How to Analyze Phosphoproteomics Data with R and Bioinformatics Tools

Phosphoproteomics, the field focused on protein phosphorylation, is essential for deciphering cellular signaling and regulatory pathways. Computational biologists analyzing such data demand specialized approaches to manage its scale and complexity, extracting meaningful mechanistic insights. This paper details a systematic methodology employing R and bioinformatics resources for phosphoproteomic analysis, emphasizing critical techniques, relevant software packages, and efficient workflows.

Applicable tools: R (4.0+), Bioconductor, Perseus, Python (optional).

Input data: quantitative matrix of phosphorylation sites (Phospho (STY)Sites.txt) output by MaxQuant/DIA-NN.

1. Data preprocessing and quality control

1.1 Data Loading and Filtering

r

library(tidyverse)

library(phosphonets)

# Load MaxQuant/DIA-NN output files

phospho_data <- read_tsv("Phospho_(STY)Sites.txt",

col_types = cols(.default = "c",

Intensity = "d",

Localization.prob = "d"))

# Strict filtering criteria

filtered_data <- phospho_data %>%

filter(Localization.prob >= 0.75) %>% # Retain high-confidence sites

filter(!grepl("REV_|CON__", Proteins)) %>% # Remove reverse database hits and contaminants

filter(Score > 40) %>% # Threshold for mass spectrometry identification score

select(Protein, Position, Amino.acid,

contains("Intensity"), Sequence)

1.2 Missing Value Imputation Strategies

1.3 Batch Effect Correction

r

library(sva)

# Create design matrix

design <- model.matrix(~ group) # 'group' represents experimental conditions

# ComBat correction

corrected_data <- ComBat(

dat = as.matrix(log2(phospho_matrix)),

batch = batch_vector, # Batch information vector

mod = design,

par.prior = TRUE

)

2. Differential Phosphorylation Analysis

2.1 Limma Differential Analysis

r

library(limma)

# Create contrast matrix

contrast.matrix <- makeContrasts(

Tumor_vs_Normal = Tumor - Normal,

levels = design

)

# Linear model fitting

fit <- lmFit(corrected_data, design) %>%

contrasts.fit(contrast.matrix) %>%

eBayes()

# Extract results

diff_results <- topTable(

fit,

coef = "Tumor_vs_Normal",

number = Inf,

p.value = 0.05,

lfc = 1 # log2 fold-change threshold

)

2.2 Phosphorylation Site-Specific Analysis

r

# Extract phosphosite context sequences

diff_results <- diff_results %>%

mutate(

motif_15mer = str_sub(Sequence, start = -7, end = 7),

site_id = paste0(Gene.names, "_", Amino.acid, Position)

)

# Kinase-substrate enrichment analysis

library(PhosR)

kinase_enrich <- kinaseSubstrateEnrichment(

diff_results$site_id,

species = "human",

method = "Fisher"

)

Analysis of dysregulated phosphosites in RASopathy interactome proteins in NS, NSML (Montero-Bullón JF et al., 2021)

3. Functional Annotation and Pathway Enrichment

3.1 Kinase Activity Prediction

r

# Calculate kinase activity using phosphosite data

kinase_activity <- computeKinaseActivity(

psite_data = diff_results,

species = "human",

id_type = "site_id",

method = "linear"

)

# Filter significant activity kinases

significant_kinases <- kinase_activity %>%

filter(p.adjust < 0.05, abs(activity_score) > 1.5)

3.2 Pathway Enrichment Analysis

r

library(clusterProfiler)

library(org.Hs.eg.db)

# Gene ID conversion

entrez_ids <- bitr(

diff_results$Gene.names,

fromType = "SYMBOL",

toType = "ENTREZID",

OrgDb = org.Hs.eg.db

)

# KEGG pathway enrichment

kegg_result <- enrichKEGG(

gene = entrez_ids$ENTREZID,

organism = "hsa",

pvalueCutoff = 0.05,

pAdjustMethod = "BH"

)

# Visualization

dotplot(kegg_result, showCategory=15, font.size=8)

4. Advanced Analysis Techniques

4.1 Phosphorylation Signaling Network Modeling

r

library(igraph)

# Build kinase-substrate interaction network

kinase_substrate_net <- kinaseSubstrateNetwork(

kinase_activity = significant_kinases,

substrate_data = diff_results,

correlation_threshold = 0.7

)

# Network topology analysis

network_metrics <- data.frame(

degree = degree(kinase_substrate_net),

betweenness = betweenness(kinase_substrate_net)

)

# Export to Cytoscape format

write_graph(kinase_substrate_net, "kinase_network.graphml", format = "graphml")

Enrichment and signal network construction (Bortel P et al., 2024)

4.2 Multi-omics Integrative Analysis

r

library(MOFA2)

# Create multi-omics object

mofa_object <- create_mofa(

data = list(

phosphoproteomics = t(phospho_matrix),

transcriptomics = t(rna_matrix),

proteomics = t(protein_matrix)

),

groups = sample_groups

)

# Model training

model <- run_mofa(

mofa_object,

n_factors = 10,

convergence_mode = "slow"

)

# Factor-feature association visualization

plot_weights(model, view = "phosphoproteomics", nfeatures = 10)

5. Visualization Techniques

5.1 Phosphorylation Heatmap

r

library(ComplexHeatmap)

library(circlize)

# Create annotation

ha <- HeatmapAnnotation(

Group = sample_groups,

col = list(Group = c("Tumor" = "red", "Normal" = "blue"))

)

# Generate heatmap

Heatmap(

as.matrix(diff_phospho_matrix),

name = "log2(Intensity)",

col = colorRamp2(c(-2, 0, 2), c("blue", "white", "red")),

top_annotation = ha,

show_row_names = FALSE,

row_km = 4, # Row clustering

column_km = 2 # Column clustering

)

5.2 Kinase Activity Bubble Plot

r

ggplot(significant_kinases,

aes(x = reorder(Kinase, activity_score),

y = activity_score)) +

geom_point(aes(size = -log10(p.value), color = Regulation)) +

scale_color_manual(values = c(down = "blue", up = "red")) +

coord_flip() +

labs(x = "Kinase", y = "Activity Score",

title = "Differential Kinase Activity") +

theme_minimal()

Bubble diagram of phosphorylation site of protein (Montero-Bullón JF et al., 2021)

6. Reproducibility Assurance

6.1 Docker Container Configuration

dockerfile

# Dockerfile

FROM rocker/rstudio:4.2.0

RUN apt-get update && apt-get install -y \

libxml2-dev \

libssl-dev \

libcurl4-openssl-dev

RUN R -e "install.packages(c('tidyverse', 'limma', 'phosphonets', 'clusterProfiler'))"

RUN R -e "BiocManager::install(c('org.Hs.eg.db', 'MOFA2'))"

COPY analysis_script.R /home/rstudio/

WORKDIR /home/rstudio

6.2 Snakemake Workflow Management

python

# Snakefile

rule all:

input: "results/final_report.html"

rule preprocess:

input: "data/Phospho_STY.txt"

output: "processed/filtered_data.rds"

script: "scripts/01_preprocessing.R"

rule diff_analysis:

input: "processed/filtered_data.rds"

output: "results/diff_phospho.csv"

script: "scripts/02_diff_analysis.R"

rule generate_report:

input: "results/diff_phospho.csv"

output: "results/final_report.html"

script: "scripts/03_report.Rmd"

7. Clinical Translation Applications

7.1 Biomarker Discovery Pipeline

r

# Machine learning for prognostic biomarker screening

library(caret)

library(glmnet)

# Prepare data

X <- t(phospho_matrix)

Y <- clinical_data$survival_status

# LASSO regression

cv_model <- cv.glmnet(X, Y, family = "binomial", alpha = 1)

coefs <- coef(cv_model, s = "lambda.min")

biomarkers <- rownames(coefs)[which(coefs != 0)]

# Survival analysis validation

library(survival)

surv_fit <- survfit(Surv(time, status) ~ biomarker_group, data = clin_phospho)

ggsurvplot(surv_fit, risk.table = TRUE, pval = TRUE)

7.2 Drug Sensitivity Prediction

r

# Predicting drug response using phosphorylation profiles

library(pRRophetic)

predicted_response <- pRRopheticPredict(

testMatrix = phospho_matrix,

drug = "Pazopanib",

tissueType = "all",

batchCorrect = "standardize"

)

8. Resources and Toolkits

8.1 Core R Package List

8.2 Public Dataset Resources

8.3 Recommended Computing Platforms

9. Key Considerations and Supplementary Content

9.1 Deep Validation of Phosphosite Localization

• Challenge: High localization probability (≥0.75) may still yield false positives
• Solution: Multi-evidence integration

r

# Multi-evidence validation

validated_sites <- diff_results %>%

filter(Localization.prob >= 0.75 &

Delta.score >= 8 & # MaxQuant Delta Score threshold

PEP < 0.01) # Posterior Error Probability

• Orthogonal Validation Recommendations:
  • Use PhosR::siteResidueMapping() for PhosphoSitePlus database matching
  • Perform PRM (Parallel Reaction Monitoring) validation for top 20 sites

9.2 Association Analysis: Phosphorylation vs. Protein Expression

• Necessity: Prevents misattribution of protein abundance changes to phosphorylation
• Normalization Method:
  r
  # Phosphosite intensity / Corresponding protein intensity
  phospho_norm <- phospho_matrix / protein_matrix[match(phospho_data$Protein, rownames(protein_matrix)), ]
• Differential Analysis Correction: Add protein expression as covariate in Limma models

9.3 Pitfalls in Kinase-Substrate Network Analysis

Database Selection Impact:

Recommended Strategy:

r

# Cross-database validation

consensus_kinases <- intersect(

phosphonets::get_kinases(site_ids, db = "PhosphoSitePlus"),

phosphonets::get_kinases(site_ids, db = "NetworKIN")

)

Critical Best Practices:

• Site Validation:
  • Require ≥2 orthogonal evidence sources
  • Delta score >8 + PEP <0.01 for clinical applications
• Expression Confounding:
  • Always normalize phospho-intensity to protein abundance
  • Include protein levels as covariates in statistical models
• Network Inference:
  • Prioritize curated databases (PhosphoSitePlus) for clinical interpretations
  • Use integrated approaches only for hypothesis generation
  • Experimentally validate top kinase predictions
• Implementation Tip: Maintain version-controlled records of all database download dates for reproducibility

9.4 Single-Cell Phosphoproteomics Analysis Extension

Data Characteristics:

• Extreme sparsity (>95% missing values)
• Pronounced batch effects

Specialized Tools:

r

library(scPhospho)

# Sparse data imputation

sc_imputed <- impute_scphospho(sc_data, method = "ALRA")

# Batch correction

sc_corrected <- correct_scBatch(sc_imputed, batch = batch_vector)

9.5 Time-Series Phosphoproteomics Analysis

Dynamic Modeling:

r

library(maSigPro)

# Create time-point design matrix

design <- make.design.matrix(

data.frame(Time = c(0,1,3,6,24),

Replicate = rep(1:3, each=5)),

degree = 3 # Cubic polynomial fitting

)

# Differential temporal profile analysis

sig_profiles <- get.siggenes(masigpro_model, rsq = 0.7)

9.6 Special Considerations for Clinical Translation

Sample Heterogeneity Handling:

r

# Stromal signal deconvolution using ESTIMATE algorithm

library(estimate)

stromal_score <- estimateStromal(phospho_matrix)

corrected_matrix <- phospho_matrix - stromal_score %*% stromal_profile

Biomarker Validation Standards:

• Independent Cohort Validation: ROC AUC > 0.8 (Discriminatory power)
• Clinical Relevance:
  • Cox regression p < 0.01 (Statistical significance)
  • Hazard Ratio > 2 (Clinical effect size)

10. Supplementary on Cutting-Edge Technologies

10.1 Deep Learning-Driven Phosphorylation Prediction

python

# Predicting novel sites using DeepPhos

from deepphospho import predictor

model = predictor.load_model('human_v1')

new_sites = model.predict(protein_sequences)

10.2 Spatial Phosphoproteomics Integration

Technical Integration:

• Step 1: Imaging Mass Spectrometry
• Step 2: Extract spatial coordinates from imaging data
• Step 3: Identify phosphorylation hotspot regions
• Step 4: Spatially align with single-cell RNA sequencing (scRNA-seq) data
• Tools Recommendation: SPARK (Spatial Phosphoproteomics Analysis R Kit)

10.3 Integrated Phosphorylation-Protein Interaction Analysis

Experimental Design:

• Phospho-IP + Co-IP/MS for identifying phosphorylation-dependent interaction networks

Analysis Pipeline:

r

library/PhosConnect

network <- build_phos_interactome(

phospho_data = phospho_matrix,

interactome = string_db,

dependency_threshold = 0.9

)

11. Common Pitfalls and Mitigation Strategies

12. Performance Optimization Techniques

Large-Scale Data Acceleration:

r

library(disk.frame)

# Convert data to disk storage format

phospho_df <- as.disk.frame(phospho_matrix,

outdir = "phospho_df")

# Parallel processing

future::plan(multisession)

diff_results <- phospho_df %>%

map_dfr(~topTable(eBayes(lmFit(., design))), .progress = TRUE)

Cloud Computing Resource Configuration:

• Recommended for 1 million phosphosites analysis:
  • AWS: r6i.8xlarge (32 vCPU, 256 GB RAM)
  • Processing Time: ~3 hours (vs. 12+ hours on conventional servers)

To learn more about the workflow, please refer to "Phosphoproteomics Workflow Explained: From Sample to Data".

For more information on mass spectrometry and how to choose a platform, please refer to "Mass Spectrometry for Phosphoproteomics: Which Platform Is Best?".

Conclusion

This guide provides a comprehensive phosphoproteomics analysis workflow from raw data processing to biological interpretation. Key advantages include:

• Rigorous Quality Control: Multi-dimensional filtering ensures data reliability
• Kinase-Centric Analysis: Focus on core regulatory factors in signaling pathways
• Multi-Omics Integration: Reveals phosphorylation-mediated regulatory roles in molecular networks
• Clinical Translation: Enables biomarker discovery and drug prediction solutions

Through optimized workflows, typical analysis cycles can be reduced from weeks to <24 hours (standard analysis of 100 samples), delivering efficient computational support for precision medicine research.

• Best Practice Recommendations:
  • Regularly update toolkit versions
  • Utilize containerization technology to ensure reproducibility
  • Clinical translation studies require validation with independent cohorts

People Also Ask

How to analyse phosphoproteomics data?

Phosphoproteomics data analysis involves two major steps. The first step includes the identification, phosphosite localization, and quantification of phosphopeptides. The second step aims to translate phosphopeptide identification and quantification results into novel biological and clinical insights.

References

1. Montero-Bullón JF, González-Velasco Ó, Isidoro-García M, Lacal J. "Integrated in silico MS-based phosphoproteomics and network enrichment analysis of RASopathy proteins." Orphanet J Rare Dis. 2021 Jul 6;16(1):303. doi: 10.1186/s13023-021-01934-x
2. Yang P, Humphrey SJ, James DE, Yang YH, Jothi R. "Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data." Bioinformatics. 2016 Jan 15;32(2):252-9. doi: 10.1093/bioinformatics/btv550
3. Bortel P, Piga I, Koenig C, Gerner C, Martinez-Val A, Olsen JV. "Systematic Optimization of Automated Phosphopeptide Enrichment for High-Sensitivity Phosphoproteomics." Mol Cell Proteomics. 2024 May;23(5):100754. doi: 10.1016/j.mcpro.2024.100754