library(tidyverse)
library(ggrepel)
library(VIM)
library(mixOmics)peak_matrix_file <- "Galaxy126-[Sample_Filter_on_data_121__Peak_Intensity_Matrix].tsv"
meta_file <- "Galaxy90-[Replicate_Filter_on_data_2_and_data_4__Sample_Metadata_(updated)].tsv"
mat_raw <- read_tsv(peak_matrix_file, show_col_types = FALSE)
meta <- read_tsv(meta_file, show_col_types = FALSE)
head(glimpse(mat_raw))## Rows: 25
## Columns: 1,147
## $ mz <chr> "batch06_QC25_rep01_04_2_05_3_06", "batch06_C05_r…
## $ `76.86787943503444` <dbl> 8315.919, 11711.464, 8230.207, 0.000, 8841.073, 8…
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## $ `336.94868819015505` <dbl> 55392.89, 104687.92, 82677.41, 126899.70, 16272.5…
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## $ `543.1776895991757` <dbl> 4874.704, 2776.283, 5292.556, 0.000, 3509.253, 41…
## $ `543.2179899674767` <dbl> 3639.046, 0.000, 4882.419, 2772.110, 6382.421, 66…
## $ `543.9403326318977` <dbl> 3738.216, 4591.451, 3740.778, 3802.853, 0.000, 67…
## $ `544.0590265957504` <dbl> 2982.710, 0.000, 3502.837, 4128.429, 0.000, 0.000…
## $ `545.5012851237835` <dbl> 3483.907, 0.000, 8867.122, 0.000, 5513.355, 6998.…
## $ `546.305810055039` <dbl> 3099.598, 4111.367, 9341.890, 0.000, 0.000, 3649.…
## $ `547.430105531091` <dbl> 0.000, 0.000, 0.000, 0.000, 62559.419, 0.000, 0.0…
## $ `547.4595851951125` <dbl> 3462.913, 0.000, 12676.405, 0.000, 10399.914, 810…
## $ `548.275478544548` <dbl> 2855.986, 3795.473, 5278.802, 0.000, 8542.857, 31…
## $ `548.284964247226` <dbl> 54964.75, 78483.76, 124041.95, 36384.49, 129684.3…
## $ `548.99630266685` <dbl> 4767.146, 0.000, 8651.377, 11535.992, 0.000, 3476…
## $ `549.2884263168897` <dbl> 18681.78, 26656.42, 39772.40, 12225.57, 43565.71,…
## $ `550.102556823465` <dbl> 2857.240, 2712.189, 3255.469, 2593.319, 0.000, 51…
## $ `550.115081140873` <dbl> 2523.384, 3036.612, 3151.768, 0.000, 0.000, 4555.…
## $ `550.2917423639434` <dbl> 3239.037, 3798.306, 6100.539, 2685.688, 5831.693,…
## $ `550.3595723240502` <dbl> 5375.503, 2651.929, 22340.668, 0.000, 10807.071, …
## $ `551.0868120980826` <dbl> 3088.564, 2720.583, 4163.668, 3519.152, 0.000, 30…
## $ `551.3627503642372` <dbl> 0.000, 0.000, 6666.311, 0.000, 4051.572, 3218.825…
## $ `552.0041347454328` <dbl> 2963.722, 0.000, 2920.756, 3425.235, 0.000, 0.000…
## $ `552.0170380487993` <dbl> 0.000, 0.000, 3350.530, 3205.301, 0.000, 0.000, 2…
## $ `553.3518076474501` <dbl> 3437.745, 14171.210, 7303.808, 0.000, 37314.129, …
## $ `554.0769102314541` <dbl> 3281.991, 2721.231, 4094.996, 6784.129, 0.000, 10…
## $ `554.0895707682206` <dbl> 2477.291, 0.000, 3373.713, 4975.812, 0.000, 0.000…
## $ `554.2776013195332` <dbl> 2879.491, 0.000, 7791.753, 0.000, 0.000, 0.000, 3…
## $ `554.3550107558062` <dbl> 0.000, 5832.364, 4573.403, 0.000, 18354.684, 0.00…
## $ `555.3663391592627` <dbl> 9218.154, 48703.248, 19314.167, 0.000, 178485.406…
## $ `555.3744380657735` <dbl> 6186.273, 28926.151, 12240.391, 0.000, 95550.198,…
## $ `555.4856441170956` <dbl> 0.000, 0.000, 5917.642, 0.000, 4465.934, 4431.732…
## $ `556.2053438921907` <dbl> 4621.714, 4240.577, 5857.394, 3657.850, 4242.185,…
## $ `556.3696348562892` <dbl> 3348.997, 14588.541, 6947.253, 0.000, 47982.542, …
## $ `557.0723961255114` <dbl> 6204.816, 0.000, 12828.791, 18557.550, 0.000, 0.0…
## $ `557.0942642681964` <dbl> 18559.08, 19955.81, 18822.36, 20101.75, 15573.42,…
## $ `557.2501888676948` <dbl> 2628.722, 0.000, 4052.273, 0.000, 0.000, 0.000, 2…
## $ `558.0931207650247` <dbl> 5046.411, 5596.069, 5175.050, 5752.999, 4659.308,…
## $ `558.5045988428506` <dbl> 8501.551, 0.000, 36026.585, 3762.128, 16793.882, …
## $ `559.0915648487917` <dbl> 5100.179, 5401.871, 5017.905, 5432.582, 4271.976,…
## $ `559.1504539479708` <dbl> 13442.224, 5494.623, 19298.708, 24472.370, 5906.4…
## $ `559.9142063342342` <dbl> 2431.614, 2969.352, 2983.744, 3083.145, 0.000, 40…
## $ `560.0759890786048` <dbl> 2551.596, 0.000, 3400.759, 6398.933, 0.000, 0.000…
## $ `560.1534372635269` <dbl> 3102.500, 0.000, 4092.743, 4596.911, 0.000, 0.000…
## $ `560.5203231719321` <dbl> 5065.299, 0.000, 21817.729, 0.000, 9254.685, 1571…
## $ `560.9754758229` <dbl> 2828.852, 4697.037, 3134.052, 3232.631, 0.000, 35…
## $ `561.1519976110152` <dbl> 5030.964, 6669.943, 5730.080, 6768.779, 0.000, 64…
## $ `562.1407522699884` <dbl> 2390.523, 0.000, 2981.304, 4485.328, 0.000, 0.000…
## $ `564.2777400143912` <dbl> 2513.620, 2309.861, 6247.019, 0.000, 0.000, 4767.…
## $ `565.5572733972022` <dbl> 10712.748, 0.000, 16140.602, 0.000, 0.000, 11202.…
## $ `565.5654320135773` <dbl> 8542.491, 0.000, 9595.606, 0.000, 6655.097, 6262.…
## $ `569.3630768552416` <dbl> 23217.361, 177225.543, 41488.055, 8616.184, 31739…
## $ `570.3665969315762` <dbl> 7209.989, 50047.033, 12196.954, 3437.982, 87250.4…
## $ `570.9782540974344` <dbl> 3373.900, 0.000, 4678.903, 5412.061, 0.000, 3104.…
## $ `571.3617050376694` <dbl> 2355.076, 11467.316, 0.000, 0.000, 20239.297, 431…
## $ `574.3429900442869` <dbl> 2711.668, 0.000, 5137.386, 0.000, 0.000, 7966.234…## # A tibble: 6 × 1,147
## mz `76.86787943503444` `90.05490324022944` `97.00499768476807`
## <chr> <dbl> <dbl> <dbl>
## 1 batch06_QC25_rep0… 8316. 13022. 24287.
## 2 batch06_C05_rep01… 11711. 11892. 20719.
## 3 batch06_S03_rep01… 8230. 9217. 21252.
## 4 batch06_S07_rep01… 0 7469. 11523.
## 5 batch06_C01_rep01… 8841. 12439. 13834.
## 6 batch06_C10_rep01… 8105. 9319. 18420.
## # ℹ 1,143 more variables: `98.99513698833377` <dbl>, `98.99549673882467` <dbl>,
## # `101.7587871575044` <dbl>, `101.75927215863561` <dbl>,
## # `101.76718434133913` <dbl>, `101.76920075333308` <dbl>,
## # `101.79279205075335` <dbl>, `101.79694978102945` <dbl>,
## # `101.79807269090797` <dbl>, `101.80953588852107` <dbl>,
## # `104.10697163627759` <dbl>, `106.95056472920265` <dbl>,
## # `110.07126153961639` <dbl>, `112.03688820594972` <dbl>, …glimpse(meta)## Rows: 27
## Columns: 5
## $ filename <chr> "batch06_B05_rep01_01_2_02_3_03", "batch06_QC25_rep01_0…
## $ injectionOrder <dbl> 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43…
## $ replicates <chr> "1-2-3", "1-2-3", "1-2-3", "1-2-3", "1-2-3", "1-2-3", "…
## $ batch <dbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6…
## $ classLabel <chr> "blank", "QC", "cow", "sheep", "sheep", "cow", "cow", "…sample_col <- colnames(mat_raw)[1] # "mz"
mat_df <- as.data.frame(mat_raw)
rownames(mat_df) <- mat_df[[sample_col]]
mat_df[[sample_col]] <- NULL
mat_df[] <- lapply(mat_df, as.numeric)
X <- t(as.matrix(mat_df))
# Treat 0 as missing
X[X == 0] <- NA
cat("Matrix dims (features x samples):", dim(X), "\n")## Matrix dims (features x samples): 1146 25cat("Total NA:", sum(is.na(X)), "\n")## Total NA: 4480cat("NA proportion:", round(mean(is.na(X))*100, 2), "%\n")## NA proportion: 15.64 %common <- intersect(colnames(X), meta$filename)
X <- X[, common, drop=FALSE]
meta2 <- meta %>% filter(filename %in% common) %>% arrange(match(filename, common))
stopifnot(all(colnames(X) == meta2$filename))
meta_cs <- meta2 %>% filter(classLabel %in% c("cow", "sheep"))
X_cs <- X[, meta_cs$filename, drop=FALSE]
table(meta_cs$classLabel)##
## cow sheep
## 10 10dim(X_cs)## [1] 1146 20miss_sample <- tibble(
sample = colnames(X_cs),
missing_frac = colMeans(is.na(X_cs))
) %>% left_join(meta_cs, by=c("sample"="filename"))
miss_feature <- tibble(
feature = rownames(X_cs),
missing_frac = rowMeans(is.na(X_cs))
)
summary(miss_sample$missing_frac)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.09599 0.12216 0.15969 0.17692 0.23189 0.30977summary(miss_feature$missing_frac)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.1500 0.1769 0.3000 0.6000# Top missing samples/features
miss_sample %>% arrange(desc(missing_frac)) %>% head(10)## # A tibble: 10 × 6
## sample missing_frac injectionOrder replicates batch classLabel
## <chr> <dbl> <dbl> <chr> <dbl> <chr>
## 1 batch06_S07_rep01_13… 0.310 13 1-2-3 6 sheep
## 2 batch06_S10_rep01_70… 0.290 70 1-2-3 6 sheep
## 3 batch06_C04_rep01_67… 0.246 67 1-2-3 6 cow
## 4 batch06_C01_rep01_16… 0.244 16 1-2-3 6 cow
## 5 batch06_C05_rep01_07… 0.239 7 1-2-3 6 cow
## 6 batch06_S09_rep01_52… 0.229 52 1-2-3 6 sheep
## 7 batch06_C03_rep01_28… 0.198 28 1-2-3 6 cow
## 8 batch06_S01_rep01_25… 0.183 25 1-2-3 6 sheep
## 9 batch06_C07_rep01_34… 0.180 34 1-2-3 6 cow
## 10 batch06_C10_rep01_19… 0.169 19 1-2-3 6 cowmiss_feature %>% arrange(desc(missing_frac)) %>% head(10)## # A tibble: 10 × 2
## feature missing_frac
## <chr> <dbl>
## 1 137.0066236948302 0.6
## 2 159.07640651627975 0.6
## 3 173.04203025798344 0.6
## 4 178.97170553267614 0.6
## 5 201.0160012125499 0.6
## 6 203.18350691738746 0.6
## 7 213.12334937636044 0.6
## 8 226.08377078800046 0.6
## 9 321.07076433594557 0.6
## 10 342.3114121538319 0.6Implement two filtering strategies (this counts as part of “missing value handling”):
Strategy A (lenient): remove features with >50% missing, keep all samples
Strategy B (strict): remove features with >20% missing, and remove samples with >30% missing
filter_strategy_A <- function(X){
feat_miss <- rowMeans(is.na(X))
X2 <- X[feat_miss <= 0.5, , drop=FALSE]
X2
}
filter_strategy_B <- function(X){
feat_miss <- rowMeans(is.na(X))
X2 <- X[feat_miss <= 0.2, , drop=FALSE]
samp_miss <- colMeans(is.na(X2))
X3 <- X2[, samp_miss <= 0.3, drop=FALSE]
X3
}
XA <- filter_strategy_A(X_cs)
XB <- filter_strategy_B(X_cs)
cat("Strategy A dims:", dim(XA), "\n")## Strategy A dims: 1090 20cat("Strategy B dims:", dim(XB), "\n")## Strategy B dims: 756 20cat("Strategy A NA%:", round(mean(is.na(XA))*100,2), "%\n")## Strategy A NA%: 15.67 %cat("Strategy B NA%:", round(mean(is.na(XB))*100,2), "%\n")## Strategy B NA%: 5.95 %log_transform <- function(x) log10(x + 1)
# TIC (total ion current / total sum scaling)
norm_tic <- function(x) sweep(x, 2, colSums(x, na.rm=TRUE), FUN="/")
# PQN
norm_pqn <- function(x) {
ref <- apply(x, 1, median, na.rm=TRUE)
quot <- sweep(x, 1, ref, FUN="/")
factors <- apply(quot, 2, median, na.rm=TRUE)
sweep(x, 2, factors, FUN="/")
}
# Small value replacement (half-min per feature)
imp_halfmin <- function(x) {
x2 <- x
for (i in 1:nrow(x2)) {
v <- x2[i, ]
mn <- min(v, na.rm=TRUE)
v[is.na(v)] <- mn/2
x2[i, ] <- v
}
x2
}
# kNN imputation
imp_knn <- function(x, k=5) {
df <- as.data.frame(t(x)) # rows=samples, cols=features
df_imp <- VIM::kNN(df, k=k, imp_var=FALSE)
x_imp <- t(as.matrix(df_imp)) # rows=features
rownames(x_imp) <- rownames(x)
colnames(x_imp) <- colnames(x)
x_imp
}TIC + Half-min
TIC + kNN
PQN + Half-min
PQN + kNN
build_pipelines <- function(X0){
out <- list()
tmp <- norm_tic(X0) %>% imp_halfmin() %>% log_transform()
out[["TIC_HalfMin"]] <- tmp
tmp <- norm_tic(X0) %>% imp_knn(k=5) %>% log_transform()
out[["TIC_kNN"]] <- tmp
tmp <- norm_pqn(X0) %>% imp_halfmin() %>% log_transform()
out[["PQN_HalfMin"]] <- tmp
tmp <- norm_pqn(X0) %>% imp_knn(k=5) %>% log_transform()
out[["PQN_kNN"]] <- tmp
out
}
pipA <- build_pipelines(XA)
pipB <- build_pipelines(XB)
names(pipA)## [1] "TIC_HalfMin" "TIC_kNN" "PQN_HalfMin" "PQN_kNN"run_pca <- function(X, meta_df, title){
# meta must match samples available in X
meta_sub <- meta_df %>% filter(filename %in% colnames(X)) %>%
arrange(match(filename, colnames(X)))
pca <- prcomp(t(X), center=TRUE, scale.=TRUE)
var <- (pca$sdev^2)/sum(pca$sdev^2)
scores <- as.data.frame(pca$x[,1:2])
scores$sample <- rownames(scores)
scores <- scores %>% left_join(meta_sub, by=c("sample"="filename"))
g <- ggplot(scores, aes(PC1, PC2, color=classLabel)) +
geom_point(size=3) +
labs(
title = title,
subtitle = paste0("Explained variance: PC1=", round(var[1]*100,1), "%, PC2=", round(var[2]*100,1), "%")
) +
theme_minimal()
list(var=var, plot=g)
}
plot_all_pca_save <- function(pips, meta_df, tag, outdir="PCA_plots"){
dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
for(nm in names(pips)){
res <- run_pca(pips[[nm]], meta_df, paste("PCA –", tag, "–", nm))
outfile <- file.path(outdir, paste0("PCA_", tag, "_", nm, ".png"))
# 안전한 PNG 디바이스: Cairo가 있으면 더 안정적
# install.packages("Cairo") 필요할 수 있음
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPNG(filename = outfile, width = 1200, height = 900, res = 150)
print(res$plot)
dev.off()
} else {
png(filename = outfile, width = 1200, height = 900, res = 150)
print(res$plot)
dev.off()
}
cat("Saved:", outfile, "\n")
}
}
plot_all_pca_save(pipA, meta_cs, "StrategyA")## Saved: PCA_plots/PCA_StrategyA_TIC_HalfMin.png## Saved: PCA_plots/PCA_StrategyA_TIC_kNN.png## Saved: PCA_plots/PCA_StrategyA_PQN_HalfMin.png## Saved: PCA_plots/PCA_StrategyA_PQN_kNN.pngplot_all_pca_save(pipB, meta_cs, "StrategyB")## Saved: PCA_plots/PCA_StrategyB_TIC_HalfMin.png## Saved: PCA_plots/PCA_StrategyB_TIC_kNN.png## Saved: PCA_plots/PCA_StrategyB_PQN_HalfMin.png## Saved: PCA_plots/PCA_StrategyB_PQN_kNN.pngplot_all_plsda_save <- function(pips, meta_df, tag, outdir="PLSDA_plots"){
dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
for(nm in names(pips)){
X <- pips[[nm]]
# meta 정렬
meta_sub <- meta_df %>%
filter(filename %in% colnames(X)) %>%
arrange(match(filename, colnames(X)))
Y <- factor(meta_sub$classLabel)
# 모델
pls <- mixOmics::plsda(t(X), Y, ncomp=2)
# 파일 저장
outfile <- file.path(outdir, paste0("PLSDA_", tag, "_", nm, ".png"))
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPNG(filename = outfile, width = 1200, height = 900, res = 150)
mixOmics::plotIndiv(pls, comp=c(1,2), group=Y, ind.names=FALSE,
title=paste("PLS-DA –", tag, "–", nm),
legend=TRUE)
dev.off()
} else {
png(filename = outfile, width = 1200, height = 900, res = 150)
mixOmics::plotIndiv(pls, comp=c(1,2), group=Y, ind.names=FALSE,
title=paste("PLS-DA –", tag, "–", nm),
legend=TRUE)
dev.off()
}
cat("Saved:", outfile, "\n")
}
}
# 실행
plot_all_plsda_save(pipA, meta_cs, "StrategyA")## Saved: PLSDA_plots/PLSDA_StrategyA_TIC_HalfMin.png## Saved: PLSDA_plots/PLSDA_StrategyA_TIC_kNN.png## Saved: PLSDA_plots/PLSDA_StrategyA_PQN_HalfMin.png## Saved: PLSDA_plots/PLSDA_StrategyA_PQN_kNN.pngplot_all_plsda_save(pipB, meta_cs, "StrategyB")## Saved: PLSDA_plots/PLSDA_StrategyB_TIC_HalfMin.png## Saved: PLSDA_plots/PLSDA_StrategyB_TIC_kNN.png## Saved: PLSDA_plots/PLSDA_StrategyB_PQN_HalfMin.png## Saved: PLSDA_plots/PLSDA_StrategyB_PQN_kNN.pngunivariate_ttest <- function(X, meta_df){
meta_sub <- meta_df %>%
filter(filename %in% colnames(X)) %>%
arrange(match(filename, colnames(X)))
g1 <- meta_sub$filename[meta_sub$classLabel == "cow"]
g2 <- meta_sub$filename[meta_sub$classLabel == "sheep"]
logFC <- rowMeans(X[, g1, drop=FALSE]) - rowMeans(X[, g2, drop=FALSE])
pvals <- apply(X, 1, function(v) t.test(v[g1], v[g2])$p.value)
tibble(
feature = rownames(X),
logFC = logFC,
pvalue = pvals,
FDR = p.adjust(pvals, method="BH")
)
}
plot_volcano <- function(res, title, fdr_cut=0.05){
res2 <- res %>% mutate(sig = FDR < fdr_cut)
ggplot(res2, aes(logFC, -log10(pvalue), color=sig)) +
geom_point(alpha=0.7) +
labs(
title=title,
subtitle=paste0("Significant (FDR<", fdr_cut, "): ", sum(res2$sig))
) +
theme_minimal()
}
plot_all_volcano_save <- function(pips, meta_df, tag, outdir="Volcano_plots"){
dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
for(nm in names(pips)){
X <- pips[[nm]]
res <- univariate_ttest(X, meta_df)
# CSV 저장
out_csv <- file.path(outdir, paste0("Univariate_", tag, "_", nm, ".csv"))
write_csv(res, out_csv)
# PNG 저장
out_png <- file.path(outdir, paste0("Volcano_", tag, "_", nm, ".png"))
g <- plot_volcano(res, paste("Volcano –", tag, "–", nm))
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPNG(filename = out_png, width = 1200, height = 900, res = 150)
print(g)
dev.off()
} else {
png(filename = out_png, width = 1200, height = 900, res = 150)
print(g)
dev.off()
}
cat("Saved:", out_csv, "\n")
cat("Saved:", out_png, "\n")
}
}
# 실행
plot_all_volcano_save(pipA, meta_cs, "StrategyA")## Saved: Volcano_plots/Univariate_StrategyA_TIC_HalfMin.csv
## Saved: Volcano_plots/Volcano_StrategyA_TIC_HalfMin.png## Saved: Volcano_plots/Univariate_StrategyA_TIC_kNN.csv
## Saved: Volcano_plots/Volcano_StrategyA_TIC_kNN.png## Saved: Volcano_plots/Univariate_StrategyA_PQN_HalfMin.csv
## Saved: Volcano_plots/Volcano_StrategyA_PQN_HalfMin.png## Saved: Volcano_plots/Univariate_StrategyA_PQN_kNN.csv
## Saved: Volcano_plots/Volcano_StrategyA_PQN_kNN.pngplot_all_volcano_save(pipB, meta_cs, "StrategyB")## Saved: Volcano_plots/Univariate_StrategyB_TIC_HalfMin.csv
## Saved: Volcano_plots/Volcano_StrategyB_TIC_HalfMin.png## Saved: Volcano_plots/Univariate_StrategyB_TIC_kNN.csv
## Saved: Volcano_plots/Volcano_StrategyB_TIC_kNN.png## Saved: Volcano_plots/Univariate_StrategyB_PQN_HalfMin.csv
## Saved: Volcano_plots/Volcano_StrategyB_PQN_HalfMin.png## Saved: Volcano_plots/Univariate_StrategyB_PQN_kNN.csv
## Saved: Volcano_plots/Volcano_StrategyB_PQN_kNN.pngget_pca_var <- function(X){
p <- prcomp(t(X), center=TRUE, scale.=TRUE)
var <- (p$sdev^2)/sum(p$sdev^2)
c(PC1=var[1], PC2=var[2])
}
make_impact_summary <- function(pips, meta_df, tag, outdir="Summary"){
dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
# (주의) univariate 결과는 새로 계산
rows <- lapply(names(pips), function(nm){
X <- pips[[nm]]
uv <- univariate_ttest(X, meta_df)
sig <- sum(uv$FDR < 0.05)
pv <- get_pca_var(X)
tibble(
pipeline = nm,
PC1 = round(pv["PC1"]*100, 1),
PC2 = round(pv["PC2"]*100, 1),
sig_FDR05 = sig
)
})
summary_tbl <- bind_rows(rows) %>% arrange(pipeline)
# CSV 저장
out_csv <- file.path(outdir, paste0("impact_summary_", tag, ".csv"))
write_csv(summary_tbl, out_csv)
# PNG 저장 (텍스트 테이블)
out_png <- file.path(outdir, paste0("impact_summary_", tag, ".png"))
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPNG(filename = out_png, width = 1400, height = 600, res = 150)
} else {
png(filename = out_png, width = 1400, height = 600, res = 150)
}
par(mar=c(1,1,1,1))
plot.new()
text(0, 1, paste(capture.output(print(summary_tbl)), collapse="\n"),
adj=c(0,1), family="mono")
dev.off()
cat("Saved:", out_csv, "\n")
cat("Saved:", out_png, "\n")
summary_tbl
}
# 실행
sumA <- make_impact_summary(pipA, meta_cs, "StrategyA")## Saved: Summary/impact_summary_StrategyA.csv
## Saved: Summary/impact_summary_StrategyA.pngsumB <- make_impact_summary(pipB, meta_cs, "StrategyB")## Saved: Summary/impact_summary_StrategyB.csv
## Saved: Summary/impact_summary_StrategyB.png# 합친 표 필요하면:
impact_all <- bind_rows(
sumA %>% mutate(strategy="A"),
sumB %>% mutate(strategy="B")
)
impact_all## # A tibble: 8 × 5
## pipeline PC1 PC2 sig_FDR05 strategy
## <chr> <dbl> <dbl> <int> <chr>
## 1 PQN_HalfMin 32.3 15.5 322 A
## 2 PQN_kNN 25.9 12.9 229 A
## 3 TIC_HalfMin 32.9 13.9 309 A
## 4 TIC_kNN 27.9 14.1 165 A
## 5 PQN_HalfMin 35.3 13.1 162 B
## 6 PQN_kNN 28.3 13.5 165 B
## 7 TIC_HalfMin 35.2 12.7 155 B
## 8 TIC_kNN 29.7 14.1 131 Bsave_interpretation_notes <- function(tag, outdir="Summary"){
dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
notes <- c(
"Interpretation guidance:",
"1) Compare PCA separation between cow and sheep across pipelines, and across Strategy A vs Strategy B.",
"2) Evaluate whether kNN vs half-min imputation changes clustering (PCA/PLS-DA) and significant features (volcano/univariate).",
"3) Assumptions:",
" - Half-min (small value replacement) assumes missingness is due to low abundance below detection limit.",
" - kNN assumes missingness can be inferred from similarity among samples/features.",
"4) Trade-off of strict filtering:",
" - Strategy A retains more features but may keep noisier missing data.",
" - Strategy B removes high-missing features/samples, potentially improving robustness but reducing coverage."
)
out_txt <- file.path(outdir, paste0("interpretation_notes_", tag, ".txt"))
writeLines(notes, out_txt)
cat("Saved:", out_txt, "\n")
}
# 실행
save_interpretation_notes("StrategyA")## Saved: Summary/interpretation_notes_StrategyA.txtsave_interpretation_notes("StrategyB")## Saved: Summary/interpretation_notes_StrategyB.txtlibrary(dplyr)
uv_results <- univariate_ttest(pipB[["PQN_kNN"]], meta_cs)
fdr_cutoff <- 0.05
sig <- uv_results %>%
mutate(mz = as.numeric(feature)) %>%
filter(!is.na(mz)) %>%
filter(FDR <= fdr_cutoff) %>%
arrange(FDR)
hmdb_lines <- sig %>%
distinct(mz) %>%
mutate(line = format(mz, scientific = FALSE, trim = TRUE)) %>%
pull(line)
# 1) txt 파일로 저장 (복붙용)
writeLines(hmdb_lines, "HMDB_query_mz_only.txt")
# 2) 콘솔 출력 (그대로 복붙)
cat(paste(hmdb_lines, collapse = "\n"))## 214.05884
## 456.27301
## 161.12842
## 175.00166
## 467.97096
## 445.98899
## 461.96286
## 170.04235
## 483.94480
## 527.07025
## 457.04298
## 179.12845
## 207.99828
## 533.02253
## 156.04054
## 170.09239
## 395.32393
## 146.11821
## 235.16496
## 499.91864
## 246.15267
## 146.11616
## 245.14734
## 245.14928
## 147.12094
## 328.05463
## 559.15045
## 118.08534
## 146.11759
## 268.05546
## 416.08429
## 223.97222
## 294.06007
## 119.08961
## 241.12922
## 148.00455
## 146.11676
## 245.14596
## 390.97765
## 112.05188
## 163.97606
## 166.94975
## 187.12591
## 470.01386
## 310.04699
## 165.97361
## 357.24958
## 165.97593
## 260.19657
## 196.87787
## 213.07484
## 307.03878
## 166.95072
## 148.00246
## 511.04046
## 163.97778
## 332.02882
## 163.97686
## 454.04003
## 258.10981
## 148.00390
## 212.85182
## 147.11284
## 149.99972
## 196.96971
## 400.15718
## 112.05265
## 203.93184
## 369.98448
## 148.06110
## 158.92199
## 185.95977
## 393.35842
## 328.06718
## 174.89594
## 238.02275
## 98.99550
## 148.00308
## 191.03165
## 123.05529
## 189.01600
## 146.16521
## 133.09717
## 148.05901
## 203.03166
## 149.06383
## 168.94884
## 308.04720
## 104.10697
## 201.93371
## 201.93234
## 253.09461
## 307.04373
## 309.04198
## 453.11260
## 404.32672
## 148.05962
## 456.16731
## 505.98398
## 385.95841
## 234.93787
## 406.95160
## 401.16054
## 179.98224
## 381.99681
## 399.14416
## 164.98116
## 213.00090
## 257.09112
## 276.18021
## 543.21799
## 397.34200
## 148.06046
## 175.11895
## 203.15026
## 437.13861
## 112.08691
## 138.05256
## 338.98533
## 231.14494
## 304.21171
## 526.24078
## 373.03779
## 192.10912
## 262.99013
## 530.21254
## 118.08666
## 296.06591
## 98.99514
## 389.17925
## 385.34214
## 320.97474
## 118.08570
## 106.95056
## 280.09170
## 140.06874
## 132.10077
## 157.04548
## 245.96796
## 258.07354
## 274.20094
## 176.89405
## 166.08624
## 317.02917
## 413.34827
## 313.94729
## 336.94869
## 521.95820
## 133.10527
## 175.11788
## 308.04091
## 321.89248
## 532.29017
## 118.08625
## 122.92452
## 236.99220
## 381.08881
## 373.24424
## 185.03231
## 222.02924
## 112.05230
## 331.04010
## 156.07674
## 142.02642
## 297.06927