1.14.0
[79] data.table_1.15.4 GenomicAlignments_1.40.0
[81] XML_3.99-0.16.1 fs_1.6.4
[83] grid_4.4.0 tidyr_1.3.1
[85] colorspace_2.1-0 nlme_3.1-165
[87] GenomeInfoDbData_1.2.12 restfulr_0.0.15
[89] cli_3.6.2 fansi_1.0.6
[91] S4Arrays_1.4.1 segmented_2.1-0
[93] viridisLite_0.4.2 gtable_0.3.5
[95] sass_0.4.9 digest_0.6.35
[97] SparseArray_1.4.8 rjson_0.2.21
[99] htmlwidgets_1.6.4 memoise_2.0.1
[101] htmltools_0.5.8.1 lifecycle_1.0.4
[103] httr_1.4.7 bit64_4.0.5
[105] MASS_7.3-61