2. MSstatsPTM — Phospho DE with protein-level adjustment

Overview

Differential phospho-site abundance, adjusted for protein-level abundance changes from the bulk proteome pipeline. Uses MSstatsPTM, which fits paired models on PTM and protein datasets and reports both unadjusted (raw phospho) and adjusted (phospho minus protein) log2 fold changes per contrast.

Same contrast set as the bulk pipeline: 4 pairwise + APP × Microglia interaction.

Site-level localization filtering is applied to the phospho report at LOC_PROB_CUTOFF = 0.99 before the MSstatsPTM converter — the bulk protein report is left untouched. The search was confirmed to be directDIA (EG.Library = "directDIA™" in the Spectronaut export); Bekker-Jensen et al. 2020 (Mol Cell Proteomics) recommend a 0.99 probability threshold for directDIA searches rather than the 0.75 Class I default used for library-based searches.

Libraries

library(data.table)
library(MSstats)
library(MSstatsPTM)
library(MSstatsConvert)
library(qs2)
library(dplyr)
library(ggplot2)
library(stringr)
library(tidyr)

Directories and parameters

project_root <- "/nemo/lab/destrooperb/home/shared/zanettc/giulia_proteomics"
phospho_base <- file.path(project_root, "phosphoproteomics")
bulk_base    <- file.path(project_root, "bulk_proteomics")

run_num <- "run3"

phospho_results_dir <- file.path(phospho_base, "results", run_num)
phospho_objects_dir <- file.path(phospho_base, "data", "processed", run_num)
phospho_input_dir   <- file.path(phospho_base, "input")
bulk_input_dir      <- file.path(bulk_base, "input")

dir.create(phospho_results_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(phospho_objects_dir, recursive = TRUE, showWarnings = FALSE)

FASTA_PATH <- file.path(phospho_input_dir, "UP000000589_10090.fasta")
PTM_LABEL  <- "Phospho (STY)"   # how Spectronaut writes the modification

stopifnot("FASTA missing — see Setup notes" = file.exists(FASTA_PATH))

Build annotation (same 16-sample design)

msstats_annotation <- data.table(
  Run          = paste0("GA_", 1:16),
  Condition    = rep(c("hApp", "hAppNLGF", "hAppNLGF_FIRE", "hApp_FIRE"), each = 4),
  BioReplicate = 1:16
)
msstats_annotation

fwrite(msstats_annotation, file.path(phospho_objects_dir, "msstats_annotation.csv"))

Load both Spectronaut reports (PTM and bulk)

The bulk report serves as the “global protein” reference for adjustment. We read the same column subset that the bulk MSstats notebook uses.

msstats_cols <- c(
  "R.Condition", "R.FileName", "R.Replicate",
  "PG.ProteinGroups", "PG.ProteinAccessions",
  "PEP.StrippedSequence",
  "EG.ModifiedSequence", "EG.PrecursorId", "EG.Qvalue",
  "EG.PTMAssayProbability",
  "FG.Charge", "FG.Id",
  "F.FrgIon", "F.Charge", "F.FrgLossType", "F.FrgNum", "F.FrgType",
  "F.NormalizedPeakArea", "F.PeakArea", "F.ExcludedFromQuantification"
)

# QC notebook 01 flagged that EG.PTMAssayProbability is sometimes missing from
# Spectronaut exports — probe the header and fall back to
# EG.PTMLocalizationProbabilities if needed.
phospho_path <- file.path(phospho_input_dir, "20260504_125530_PCF000612_phospho_GA_MSStats_updated.csv")
phospho_header <- names(fread(phospho_path, nrows = 0))
if ("EG.PTMAssayProbability" %in% phospho_header) {
  loc_prob_col <- "EG.PTMAssayProbability"
} else if ("EG.PTMLocalizationProbabilities" %in% phospho_header) {
  loc_prob_col <- "EG.PTMLocalizationProbabilities"
  msstats_cols <- sub("EG.PTMAssayProbability", loc_prob_col, msstats_cols, fixed = TRUE)
} else {
  stop(paste0(
    "FATAL: no localization probability column found in the phospho report.\n",
    "Expected EG.PTMAssayProbability or EG.PTMLocalizationProbabilities.\n",
    "Re-export from Spectronaut with PTM localization enabled before proceeding.\n",
    "Do NOT run the pipeline without this column — site-level filtering would be skipped\n",
    "and downstream results would be unreliable."
  ))
}
cat("Localization probability column:", loc_prob_col, "\n")

Localization probability column: EG.PTMAssayProbability 

cat("Reading phospho report...\n")

Reading phospho report...

phospho_raw <- fread(
  phospho_path,
  select = msstats_cols
)
cat("Phospho rows:", nrow(phospho_raw), "\n")

Phospho rows: 19232048 

cat("Reading bulk proteome report (for protein-level adjustment)...\n")

Reading bulk proteome report (for protein-level adjustment)...

protein_raw <- fread(
  file.path(bulk_input_dir, "20260423_154110_PCF000612_GA_MSStats_Report-Proteome.csv"),
  select = setdiff(msstats_cols,
                   c("EG.PTMAssayProbability", "EG.PTMLocalizationProbabilities"))
)
cat("Protein rows:", nrow(protein_raw), "\n")

Protein rows: 24057952 

Filter phospho by site localization probability

# directDIA confirmed via EG.Library = "directDIA™" in the Spectronaut export.
# Bekker-Jensen et al. 2020 (Mol Cell Proteomics) recommend 0.99 for directDIA
# rather than the 0.75 Class I threshold used for library-based DIA searches.
LOC_PROB_CUTOFF <- 0.99

n_before         <- nrow(phospho_raw)
sites_before     <- uniqueN(phospho_raw$EG.ModifiedSequence)
proteins_before  <- uniqueN(phospho_raw$PG.ProteinGroups)

phospho_raw <- phospho_raw[get(loc_prob_col) >= LOC_PROB_CUTOFF]

n_after         <- nrow(phospho_raw)
sites_after     <- uniqueN(phospho_raw$EG.ModifiedSequence)
proteins_after  <- uniqueN(phospho_raw$PG.ProteinGroups)
pct             <- round(100 * n_after / n_before, 2)

cat(sprintf("Localization filter: %s >= %.2f\n", loc_prob_col, LOC_PROB_CUTOFF))

Localization filter: EG.PTMAssayProbability >= 0.99

cat(sprintf("  Rows:     %d  →  %d  (%.2f%% retained)\n",  n_before,    n_after,    pct))

  Rows:     19232048  →  5003615  (26.02% retained)

cat(sprintf("  Sites:    %d  →  %d\n",                      sites_before, sites_after))

  Sites:    175523  →  58826

cat(sprintf("  Proteins: %d  →  %d\n",                      proteins_before, proteins_after))

  Proteins: 8199  →  7308

# protein_raw is intentionally NOT filtered — protein-level adjustment uses
# whole-protein abundance and does not require site localization.

Normalise phospho R.FileName + patch R.Condition / R.Replicate

The phospho report writes sample IDs as GA_P_1..GA_P_16 while the bulk proteome and the annotation use GA_1..GA_16. Strip the GA_P_ prefix on the phospho data so both reports key against the same annotation. R.Condition is also “Not Defined” in both raw files — overwrite it from the annotation.

phospho_raw[, R.FileName := stringr::str_replace(R.FileName, "^GA_P_", "GA_")]

patch <- function(dt) {
  dt[msstats_annotation, on = c("R.FileName" = "Run"),
     `:=`(R.Condition = i.Condition, R.Replicate = i.BioReplicate)]
  dt
}
phospho_raw <- patch(phospho_raw)
protein_raw <- patch(protein_raw)

Convert to MSstatsPTM input format

SpectronauttoMSstatsPTMFormat() returns a list of two data frames: PTM and PROTEIN. Both must be present for protein-level adjustment. The same annotation table is used for both — MSstatsPTM matches by Run.

ptm_input <- suppressWarnings(SpectronauttoMSstatsPTMFormat(
  input              = phospho_raw,
  annotation         = msstats_annotation,
  fasta_path         = FASTA_PATH,
  protein_input      = protein_raw,
  use_unmod_peptides = FALSE,         # site-level: drop unmodified peptides
  mod_id             = "\\[Phospho \\(STY\\)\\]",
  filter_with_Qvalue = TRUE,
  qvalue_cutoff      = 0.01,
  removeProtein_with1Feature = FALSE
))

[1] "FASTA file missing 275 Proteins. These will be removed. This may be due to non-unique identifications."
INFO  [2026-05-05 18:59:03] ** Raw data from Spectronaut imported successfully.
INFO  [2026-05-05 18:59:03] ** Raw data from Spectronaut cleaned successfully.
INFO  [2026-05-05 18:59:03] ** Using provided annotation.
INFO  [2026-05-05 18:59:03] ** Run labels were standardized to remove symbols such as '.' or '%'.
INFO  [2026-05-05 18:59:03] ** The following options are used:
  - Features will be defined by the columns: PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge
  - Shared peptides will be removed.
  - Proteins with single feature will not be removed.
  - Features with less than 3 measurements across runs will be removed.
INFO  [2026-05-05 18:59:03] ** Intensities with values of FExcludedFromQuantification equal to TRUE are replaced with NA
WARN  [2026-05-05 18:59:03] ** PGQvalue not found in input columns.
INFO  [2026-05-05 18:59:03] ** Intensities with values not smaller than 0.01 in EGQvalue are replaced with NA
INFO  [2026-05-05 18:59:03] ** Features with all missing measurements across runs are removed.
INFO  [2026-05-05 18:59:04] ** Shared peptides are removed.
INFO  [2026-05-05 18:59:05] ** Multiple measurements in a feature and a run are summarized by summaryforMultipleRows: max
INFO  [2026-05-05 18:59:05] ** Features with one or two measurements across runs are removed.
INFO  [2026-05-05 18:59:06] ** Run annotation merged with quantification data.
INFO  [2026-05-05 18:59:06] ** Features with one or two measurements across runs are removed.
INFO  [2026-05-05 18:59:06] ** Fractionation handled.
INFO  [2026-05-05 18:59:06] ** Updated quantification data to make balanced design. Missing values are marked by NA
INFO  [2026-05-05 18:59:06] ** Finished preprocessing. The dataset is ready to be processed by the dataProcess function.
INFO  [2026-05-05 18:59:09] ** Raw data from Spectronaut imported successfully.
INFO  [2026-05-05 18:59:14] ** Raw data from Spectronaut cleaned successfully.
INFO  [2026-05-05 18:59:16] ** Using annotation extracted from quantification data.
INFO  [2026-05-05 18:59:16] ** Run labels were standardized to remove symbols such as '.' or '%'.
INFO  [2026-05-05 18:59:16] ** The following options are used:
  - Features will be defined by the columns: PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge
  - Shared peptides will be removed.
  - Proteins with single feature will not be removed.
  - Features with less than 3 measurements across runs will be removed.
INFO  [2026-05-05 18:59:21] ** Intensities with values of FExcludedFromQuantification equal to TRUE are replaced with NA
WARN  [2026-05-05 18:59:22] ** PGQvalue not found in input columns.
INFO  [2026-05-05 18:59:23] ** Intensities with values not smaller than 0.01 in EGQvalue are replaced with NA
INFO  [2026-05-05 18:59:27] ** Features with all missing measurements across runs are removed.
INFO  [2026-05-05 18:59:31] ** Shared peptides are removed.
INFO  [2026-05-05 19:00:30] ** Multiple measurements in a feature and a run are summarized by summaryforMultipleRows: max
INFO  [2026-05-05 19:00:32] ** Features with one or two measurements across runs are removed.
INFO  [2026-05-05 19:00:35] ** Run annotation merged with quantification data.
INFO  [2026-05-05 19:00:45] ** Features with one or two measurements across runs are removed.
INFO  [2026-05-05 19:00:45] ** Fractionation handled.
INFO  [2026-05-05 19:01:08] ** Updated quantification data to make balanced design. Missing values are marked by NA
INFO  [2026-05-05 19:01:10] ** Finished preprocessing. The dataset is ready to be processed by the dataProcess function.

cat("PTM features:    ", nrow(ptm_input$PTM),     "\n")

PTM features:     540080 

cat("Protein features:", nrow(ptm_input$PROTEIN), "\n")

Protein features: 11946400 

qs_save(ptm_input, file.path(phospho_objects_dir, "ptm_input.qs2"))

Reload checkpoint (skip the read+convert on re-runs)

ptm_input <- qs_read(file.path(phospho_objects_dir, "ptm_input.qs2"))

Data summarisation (per dataset)

dataSummarizationPTM() runs dataProcess on PTM and PROTEIN separately and returns both. TMP summarisation, median normalisation, MB-imputation — same defaults as the bulk pipeline.

summarised <- suppressWarnings(dataSummarizationPTM(
  data                                 = ptm_input,
  normalization                        = "equalizeMedians",
  summaryMethod                        = "TMP",
  censoredInt                          = "NA",
  MBimpute                             = TRUE,
  featureSubset                        = "highQuality",
  remove_uninformative_feature_outlier = TRUE
))

INFO  [2026-05-05 19:01:15] ** Features with one or two measurements across runs are removed.
INFO  [2026-05-05 19:01:15] ** Fractionation handled.
INFO  [2026-05-05 19:01:16] ** Updated quantification data to make balanced design. Missing values are marked by NA
INFO  [2026-05-05 19:01:17] ** Log2 intensities under cutoff = -0.34817  were considered as censored missing values.
INFO  [2026-05-05 19:01:17] ** Log2 intensities = NA were considered as censored missing values.
INFO  [2026-05-05 19:01:17] ** Use all features that the dataset originally has.
INFO  [2026-05-05 19:01:17] 
 # proteins: 8883
 # peptides per protein: 1-11
 # features per peptide: 1-6
INFO  [2026-05-05 19:01:17] Some proteins have only one feature: 
 A2AJI0_S282,
 A2AP18_S676_S686,
 A2AR02_S411_S413,
 A2RSJ4_S21,
 A2RSJ4_S952 ...
INFO  [2026-05-05 19:01:17] 
                    hApp hAppNLGF hAppNLGF_FIRE hApp_FIRE
             # runs    4        4             4         4
    # bioreplicates    4        4             4         4
 # tech. replicates    1        1             1         1
INFO  [2026-05-05 19:01:17] Some features are completely missing in at least one condition:  
 AAAAALSGS[Phospho (STY)]PPQTEKPTHYR_3_b5_1,
 AAAAALSGS[Phospho (STY)]PPQTEKPTHYR_3_y11_2,
 AAAAALSGS[Phospho (STY)]PPQTEKPTHYR_3_y15_2,
 AAADDGEEPKS[Phospho (STY)]EPETK_3_b4_1,
 AAADDGEEPKS[Phospho (STY)]EPETK_3_y13_2 ...
INFO  [2026-05-05 19:01:17]  == Start the summarization per subplot...

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INFO  [2026-05-05 19:03:25]  == Summarization is done.

INFO  [2026-05-05 19:03:45] ** Features with one or two measurements across runs are removed.
INFO  [2026-05-05 19:03:45] ** Fractionation handled.
INFO  [2026-05-05 19:04:02] ** Updated quantification data to make balanced design. Missing values are marked by NA
INFO  [2026-05-05 19:04:29] ** Log2 intensities under cutoff = -2.162  were considered as censored missing values.
INFO  [2026-05-05 19:04:29] ** Log2 intensities = NA were considered as censored missing values.
INFO  [2026-05-05 20:46:10] ** Flag uninformative feature and outliers by feature selection algorithm.
INFO  [2026-05-05 20:46:13] 
 # proteins: 8918
 # peptides per protein: 1-631
 # features per peptide: 1-6
INFO  [2026-05-05 20:46:13] Some proteins have only one feature: 
 A0A0G2JET4,
 A6X935,
 C0HLV9,
 D3Z7A4;K7N6U2;K7N6Y8;K7N754;L7N2A7,
 P35991 ...
INFO  [2026-05-05 20:46:13] 
                    hApp hAppNLGF hAppNLGF_FIRE hApp_FIRE
             # runs    4        4             4         4
    # bioreplicates    4        4             4         4
 # tech. replicates    1        1             1         1
INFO  [2026-05-05 20:46:15] Some features are completely missing in at least one condition:  
 AAAAAAAAQM[Oxidation (M)]HTK_2_b4_1,
 AAAAAAAAQM[Oxidation (M)]HTK_2_b5_1,
 AAAAAAAAQM[Oxidation (M)]HTK_2_y8_1,
 AAAADGEPLHNEEER_2_y10_1,
 AAAAVASAASSC[Carbamidomethyl (C)]R_3_y7_1 ...
INFO  [2026-05-05 20:46:15]  == Start the summarization per subplot...
INFO  [2026-05-05 20:46:16] ** Filtered out uninformative features and outliers.

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INFO  [2026-05-05 21:05:26]  == Summarization is done.

qs_save(summarised, file.path(phospho_objects_dir, "summarised_ptm.qs2"))

Verify condition levels and build contrast matrix

Same 4 pairwise + interaction as the bulk pipeline.

Interaction expands to: (hAppNLGF − hApp) − (hAppNLGF_FIRE − hApp_FIRE) Disease effects with microglia vs disease effects without.

summarised <- qs_read(file.path(phospho_objects_dir, "summarised_ptm.qs2"))

condition_levels <- levels(summarised$PTM$ProteinLevelData$GROUP)
cat("Condition levels (alphabetical):\n")

Condition levels (alphabetical):

print(condition_levels)

[1] "hApp"          "hApp_FIRE"     "hAppNLGF"      "hAppNLGF_FIRE"

make_contrast <- function(numerator, denominator, levels) {
  v <- setNames(rep(0L, length(levels)), levels)
  v[numerator]   <-  1L
  v[denominator] <- -1L
  v
}

interaction_weights <- c(
  hApp           = -1,
  hAppNLGF       =  1,
  hApp_FIRE      =  1,
  hAppNLGF_FIRE = -1
)

make_linear_contrast <- function(weights, levels) {
  v <- setNames(rep(0, length(levels)), levels)
  v[names(weights)] <- weights
  stopifnot(abs(sum(v)) < 1e-9)
  v
}

comparison_matrix <- rbind(
  make_contrast("hAppNLGF",      "hApp",        condition_levels),
  make_contrast("hApp_FIRE",     "hApp",        condition_levels),
  make_contrast("hAppNLGF_FIRE", "hAppNLGF",    condition_levels),
  make_contrast("hAppNLGF_FIRE", "hApp_FIRE",   condition_levels),
  make_linear_contrast(interaction_weights,     condition_levels)
)
rownames(comparison_matrix) <- c(
  "hAppNLGF_vs_hApp",
  "hApp_FIRE_vs_hApp",
  "hAppNLGF_FIRE_vs_hAppNLGF",
  "hAppNLGF_FIRE_vs_hApp_FIRE",
  "Interaction_APP_x_Microglia"
)
print(comparison_matrix)

                            hApp hApp_FIRE hAppNLGF hAppNLGF_FIRE
hAppNLGF_vs_hApp              -1         0        1             0
hApp_FIRE_vs_hApp             -1         1        0             0
hAppNLGF_FIRE_vs_hAppNLGF      0         0       -1             1
hAppNLGF_FIRE_vs_hApp_FIRE     0        -1        0             1
Interaction_APP_x_Microglia   -1         1        1            -1

Group comparison (adjusted for protein abundance)

groupComparisonPTM() returns three result tables:

• PTM.Model — raw PTM contrast results (unadjusted phospho changes)
• PROTEIN.Model — protein-level contrast results (from the bulk reference)
• ADJUSTED.Model — phospho minus protein per contrast → “phospho-specific” changes

The adjusted table is what most phospho papers cite — it isolates regulation that isn’t simply explained by total protein abundance.

ptm_results <- suppressWarnings(groupComparisonPTM(
  data            = summarised,
  contrast.matrix = comparison_matrix,
  data.type       = "LabelFree"
))

INFO  [2026-05-05 21:05:50]  == Start to test and get inference in whole plot ...

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INFO  [2026-05-05 21:07:48]  == Comparisons for all proteins are done.

INFO  [2026-05-05 21:07:50]  == Start to test and get inference in whole plot ...

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INFO  [2026-05-05 21:09:41]  == Comparisons for all proteins are done.

#qs_save(ptm_results, file.path(phospho_objects_dir, "ptm_results.qs2"))

Export results CSVs

write_table <- function(df, filename) {
  fwrite(as.data.table(df), file.path(phospho_results_dir, filename))
  cat("Wrote", filename, ":", nrow(df), "rows\n")
}

write_table(ptm_results$PTM.Model,      "PTM_unadjusted_GroupComparison.csv")

Wrote PTM_unadjusted_GroupComparison.csv : 44415 rows

write_table(ptm_results$PROTEIN.Model,  "PROTEIN_GroupComparison.csv")

Wrote PROTEIN_GroupComparison.csv : 44590 rows

write_table(ptm_results$ADJUSTED.Model, "PTM_adjusted_GroupComparison.csv")

Wrote PTM_adjusted_GroupComparison.csv : 44415 rows

Quick summary of adjusted results

adj <- as.data.table(ptm_results$ADJUSTED.Model)
adj[!is.na(adj.pvalue),
    .(n_tested = .N,
      n_sig    = sum(adj.pvalue < 0.05),
      n_up     = sum(adj.pvalue < 0.05 & log2FC > 0),
      n_down   = sum(adj.pvalue < 0.05 & log2FC < 0)),
    by = Label][order(Label)]