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Ignored files:
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Modified: output/cnv/dset_hnsc.RDS
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Modified: output/cnv/regul_kirc.RDS
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown
(analysis/Differentially_Methylation.Rmd) and HTML
(docs/Differentially_Methylation.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 85fc7b1 | Zhen Zuo | 2024-06-24 | . |
| html | 85fc7b1 | Zhen Zuo | 2024-06-24 | . |
library(viper)
library(aracne.networks)
library(dplyr)
library(plyr)
library(stringr)
library(Biobase)
library(EnsDb.Hsapiens.v86)
dir.create("output/DME/",showWarnings = FALSE)
dir.create("output/methy/",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$p.value
}
# Adjust the p-values
adjusted_p_values <- p.adjust(p_values, method = adjust_method)
# Create a data frame with fold change, p-values, and adjusted
# p-values
results <- data.frame(Fold_Change = fold_change, P_Value = p_values,
Adjusted_P_Value = adjusted_p_values)
# Return the results data frame
return(results)
}
count_zeros_in_rows <- function(mat) {
# Ensure the input is a matrix
if (!is.matrix(mat)) {
stop("Input must be a matrix.")
}
# Use rowSums to count zeros in each row
zero_counts <- rowSums(mat != 0)
return(zero_counts)
}df <- read.csv("data/omics_regulon_pairs.csv")
labels <- c("kirc", "kirc", "hnsc", "hnsc", "lusc", "lusc", "luad", "luad",
"paad", "paad")
for (i in c(1, 3, 5, 7, 9)) {
normal <- read_exp(df$methy[i])
expr_n <- normal[[1]]
meta_n <- normal[[2]]
tumor <- read_exp(df$methy[i+1])
expr_t <- tumor[[1]]
meta_t <- tumor[[2]]
common_terms <- intersect(rownames(expr_t), rownames(expr_n))
expr_t[!is.finite(as.matrix(expr_t))] <- 0
expr_n[!is.finite(as.matrix(expr_n))] <- 0
expr <- cbind(expr_t[common_terms, ], expr_n[common_terms, ])
saveRDS(expr_n[common_terms, ], paste("output/methy/count_matrix_", labels[i], "_normal.RDS",
sep = ""))
saveRDS(expr_t[common_terms, ], paste("output/methy/count_matrix_", labels[i], "_tumor.RDS",
sep = ""))
meta <- meta_n[common_terms, ]
fc <- calculate_log_fold_change_and_pvalue(data_matrix = as.matrix(expr),
group = c(rep("tumor", ncol(expr_t)), rep("normal", ncol(expr_n))))
fc <- cbind(meta, fc)
write.csv(fc, paste("output/DME/", labels[i],
"_fc.csv", sep = ""), row.names = F)
fc <- fc[(fc$Adjusted_P_Value < 0.05)&(abs(fc$Fold_Change)>0.1), ]
write.csv(fc, paste("output/DME/", labels[i],
"_fc_0.05.csv", sep = ""), row.names = F)
}binary_unique_index
FALSE TRUE
115 12841
binary_unique_index
FALSE TRUE
115 12843
binary_unique_index
FALSE TRUE
115 12842
binary_unique_index
FALSE TRUE
115 12843
binary_unique_index
FALSE TRUE
115 12842
binary_unique_index
FALSE TRUE
115 12842
binary_unique_index
FALSE TRUE
115 12840
binary_unique_index
FALSE TRUE
115 12843
binary_unique_index
FALSE TRUE
115 12842
binary_unique_index
FALSE TRUE
115 12843
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_1.56.1
[7] GenomeInfoDb_1.40.1 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
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 png_0.1-8
[11] vctrs_0.6.5 ProtGenerics_1.36.0
[13] pkgconfig_2.0.3 crayon_1.5.2
[15] fastmap_1.2.0 XVector_0.44.0
[17] utf8_1.2.4 Rsamtools_2.20.0
[19] promises_1.3.0 rmarkdown_2.27
[21] UCSC.utils_1.0.0 purrr_1.0.2
[23] bit_4.0.5 xfun_0.45
[25] zlibbioc_1.50.0 cachem_1.1.0
[27] jsonlite_1.8.8 blob_1.2.4
[29] later_1.3.2 DelayedArray_0.30.1
[31] BiocParallel_1.38.0 parallel_4.4.0
[33] R6_2.5.1 bslib_0.7.0
[35] stringi_1.8.4 rtracklayer_1.64.0
[37] jquerylib_0.1.4 SummarizedExperiment_1.34.0
[39] Rcpp_1.0.12 knitr_1.47
[41] mixtools_2.0.0 httpuv_1.6.15
[43] Matrix_1.7-0 splines_4.4.0
[45] tidyselect_1.2.1 abind_1.4-5
[47] rstudioapi_0.16.0 yaml_2.3.8
[49] codetools_0.2-20 curl_5.2.1
[51] lattice_0.22-6 tibble_3.2.1
[53] KEGGREST_1.44.0 evaluate_0.24.0
[55] survival_3.7-0 proxy_0.4-27
[57] kernlab_0.9-32 Biostrings_2.72.1
[59] pillar_1.9.0 MatrixGenerics_1.16.0
[61] whisker_0.4.1 KernSmooth_2.23-24
[63] plotly_4.10.4 generics_0.1.3
[65] rprojroot_2.0.4 RCurl_1.98-1.14
[67] ggplot2_3.5.1 munsell_0.5.1
[69] scales_1.3.0 class_7.3-22
[71] glue_1.7.0 lazyeval_0.2.2
[73] tools_4.4.0 BiocIO_1.14.0
[75] data.table_1.15.4 GenomicAlignments_1.40.0
[77] fs_1.6.4 XML_3.99-0.16.1
[79] grid_4.4.0 tidyr_1.3.1
[81] colorspace_2.1-0 nlme_3.1-165
[83] GenomeInfoDbData_1.2.12 restfulr_0.0.15
[85] cli_3.6.2 workflowr_1.7.1
[87] fansi_1.0.6 S4Arrays_1.4.1
[89] segmented_2.1-0 viridisLite_0.4.2
[91] gtable_0.3.5 sass_0.4.9
[93] digest_0.6.35 SparseArray_1.4.8
[95] rjson_0.2.21 htmlwidgets_1.6.4
[97] memoise_2.0.1 htmltools_0.5.8.1
[99] lifecycle_1.0.4 httr_1.4.7
[101] bit64_4.0.5 MASS_7.3-61