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diann_maxlfq.R
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
rm(list=ls())
library(iq)
library(stringr)
library(matrixStats)
pathes <- list.files("G:\\Dropbox\\papers_Fengchao\\msfragger_dia\\script\\results\\", pattern = "(report\\.tsv)|(diann-output\\.tsv)", full.names = TRUE, recursive = TRUE)
globalPrecursorFDR <- 0.01
globalProteinFDR <- 0.01
runSpecificPrecursorFDR <- 0.01
runSpecificProteinFDR <- 0.01
for (path in pathes) {
if (file.exists(path)) {
if (str_detect(path, "runtime")) {
next
}
out_path <- str_c(dirname(path), "/precursor_maxlfq.tsv")
if (file.exists(out_path) == FALSE) {
print(out_path)
print(str_c("global precursor FDR = ", globalPrecursorFDR))
print(str_c("global protein FDR = ", globalProteinFDR))
print(str_c("run specific precursor FDR = ", runSpecificPrecursorFDR))
print(str_c("run specific protein FDR = ", runSpecificProteinFDR))
df <- fast_read(path,
sample_id = "Run",
primary_id = "Precursor.Id",
secondary_id = "Precursor.Id",
intensity_col = "Precursor.Normalised",
annotation_col = NULL,
filter_string_equal = NULL,
filter_string_not_equal = NULL,
filter_double_less = c("Global.Q.Value" = globalPrecursorFDR, "Global.PG.Q.Value" = globalProteinFDR, "Q.Value" = runSpecificPrecursorFDR, "PG.Q.Value" = runSpecificProteinFDR),
filter_double_greater = NULL,
intensity_col_sep = NULL,
intensity_col_id = NULL,
na_string = "0")
df_norm <- fast_preprocess(df$quant_table, median_normalization = FALSE, log2_intensity_cutoff = 0, pdf_out = NULL) # Do not enable median normalization because the tools have their own normalization algorithm.
df_maxlfq <- fast_MaxLFQ(df_norm, row_names = df$protein[, 1], col_names = df$sample) # MaxLFQ requires a precursor having non-zero intensities in at least two runs, which makes the "quantified" entries slightly fewer than those from the DIA-NN main report.
maxlfq <- df_maxlfq$estimate
maxlfq[maxlfq <= 0] <- NA
write.table(2^maxlfq, out_path, quote = FALSE, sep = "\t", col.names = NA)
}
out_path <- str_c(dirname(path), "/modified_sequence_maxlfq.tsv")
if (file.exists(out_path) == FALSE) {
print(out_path)
print(str_c("global precursor FDR = ", globalPrecursorFDR))
print(str_c("global protein FDR = ", globalProteinFDR))
print(str_c("run specific precursor FDR = ", runSpecificPrecursorFDR))
print(str_c("run specific protein FDR = ", runSpecificProteinFDR))
df <- fast_read(path,
sample_id = "Run",
primary_id = "Modified.Sequence",
secondary_id = "Precursor.Id",
intensity_col = "Precursor.Normalised",
annotation_col = NULL,
filter_string_equal = NULL,
filter_string_not_equal = NULL,
filter_double_less = c("Global.Q.Value" = globalPrecursorFDR, "Global.PG.Q.Value" = globalProteinFDR, "Q.Value" = runSpecificPrecursorFDR, "PG.Q.Value" = runSpecificProteinFDR),
filter_double_greater = NULL,
intensity_col_sep = NULL,
intensity_col_id = NULL,
na_string = "0")
df_norm <- fast_preprocess(df$quant_table, median_normalization = FALSE, log2_intensity_cutoff = 0, pdf_out = NULL) # Do not enable median normalization because the tools have their own normalization algorithm.
df_maxlfq <- fast_MaxLFQ(df_norm, row_names = df$protein[, 1], col_names = df$sample) # MaxLFQ requires a precursor having non-zero intensities in at least two runs, which makes the "quantified" entries slightly fewer than those from the DIA-NN main report.
maxlfq <- df_maxlfq$estimate
maxlfq[maxlfq <= 0] <- NA
write.table(2^maxlfq, out_path, quote = FALSE, sep = "\t", col.names = NA)
}
out_path <- str_c(dirname(path), "/protein_maxlfq.tsv")
if (file.exists(out_path) == FALSE) {
print(out_path)
print(str_c("global precursor FDR = ", globalPrecursorFDR))
print(str_c("global protein FDR = ", globalProteinFDR))
print(str_c("run specific precursor FDR = ", runSpecificPrecursorFDR))
print(str_c("run specific protein FDR = ", runSpecificProteinFDR))
df <- fast_read(path,
sample_id = "Run",
primary_id = "Protein.Group",
secondary_id = "Precursor.Id",
intensity_col = "Precursor.Normalised",
annotation_col = NULL,
filter_string_equal = NULL,
filter_string_not_equal = NULL,
filter_double_less = c("Global.Q.Value" = globalPrecursorFDR, "Global.PG.Q.Value" = globalProteinFDR, "Q.Value" = runSpecificPrecursorFDR, "PG.Q.Value" = runSpecificProteinFDR),
filter_double_greater = NULL,
intensity_col_sep = NULL,
intensity_col_id = NULL,
na_string = "0")
df_norm <- fast_preprocess(df$quant_table, median_normalization = FALSE, pdf_out = NULL) # Do not enable median normalization because the tools have their own normalization algorithm.
df_maxlfq <- fast_MaxLFQ(df_norm, row_names = df$protein[, 1], col_names = df$sample) # MaxLFQ requires a precursor having non-zero intensities in at least two runs, which makes the "quantified" entries slightly fewer than those from the DIA-NN main report.
maxlfq <- df_maxlfq$estimate
maxlfq[maxlfq <= 0] <- NA
write.table(2^maxlfq, out_path, quote = FALSE, sep = "\t", col.names = NA)
}
out_path <- str_c(dirname(path), "/gene_maxlfq.tsv")
if (file.exists(out_path) == FALSE) {
print(out_path)
print(str_c("global precursor FDR = ", globalPrecursorFDR))
print(str_c("global protein FDR = ", globalProteinFDR))
print(str_c("run specific precursor FDR = ", runSpecificPrecursorFDR))
print(str_c("run specific protein FDR = ", runSpecificProteinFDR))
df <- fast_read(path,
sample_id = "Run",
primary_id = "Genes",
secondary_id = "Precursor.Id",
intensity_col = "Precursor.Normalised",
annotation_col = NULL,
filter_string_equal = NULL,
filter_string_not_equal = NULL,
filter_double_less = c("Global.Q.Value" = globalPrecursorFDR, "Global.PG.Q.Value" = globalProteinFDR, "Q.Value" = runSpecificPrecursorFDR, "PG.Q.Value" = runSpecificProteinFDR),
filter_double_greater = NULL,
intensity_col_sep = NULL,
intensity_col_id = NULL,
na_string = "0")
df_norm <- fast_preprocess(df$quant_table, median_normalization = FALSE, pdf_out = NULL) # Do not enable median normalization because the tools have their own normalization algorithm.
df_maxlfq <- fast_MaxLFQ(df_norm, row_names = df$protein[, 1], col_names = df$sample) # MaxLFQ requires a precursor having non-zero intensities in at least two runs, which makes the "quantified" entries slightly fewer than those from the DIA-NN main report.
maxlfq <- df_maxlfq$estimate
maxlfq[maxlfq <= 0] <- NA
write.table(2^maxlfq, out_path, quote = FALSE, sep = "\t", col.names = NA)
}
}
}