Fast MaxLFQ

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Fast MaxLFQ

Thang V Pham

2023-12-06

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)
#> Concatenating secondary ids...
#> Removing low intensities...
#> Barplotting raw data ...
#> Median normalization ...
#> Barplotting after normalization ...

protein_list <- iq::create_protein_list(norm_data)
#> # proteins = 3554, # samples = 24
#> 5%
#> 10%
#> 15%
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#> 95%
#> Completed.

result <- iq::create_protein_table(protein_list)
#> 5%
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#> Completed.

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)
#> nrow = 3369557, # proteins = 3554, # samples = 24
#> Using 11 threads...
#> 0%
#> 5%
#> 10%
#> 16%
#> 21%
#> 26%
#> 32%
#> 37%
#> 43%
#> 48%
#> 53%
#> 58%
#> 64%
#> 69%
#> 74%
#> 79%
#> 85%
#> 90%
#> 95%
#> Completed.

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")
#> Max difference = 1.225686e-13

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

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

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)
})
#> # proteins = 3554, # samples = 24
#> 5%
#> 10%
#> 15%
#> 20%
#> 25%
#> 30%
#> 35%
#> 40%
#> 45%
#> 50%
#> 55%
#> 60%
#> 65%
#> 70%
#> 75%
#> 80%
#> 85%
#> 90%
#> 95%
#> Completed.
#> 5%
#> 10%
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#> 20%
#> 25%
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#> 35%
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#> 45%
#> 50%
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#> 60%
#> 65%
#> 70%
#> 75%
#> 80%
#> 85%
#> 90%
#> 95%
#> Completed.
#>    user  system elapsed 
#>  584.83   18.31  603.19

system.time({
    result_faster <- iq::fast_MaxLFQ(norm_data)
})
#> nrow = 3369557, # proteins = 3554, # samples = 24
#> Using 11 threads...
#> 0%
#> 6%
#> 11%
#> 16%
#> 22%
#> 27%
#> 32%
#> 37%
#> 43%
#> 48%
#> 53%
#> 58%
#> 64%
#> 69%
#> 75%
#> 80%
#> 86%
#> 92%
#> 98%
#> Completed.
#>    user  system elapsed 
#>    3.95    0.08    1.98

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)
#> 
#> Command: --sample R.FileName --primary PG.ProteinGroups --secondary EG.Library FG.Id FG.Charge F.FrgIon F.Charge F.FrgLossType --quant F.PeakArea --annotation PG.Genes PG.ProteinNames --filter-string-equal F.ExcludedFromQuantification False --filter-double-less PG.Qvalue 0.01 --filter-double-less EG.Qvalue 0.01 DIA-report-long-format.txt 
#> 
#> Sample column:
#>     R.FileName
#> Protein column:
#>     PG.ProteinGroups
#> Ion column(s):
#>     EG.Library FG.Id FG.Charge F.FrgIon F.Charge F.FrgLossType
#> Quant column:
#>     F.PeakArea
#> Annotation column(s):
#>     PG.Genes PG.ProteinNames
#> String equal filter(s):
#>     F.ExcludedFromQuantification == False
#> Double less filter(s):
#>     PG.Qvalue < 0.010000
#>     EG.Qvalue < 0.010000
#> 
#> Using 4 threads ...
#> 20 samples read
#> 
#> # lines read (excluding headers)      = 5547331
#> # quantitative values after filtering = 3390569
#> 
#> # samples  = 24
#> # proteins = 3554

iq_norm_data <- iq::fast_preprocess(iq_dat$quant_table)
#> Removing low intensities...
#> Barplotting raw data ...
#> Median normalization ...
#> Barplotting after normalization ...

result_fastest <- iq::fast_MaxLFQ(iq_norm_data, 
                                  row_names = iq_dat$protein[, 1], 
                                  col_names = iq_dat$sample)
#> nrow = 3369557, # proteins = 3554, # samples = 24
#> Using 11 threads...
#> 0%
#> 5%
#> 10%
#> 15%
#> 26%
#> 35%
#> 40%
#> 46%
#> 51%
#> 57%
#> 64%
#> 69%
#> 75%
#> 80%
#> 85%
#> 91%
#> 98%
#> Completed.

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")
#> Max difference = 1.136868e-13

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

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

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)
    
})
#> Concatenating secondary ids...
#> Removing low intensities...
#> Barplotting raw data ...
#> Median normalization ...
#> Barplotting after normalization ...
#> # proteins = 3554, # samples = 24
#> 5%
#> 10%
#> 15%
#> 20%
#> 25%
#> 30%
#> 35%
#> 40%
#> 45%
#> 50%
#> 55%
#> 60%
#> 65%
#> 70%
#> 75%
#> 80%
#> 85%
#> 90%
#> 95%
#> Completed.
#> 5%
#> 10%
#> 15%
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#> 25%
#> 30%
#> 35%
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#> 45%
#> 50%
#> 55%
#> 60%
#> 65%
#> 70%
#> 75%
#> 80%
#> 85%
#> 90%
#> 95%
#> Completed.
#>    user  system elapsed 
#>  826.36   31.81  858.46

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)
})
#> 
#> Command: --sample R.FileName --primary PG.ProteinGroups --secondary EG.Library FG.Id FG.Charge F.FrgIon F.Charge F.FrgLossType --quant F.PeakArea --annotation PG.Genes PG.ProteinNames --filter-string-equal F.ExcludedFromQuantification False --filter-double-less PG.Qvalue 0.01 --filter-double-less EG.Qvalue 0.01 DIA-report-long-format.txt 
#> 
#> Sample column:
#>     R.FileName
#> Protein column:
#>     PG.ProteinGroups
#> Ion column(s):
#>     EG.Library FG.Id FG.Charge F.FrgIon F.Charge F.FrgLossType
#> Quant column:
#>     F.PeakArea
#> Annotation column(s):
#>     PG.Genes PG.ProteinNames
#> String equal filter(s):
#>     F.ExcludedFromQuantification == False
#> Double less filter(s):
#>     PG.Qvalue < 0.010000
#>     EG.Qvalue < 0.010000
#> 
#> Using 4 threads ...
#> 20 samples read
#> 
#> # lines read (excluding headers)      = 5547331
#> # quantitative values after filtering = 3390569
#> 
#> # samples  = 24
#> # proteins = 3554
#> Removing low intensities...
#> Barplotting raw data ...
#> Median normalization ...
#> Barplotting after normalization ...
#> nrow = 3369557, # proteins = 3554, # samples = 24
#> Using 11 threads...
#> 0%
#> 5%
#> 10%
#> 15%
#> 21%
#> 26%
#> 32%
#> 38%
#> 43%
#> 49%
#> 54%
#> 60%
#> 67%
#> 73%
#> 79%
#> 84%
#> 89%
#> 95%
#> Completed.
#>    user  system elapsed 
#>   30.78    2.48   17.55

References

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.