Version Compatibility

Reference examples tested with: MSnbase 2.28+, ggplot2 3.5+, limma 3.58+, DEqMS 1.20+, ashr 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Proteomics Pipeline

"Process my proteomics data from raw MS files to differential abundance" → Orchestrate data import (pyopenms/MaxQuant), QC assessment, protein quantification, normalization, differential abundance testing (limma/DEqMS, or MSstats for feature-level designs), and PTM analysis.

Pipeline Overview

Raw MS Data (mzML) ──> MaxQuant/DIA-NN ──> proteinGroups.txt
                                                 │
                                                 ▼
                    ┌────────────────────────────────────────────┐
                    │           proteomics-pipeline              │
                    ├────────────────────────────────────────────┤
                    │  1. Data Import & Filtering                │
                    │  2. Log2 Transform & Normalization         │
                    │  3. Missing Value Imputation               │
                    │  4. QC: PCA, Correlation                   │
                    │  5. Differential Abundance (limma/MSstats) │
                    │  6. Visualization & Export                 │
                    └────────────────────────────────────────────┘
                                                 │
                                                 ▼
                          Differential Proteins + Volcano Plots

Complete R Workflow

library(limma)
library(ggplot2)
library(pheatmap)

# === 1. DATA IMPORT ===
proteins <- read.delim('proteinGroups.txt', stringsAsFactors = FALSE)
cat('Loaded', nrow(proteins), 'protein groups\n')

# Filter contaminants, reverse, only-by-site
proteins <- proteins[proteins$Potential.contaminant != '+' &
                      proteins$Reverse != '+' &
                      proteins$Only.identified.by.site != '+', ]
cat('After filtering:', nrow(proteins), 'proteins\n')

# Extract LFQ intensities
lfq_cols <- grep('^LFQ\\.intensity\\.', colnames(proteins), value = TRUE)
intensities <- proteins[, lfq_cols]
rownames(intensities) <- proteins$Majority.protein.IDs
colnames(intensities) <- gsub('LFQ\\.intensity\\.', '', colnames(intensities))

# === 2. LOG2 TRANSFORM & NORMALIZE ===
intensities[intensities == 0] <- NA
log2_int <- log2(intensities)

# Median centering
sample_medians <- apply(log2_int, 2, median, na.rm = TRUE)
global_median <- median(sample_medians)
normalized <- sweep(log2_int, 2, sample_medians - global_median)

# === 3. FILTER & IMPUTE ===
# Keep proteins with < 50% missing
valid_rows <- rowSums(is.na(normalized)) < ncol(normalized) * 0.5
filtered <- normalized[valid_rows, ]
cat('Proteins after filtering:', nrow(filtered), '\n')

# MinProb imputation (left-censored)
impute_minprob <- function(x) {
    nas <- is.na(x)
    if (all(nas)) return(x)
    x[nas] <- rnorm(sum(nas), mean = mean(x, na.rm = TRUE) - 1.8 * sd(x, na.rm = TRUE),
                    sd = 0.3 * sd(x, na.rm = TRUE))
    x
}
imputed <- as.data.frame(t(apply(filtered, 1, impute_minprob)))

# === 4. QC ===
# PCA
pca <- prcomp(t(imputed), scale. = TRUE)
pca_df <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], Sample = rownames(pca$x))

# === 5. DIFFERENTIAL ANALYSIS ===
# Load sample annotation (columns: sample, condition)
sample_info <- read.csv('sample_annotation.csv')
sample_info$condition <- factor(sample_info$condition)

design <- model.matrix(~ 0 + condition, data = sample_info)
colnames(design) <- levels(sample_info$condition)

fit <- lmFit(as.matrix(imputed), design)
contrast <- makeContrasts(Treatment - Control, levels = design)
fit2 <- contrasts.fit(fit, contrast)
fit2 <- eBayes(fit2, trend = TRUE, robust = TRUE)

results <- topTable(fit2, coef = 1, number = Inf, adjust.method = 'BH')
results$protein <- rownames(results)
results$significant <- abs(results$logFC) > 1 & results$adj.P.Val < 0.05

# === 6. OUTPUT ===
cat('\nResults:\n')
cat('  Significant proteins:', sum(results$significant), '\n')
cat('  Up-regulated:', sum(results$significant & results$logFC > 0), '\n')
cat('  Down-regulated:', sum(results$significant & results$logFC < 0), '\n')

write.csv(results, 'differential_proteins.csv', row.names = FALSE)

MSstats Workflow

library(MSstats)

# From MaxQuant
evidence <- read.table('evidence.txt', sep = '\t', header = TRUE)
proteinGroups <- read.table('proteinGroups.txt', sep = '\t', header = TRUE)
annotation <- read.csv('annotation.csv')

# Convert to MSstats format
msstats_input <- MaxQtoMSstatsFormat(evidence = evidence,
                                      proteinGroups = proteinGroups,
                                      annotation = annotation)

# Process data
processed <- dataProcess(msstats_input, normalization = 'equalizeMedians',
                         summaryMethod = 'TMP', censoredInt = 'NA')

# Comparison
comparison <- matrix(c(1, -1), nrow = 1)
rownames(comparison) <- 'Treatment_vs_Control'
colnames(comparison) <- c('Control', 'Treatment')

results <- groupComparison(contrast.matrix = comparison, data = processed)

QC Checkpoints

Workflow Variants

TMT/iTRAQ Isobaric Labeling

library(MSnbase)

# Load TMT data
tmt_data <- readMSnSet('tmt_psms.txt')

# Normalize with reference channel
tmt_norm <- normalize(tmt_data, method = 'center.median')

# Summarize to protein level
protein_data <- combineFeatures(tmt_norm, groupBy = fData(tmt_norm)$protein, fun = 'median')

# Then proceed with limma as above

SILAC Workflow

# SILAC ratios from MaxQuant
silac <- read.delim('proteinGroups.txt')
ratio_cols <- grep('Ratio.H.L.normalized', colnames(silac), value = TRUE)

# Log2 transform ratios
silac_log2 <- log2(silac[, ratio_cols])

# One-sample t-test against 0 (no change)
results <- apply(silac_log2, 1, function(x) t.test(x, mu = 0)$p.value)

DIA-NN Workflow

# Load DIA-NN report
diann <- read.delim('report.tsv')

# Pivot to matrix
library(tidyr)
protein_matrix <- diann %>%
    select(Protein.Group, Run, PG.MaxLFQ) %>%
    pivot_wider(names_from = Run, values_from = PG.MaxLFQ)

# Then proceed with normalization and limma

Related Skills

• proteomics/data-import - Load MS data formats
• proteomics/proteomics-qc - Quality control before analysis
• proteomics/quantification - Normalization methods
• proteomics/differential-abundance - Statistical testing details
• proteomics/ptm-analysis - Phosphoproteomics and other PTMs
• data-visualization/volcano-and-ma-plots - Volcano plots with LFC shrinkage