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Merged
tonywu1999 merged 18 commits into from
Apr 14, 2026
Merged

feat(protein turnover): per-label statistics and multi-label summarization #193

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0260c10
feat: per-label statistics and multi-label summarization
tonywu1999 Apr 11, 2026
f4e74f8
fix(pr4): add LABEL to per-feature observation count grouping keys
tonywu1999 Apr 11, 2026
0b76e02
test(summarization): unit tests for per-label statistics
tonywu1999 Apr 11, 2026
5202b2c
fix is_labeled_ref
tonywu1999 Apr 13, 2026
654ac41
update unit tests
tonywu1999 Apr 13, 2026
c594c19
fix grouping based on srm or not
tonywu1999 Apr 13, 2026
a34440f
fix srm discrepancies
tonywu1999 Apr 13, 2026
d861d0d
fix is_labeled_ref
tonywu1999 Apr 13, 2026
3fd691c
add unit tests
tonywu1999 Apr 13, 2026
833427b
make unit test more robust
tonywu1999 Apr 14, 2026
9c9d7b5
fix tests
tonywu1999 Apr 14, 2026
78f678a
fix unit tests
tonywu1999 Apr 14, 2026
f59c26a
remove dead code for .prepareLinear
tonywu1999 Apr 14, 2026
cadbca0
update docs
tonywu1999 Apr 14, 2026
cc151ef
fix summarization with labels
tonywu1999 Apr 14, 2026
14975f5
make fixes with labeling
tonywu1999 Apr 14, 2026
b9e28eb
add DDARawData test
tonywu1999 Apr 14, 2026
eca8c92
all tests pass
tonywu1999 Apr 14, 2026
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136 changes: 79 additions & 57 deletions R/dataProcess.R
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Original file line number Diff line number Diff line change
Expand Up @@ -210,7 +210,12 @@ MSstatsSummarizeWithMultipleCores = function(input, method, impute, censored_sym
remove50missing, equal_variance, numberOfCores = 1,
aft_iterations = 90) {
if (numberOfCores > 1) {
protein_indices = split(seq_len(nrow(input)), list(input$PROTEIN))
is_labeled_reference = "is_labeled_ref" %in% colnames(input) && any(input$is_labeled_ref, na.rm = TRUE)
if (is_labeled_reference) {
protein_indices = split(seq_len(nrow(input)), list(input$PROTEIN))
} else {
protein_indices = split(seq_len(nrow(input)), list(input$PROTEIN, input$LABEL))
}
num_proteins = length(protein_indices)
function_environment = environment()
cl = parallel::makeCluster(numberOfCores)
Expand Down Expand Up @@ -292,9 +297,14 @@ MSstatsSummarizeWithMultipleCores = function(input, method, impute, censored_sym
#'
MSstatsSummarizeWithSingleCore = function(input, method, impute, censored_symbol,
remove50missing, equal_variance, aft_iterations = 90) {


protein_indices = split(seq_len(nrow(input)), list(input$PROTEIN))


is_labeled_reference = "is_labeled_ref" %in% colnames(input) && any(input$is_labeled_ref, na.rm = TRUE)
if (is_labeled_reference) {
protein_indices = split(seq_len(nrow(input)), list(input$PROTEIN))
} else {
protein_indices = split(seq_len(nrow(input)), list(input$PROTEIN, input$LABEL))
}
num_proteins = length(protein_indices)
summarized_results = vector("list", num_proteins)
if (method == "TMP") {
Expand Down Expand Up @@ -356,57 +366,59 @@ MSstatsSummarizeWithSingleCore = function(input, method, impute, censored_symbol
#' head(single_protein_summary[[1]])
#'
#'
MSstatsSummarizeSingleLinear = function(single_protein,
MSstatsSummarizeSingleLinear = function(single_protein,
impute,
censored_symbol,
remove50missing,
censored_symbol,
remove50missing,
aft_iterations = 90,
equal_variances = TRUE) {
ABUNDANCE = RUN = FEATURE = PROTEIN = LogIntensities = NULL

cols = intersect(
colnames(single_protein),
c("newABUNDANCE", "cen", "RUN", "FEATURE", "ref_covariate")
)


is_labeled_reference = "is_labeled_ref" %in% colnames(single_protein) &&
any(single_protein$is_labeled_ref, na.rm = TRUE)

single_protein = single_protein[
(n_obs > 1 & !is.na(n_obs)) &
(n_obs_run > 0 & !is.na(n_obs_run))
]

if (nrow(single_protein) == 0) {
return(list(NULL, NULL))
}

single_protein[, RUN := factor(RUN)]
single_protein[, FEATURE := factor(FEATURE)]

if (impute & any(single_protein[["censored"]])) {
survival_fit = .fitSurvival(
single_protein[LABEL == "L", cols, with = FALSE],
aft_iterations
)
fit_data = if (is_labeled_reference) {
single_protein[is_labeled_ref == FALSE, cols, with = FALSE]
} else {
single_protein[, cols, with = FALSE]
}
survival_fit = .fitSurvival(fit_data, aft_iterations)
sigma2 = survival_fit$scale^2

single_protein[, c("predicted", "imputation_var") := {
pred = predict(survival_fit, newdata = .SD, se.fit = TRUE)
list(pred$fit, pred$se.fit^2 + sigma2)
}]

single_protein[, predicted := ifelse(
censored & (LABEL == "L"),
predicted,
NA
)]
single_protein[, newABUNDANCE := ifelse(
censored & LABEL == "L",
predicted,
newABUNDANCE
)]

survival = single_protein[, c(cols, "predicted"), with = FALSE]

if (is_labeled_reference) {
single_protein[, predicted := ifelse(censored & is_labeled_ref == FALSE, predicted, NA)]
single_protein[, newABUNDANCE := ifelse(censored & is_labeled_ref == FALSE, predicted, newABUNDANCE)]
} else {
single_protein[, predicted := ifelse(censored, predicted, NA)]
single_protein[, newABUNDANCE := ifelse(censored, predicted, newABUNDANCE)]
}

survival = single_protein[, intersect(c(cols, "LABEL", "predicted"), colnames(single_protein)), with = FALSE]
} else {
survival = single_protein[, cols, with = FALSE]
survival = single_protein[, intersect(c(cols, "LABEL"), colnames(single_protein)), with = FALSE]
survival[, predicted := NA]
}

Expand All @@ -432,29 +444,29 @@ MSstatsSummarizeSingleLinear = function(single_protein,
is_single_feature = .checkSingleFeature(single_protein)

if (is_single_feature) {
result = single_protein[LABEL == "L",
.(LogIntensities = mean(newABUNDANCE)),
by = RUN]
# Todo: enable SRM linear summarization
if (is_labeled_reference) single_protein = single_protein[LABEL == "L"]
result = single_protein[, .(LogIntensities = mean(newABUNDANCE)), by = RUN]
result[, Protein := unique(single_protein$PROTEIN)]
result[, LABEL := if (is_labeled_reference) "L" else unique(single_protein$LABEL)]
result[, Variance := NA_real_]
setcolorder(result, c("Protein", "RUN", "LogIntensities",
"Variance"))

setcolorder(result, c("Protein", "RUN", "LogIntensities", "Variance"))

Comment thread
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return(list(result, survival))
} else {
counts = xtabs(
~ RUN + FEATURE,
data = unique(single_protein[, .(FEATURE, RUN)])
)
counts = as.matrix(counts)

fit = try(
.fitLinearModel(single_protein, is_single_feature,
is_labeled = label,
equal_variances),
silent = TRUE
)

if (inherits(fit, "try-error")) {
msg = paste("*** error : can't fit the model for",
unique(single_protein$PROTEIN))
Expand All @@ -464,12 +476,13 @@ MSstatsSummarizeSingleLinear = function(single_protein,
} else {
cf = summary(fit)$coefficients[, 1]
cov_mat = vcov(fit)

result = unique(single_protein[, .(Protein = PROTEIN,
RUN = RUN)])

result = unique(single_protein[, .(Protein = PROTEIN, RUN = RUN)])
extracted_values = get_linear_summary(single_protein, cf,
counts, label, cov_mat)
result = cbind(result, extracted_values)
# Todo: enable SRM linear summarization
result[, LABEL := if (is_labeled_reference) "L" else unique(single_protein$LABEL)]
}

return(list(result, survival))
Expand Down Expand Up @@ -508,12 +521,14 @@ MSstatsSummarizeSingleLinear = function(single_protein,
#' impute, cens, FALSE, 100)
#' head(single_protein_summary[[1]])
#'
MSstatsSummarizeSingleTMP = function(single_protein, impute, censored_symbol,
MSstatsSummarizeSingleTMP = function(single_protein, impute, censored_symbol,
remove50missing, aft_iterations = 90) {
newABUNDANCE = n_obs = n_obs_run = RUN = FEATURE = LABEL = NULL
predicted = censored = NULL
cols = intersect(colnames(single_protein), c("newABUNDANCE", "cen", "RUN",
"FEATURE", "ref_covariate"))
is_labeled_reference = "is_labeled_ref" %in% colnames(single_protein) &&
any(single_protein$is_labeled_ref, na.rm = TRUE)
single_protein = single_protein[(n_obs > 1 & !is.na(n_obs)) &
(n_obs_run > 0 & !is.na(n_obs_run))]
if (nrow(single_protein) == 0) {
Expand All @@ -522,43 +537,50 @@ MSstatsSummarizeSingleTMP = function(single_protein, impute, censored_symbol,
single_protein[, RUN := factor(RUN)]
single_protein[, FEATURE := factor(FEATURE)]
if (impute & any(single_protein[["censored"]])) {

# Flag to track convergence warning
converged = TRUE


fit_data = if (is_labeled_reference) {
single_protein[is_labeled_ref == FALSE, cols, with = FALSE]
} else {
single_protein[, cols, with = FALSE]
}

# Try to fit survival model and catch convergence warnings
survival_fit = withCallingHandlers({
.fitSurvival(single_protein[LABEL == "L", cols, with = FALSE],
aft_iterations)
.fitSurvival(fit_data, aft_iterations)
}, warning = function(w) {
if (grepl("converge", conditionMessage(w), ignore.case = TRUE)) {
message("Convergence warning caught: ", conditionMessage(w))
converged <<- FALSE
}
})

if (converged) {
single_protein[, predicted := predict(survival_fit, newdata = .SD)]
} else {
single_protein[, predicted := NA_real_]
}

single_protein[, predicted := ifelse(censored & (LABEL == "L"), predicted, NA)]
single_protein[, newABUNDANCE := ifelse(censored & LABEL == "L",
predicted, newABUNDANCE)]
survival = single_protein[, c(cols, "predicted"), with = FALSE]

if (is_labeled_reference) {
single_protein[, predicted := ifelse(censored & is_labeled_ref == FALSE, predicted, NA)]
single_protein[, newABUNDANCE := ifelse(censored & is_labeled_ref == FALSE, predicted, newABUNDANCE)]
} else {
single_protein[, predicted := ifelse(censored, predicted, NA)]
single_protein[, newABUNDANCE := ifelse(censored, predicted, newABUNDANCE)]
}
survival = single_protein[, intersect(c(cols, "LABEL", "predicted"), colnames(single_protein)), with = FALSE]
} else {
survival = single_protein[, cols, with = FALSE]
survival = single_protein[, intersect(c(cols, "LABEL"), colnames(single_protein)), with = FALSE]
survival[, predicted := NA]
}

single_protein = .isSummarizable(single_protein, remove50missing)
if (is.null(single_protein)) {
return(list(NULL, NULL))
} else {
single_protein = single_protein[!is.na(newABUNDANCE), ]
is_labeled_reference = "is_labeled_ref" %in% colnames(single_protein) &&
any(single_protein$is_labeled_ref, na.rm = TRUE)
result = .runTukey(single_protein, is_labeled_reference, censored_symbol,
remove50missing)
}
Expand Down
55 changes: 26 additions & 29 deletions R/utils_output.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -40,34 +40,31 @@ MSstatsSummarizationOutput = function(input, summarized, processed,

input = .finalizeInput(input, summarized, method, impute, censored_symbol)
summarized = lapply(summarized, function(x) x[[1]])
summarized = data.table::rbindlist(summarized)
summarized = data.table::rbindlist(summarized, fill = TRUE)
if (inherits(summarized, "try-error")) {
msg = paste("*** error : can't summarize per subplot with ",
msg = paste("*** error : can't summarize per subplot with ",
method, ".")
getOption("MSstatsLog")("ERROR", msg)
getOption("MSstatsMsg")("ERROR", msg)
rqall = NULL
rqmodelqc = NULL
workpred = NULL
} else {
input[LABEL == "L", TotalGroupMeasurements := uniqueN(.SD),
by = c("PROTEIN", "GROUP"),
input[, TotalGroupMeasurements := uniqueN(.SD),
by = c("PROTEIN", "GROUP", "LABEL"),
.SDcols = c("FEATURE", "originalRUN")]
cols = intersect(c("PROTEIN", "originalRUN", "RUN", "GROUP",
"GROUP_ORIGINAL", "SUBJECT_ORIGINAL",
"GROUP_ORIGINAL", "SUBJECT_ORIGINAL", "LABEL",
"TotalGroupMeasurements",
"NumMeasuredFeature", "MissingPercentage",
"NumMeasuredFeature", "MissingPercentage",
"more50missing", "NumImputedFeature"),
colnames(input))
merge_col = ifelse(is.element("RUN", colnames(summarized)),
merge_col = ifelse(is.element("RUN", colnames(summarized)),
"RUN", "SUBJECT_ORIGINAL")
lab = unique(input[LABEL == "L", cols, with = FALSE])
if (nlevels(input$LABEL) > 1) {
lab = lab[GROUP != 0]
}
lab = unique(input[, cols, with = FALSE])
lab = lab[, colnames(lab) != "GROUP", with = FALSE]
rqall = merge(summarized, lab, by.x = c(merge_col, "Protein"),
by.y = c(merge_col, "PROTEIN"))
rqall = merge(summarized, lab, by.x = c(merge_col, "Protein", "LABEL"),
by.y = c(merge_col, "PROTEIN", "LABEL"))
data.table::setnames(rqall, c("GROUP_ORIGINAL", "SUBJECT_ORIGINAL"),
c("GROUP", "SUBJECT"), skip_absent = TRUE)

Expand All @@ -83,8 +80,8 @@ MSstatsSummarizationOutput = function(input, summarized, processed,
output_cols = intersect(c("PROTEIN", "PEPTIDE", "TRANSITION", "FEATURE",
"LABEL", "GROUP", "RUN", "SUBJECT", "FRACTION",
"originalRUN", "censored", "INTENSITY", "ABUNDANCE",
"newABUNDANCE", "predicted", "feature_quality",
"is_outlier", "remove"), colnames(input))
"newABUNDANCE", "predicted", "feature_quality",
"is_outlier", "remove", "is_labeled_ref"), colnames(input))
input = input[, output_cols, with = FALSE]

if (is.element("remove", colnames(processed))) {
Expand Down Expand Up @@ -126,31 +123,31 @@ MSstatsSummarizationOutput = function(input, summarized, processed,
INTENSITY = newABUNDANCE = NumImputedFeature = NULL

survival_predictions = lapply(summarized, function(x) x[[2]])
predicted_survival = data.table::rbindlist(survival_predictions)
predicted_survival = data.table::rbindlist(survival_predictions, fill = TRUE)
if (impute) {
cols = intersect(colnames(input), c("newABUNDANCE",
cols = intersect(colnames(input), c("newABUNDANCE",
"cen", "RUN",
"FEATURE", "ref_covariate"))
input = merge(input[, colnames(input) != "newABUNDANCE", with = FALSE],
"FEATURE", "ref_covariate", "LABEL"))
input = merge(input[, colnames(input) != "newABUNDANCE", with = FALSE],
predicted_survival,
by = setdiff(cols, "newABUNDANCE"),
all.x = TRUE)
}
input[, NonMissingStats := .getNonMissingFilterStats(.SD, censored_symbol)]
input[, NumMeasuredFeature := sum(NonMissingStats),
by = c("PROTEIN", "RUN")]
input[, NumMeasuredFeature := sum(NonMissingStats),
by = c("PROTEIN", "RUN", "LABEL")]
input[, MissingPercentage := 1 - (NumMeasuredFeature / total_features)]
input[, more50missing := MissingPercentage >= 0.5]
if (!is.null(censored_symbol)) {
if (is.element("censored", colnames(input))) {
input[, nonmissing_orig := LABEL == "L" & !censored]
input[, nonmissing_orig := !censored]
} else {
input[, nonmissing_orig := LABEL == "L" & !is.na(INTENSITY)]
input[, nonmissing_orig := !is.na(INTENSITY)]
}
input[, nonmissing_orig := ifelse(is.na(newABUNDANCE), TRUE, nonmissing_orig)]
if (impute) {
input[, NumImputedFeature := sum(LABEL == "L" & !nonmissing_orig),
by = c("PROTEIN", "RUN")]
input[, NumImputedFeature := sum(!nonmissing_orig),
by = c("PROTEIN", "RUN", "LABEL")]
} else {
input[, NumImputedFeature := 0]
}
Expand All @@ -168,15 +165,15 @@ MSstatsSummarizationOutput = function(input, summarized, processed,
censored = INTENSITY = newABUNDANCE = NumImputedFeature = NULL

input[, NonMissingStats := .getNonMissingFilterStats(.SD, censored_symbol)]
input[, NumMeasuredFeature := sum(NonMissingStats),
by = c("PROTEIN", "RUN")]
input[, NumMeasuredFeature := sum(NonMissingStats),
by = c("PROTEIN", "RUN", "LABEL")]
input[, MissingPercentage := 1 - (NumMeasuredFeature / total_features)]
input[, more50missing := MissingPercentage >= 0.5]
if (!is.null(censored_symbol)) {
if (is.element("censored", colnames(input))) {
input[, nonmissing_orig := LABEL == "L" & !censored]
input[, nonmissing_orig := !censored]
} else {
input[, nonmissing_orig := LABEL == "L" & !is.na(INTENSITY)]
input[, nonmissing_orig := !is.na(INTENSITY)]
}
input[, nonmissing_orig := ifelse(is.na(newABUNDANCE), TRUE, nonmissing_orig)]
input[, NumImputedFeature := 0]
Expand Down
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