Fast MaxLFQ"

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
require("knitr")
local_file_exist <- file.exists("DIA-report-long-format.txt")

We have implemented a highly optimized version of the iq pipeline (Pham et al., Bioinformatics 2020). To run the following examples, download DIA-report-long-format.zip and unzip the file to a local working directory.

The unzipped file DIA-report-long-format.txt is a tab-deliminated text file exported from a Spectronaut search using this export schema iq.rs. The user might want to import this schema to their Spectronaut installation for the ease of using the iq pipeline.

The standard pipeline

First, we apply the standard pipeline implemented in pure R. Read and filter the data

library("iq") # if not already installed, run install.packages("iq") 

raw <- read.delim("DIA-Report-long-format.txt")

selected <- raw$F.ExcludedFromQuantification == "False" & 
            !is.na(raw$PG.Qvalue) & (raw$PG.Qvalue < 0.01) &
            !is.na(raw$EG.Qvalue) & (raw$EG.Qvalue < 0.01)

raw <- raw[selected,]

Normalize the data, create a protein list, and perform the MaxLFQ algorithm

sample_id  <- "R.FileName" 

secondary_id <- c("EG.Library", "FG.Id", "FG.Charge", "F.FrgIon", "F.Charge", "F.FrgLossType")

norm_data <- iq::preprocess(raw, 
                            sample_id  = sample_id, 
                            secondary_id = secondary_id)

protein_list <- iq::create_protein_list(norm_data)

result <- iq::create_protein_table(protein_list)

Extract annotation columns and write the result to an output file

annotation_columns <- c("PG.Genes", "PG.ProteinNames")

extra_names <- iq::extract_annotation(rownames(result$estimate), 
                                      raw, 
                                      annotation_columns = annotation_columns)

write.table(cbind(Protein = rownames(result$estimate),
                  extra_names[, annotation_columns],
                  MaxLFQ_annotation = result$annotation,
                  result$estimate),
            "iq-MaxLFQ.txt", sep = "\t", row.names = FALSE)

The resulting file iq-MaxLFQ.txt is the protein level quantification report in a tab-deliminated text format.

A faster MaxLFQ implementation

The function iq::fast_MaxLFQ implemented in C++ combines the functionalities of iq::create_protein_list and iq::create_protein_table.

#--------------------- Replacing ---------------------
# protein_list <- iq::create_protein_list(norm_data) #
# result <- iq::create_protein_table(protein_list)   #
#-----------------------------------------------------

result_faster <- iq::fast_MaxLFQ(norm_data)

The results of the R implementation result and C++ implementation result_faster should be equal up to the floating-point precision of the underlying numerical libraries.

cat("Max difference =", max(abs(result_faster$estimate - result$estimate), na.rm = TRUE), "\n")

cat("Identical NAs =", identical(is.na(result_faster$estimate), is.na(result$estimate)), "\n")

cat("Equal annotation =", identical(result_faster$annotation, result$annotation), "\n")

Benchmarking execution time

We can check the improvement in execution time. The following result is obtained on a computer with 12 CPU cores.

system.time({
    protein_list <- iq::create_protein_list(norm_data)
    result <- iq::create_protein_table(protein_list)
})

system.time({
    result_faster <- iq::fast_MaxLFQ(norm_data)
})

An efficient data structure and data loading

We have implemented a fast data loading algorithm and an efficient data structure. The memory usage is highly optimized to enable the processing of very large datasets.

sample_id  <- "R.FileName" 

secondary_id <- c("EG.Library", "FG.Id", "FG.Charge", "F.FrgIon", "F.Charge", "F.FrgLossType")

annotation_columns <- c("PG.Genes", "PG.ProteinNames")

iq_dat <- iq::fast_read("DIA-report-long-format.txt",
                        sample_id  = sample_id, 
                        secondary_id = secondary_id,
                        filter_string_equal = c("F.ExcludedFromQuantification" = "False"),
                        annotation_col = annotation_columns)

iq_norm_data <- iq::fast_preprocess(iq_dat$quant_table)

result_fastest <- iq::fast_MaxLFQ(iq_norm_data, 
                                  row_names = iq_dat$protein[, 1], 
                                  col_names = iq_dat$sample)

The result of the optimized pipeline result_fastest should be the same as that of the standard pipeline result.

cat("Max difference =", max(abs(result_fastest$estimate - result$estimate), na.rm = TRUE), "\n")

cat("Identical NAs =", identical(is.na(result_fastest$estimate), is.na(result$estimate)), "\n")

cat("Equal annotation =", identical(result_fastest$annotation, result$annotation), "\n")

The annotation columns are stored in the protein component of the input data structure iq_dat. We can extract the annotation columns and write the result to an output text file.

iq_extra_names <- iq::extract_annotation(rownames(result_fastest$estimate), 
                                         iq_dat$protein, 
                                         annotation_columns = annotation_columns)

write.table(cbind(Protein = rownames(result_fastest$estimate),
                  iq_extra_names[, annotation_columns],
                  MaxLFQ_annotation = result_fastest$annotation,
                  result_fastest$estimate), 
            "iq-MaxLFQ-fast.txt", sep = "\t", row.names = FALSE)

Benchmarking execution time

sample_id  <- "R.FileName" 

secondary_id <- c("EG.Library", "FG.Id", "FG.Charge", "F.FrgIon", "F.Charge", "F.FrgLossType")

annotation_columns <- c("PG.Genes", "PG.ProteinNames")

system.time({

    # reading data
    raw <- read.delim("DIA-report-long-format.txt")

    # filtering
    selected <- raw$F.ExcludedFromQuantification == "False" & 
                !is.na(raw$PG.Qvalue) & raw$PG.Qvalue < 0.01 &
                !is.na(raw$EG.Qvalue) & raw$EG.Qvalue < 0.01

    raw <- raw[selected,]

    ## process

    norm_data <- iq::preprocess(raw, 
                                sample_id  = sample_id, 
                                secondary_id = secondary_id)

    protein_list <- iq::create_protein_list(norm_data)

    result <- iq::create_protein_table(protein_list)

})

system.time({
    iq_dat <- iq::fast_read("DIA-report-long-format.txt",
                            sample_id  = sample_id, 
                            secondary_id = secondary_id,
                            filter_string_equal = c("F.ExcludedFromQuantification" = "False"),
                            annotation_col = annotation_columns)

    iq_norm_data <- iq::fast_preprocess(iq_dat$quant_table)

    result_fastest <- iq::fast_MaxLFQ(iq_norm_data, 
                                      row_names = iq_dat$protein[, 1], 
                                      col_names = iq_dat$sample)
})

References

  1. Pham TV, Henneman AA, Jimenez CR (2020) iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 36(8):2611-2613.


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iq documentation built on Sept. 15, 2021, 9:08 a.m.