Package 'iq'

Title: Protein Quantification in Mass Spectrometry-Based Proteomics
Description: An implementation of the MaxLFQ algorithm by Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive pipeline for processing proteomics data in data-independent acquisition mode (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>; Pham et al. 2026 <doi:10.1021/acs.jproteome.5c01038>). It offers additional options for protein quantification using the N most intense fragment ions, using all fragment ions, the median polish algorithm by Tukey (1977, ISBN:0201076160), and a robust linear model. In general, the tool can be used to integrate multiple proportional observations into a single quantitative value.
Authors: Thang Pham [aut, cre, cph, ctb] , Alex Henneman [ctb]
Maintainer: Thang Pham <[email protected]>
License: BSD_3_clause + file LICENSE
Version: 2.0.1
Built: 2026-05-20 09:43:58 UTC
Source: https://github.com/tvpham/iq

Help Index

Group connected samples

Description

Two samples are directly connected if they share at least one quantified fragment ion. Two samples in a connected component are either directly connected or indirectly via other samples.

Usage

connected_component(X)

Arguments

X

A matrix of intensities in the log2 space. Columns are samples and rows are fragment ions.

Value

A vector with length equal to the number of columns of the input containing membership of the connected components.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

Creating a list of matrices of fragment ion intensities for all proteins

Description

For each protein, a numerical matrix is formed where the columns are samples and rows are fragment ions.

Usage

create_protein_list(preprocessed_data)

Arguments

preprocessed_data

A data frame of four components as output of the preprocess function.

Value

A list where each element contains the quantitative data of a protein. The column names are sample names and the row names fragment ions.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

See Also

preprocess

Examples

data("spikeins")
head(spikeins)
# This example set of spike-in proteins has been 'median-normalized'.
norm_data <- iq::preprocess(spikeins, median_normalization = FALSE, pdf_out = NULL)
protein_list <- iq::create_protein_list(norm_data)

Protein quantification for a list of proteins

Description

Travels through the input list and quantifies all proteins one by one.

Usage

create_protein_table(protein_list, method = "maxLFQ", ...)

Arguments

protein_list

The input protein list
method

Possible values are "maxLFQ", "median_polish", "topN", and "meanInt".
...

Additional parameters for individual quantitation methods.

Value

A list of two components is returned

estimate

A table of protein abundances for all samples in log2 space.
annotation

A vector of annotations, one for each protein.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

See Also

create_protein_list, maxLFQ, median_polish, topN, meanInt

Examples

data("spikeins")
# This example set of spike-in proteins has been 'median-normalized'.
norm_data <- iq::preprocess(spikeins, median_normalization = FALSE, pdf_out = NULL)
protein_list <- iq::create_protein_list(norm_data)
result <- iq::create_protein_table(protein_list)
head(result)

Protein annotation extraction

Description

Extracts annotation columns from a long-format input

Usage

extract_annotation(protein_ids, quant_table, primary_id = "PG.ProteinGroups",
                   annotation_columns = NULL)

Arguments

protein_ids

A vector of protein ids.
quant_table

A long-format input table. The input is typically the same as input to the preprocess function.
primary_id

The column containing protein ids.
annotation_columns

A vector of columns for annotation.

Value

A table of proteins and associated annotation extracted from the input.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

See Also

preprocess

Examples

data("spikeins")
extra_names <- iq::extract_annotation(levels(spikeins$PG.ProteinGroups),
                                      spikeins,
                                      annotation_columns = c("PG.Genes", "PG.ProteinNames"))

The MaxLFQ algorithm

Description

A fast implementation of the MaxLFQ algorithm.

Usage

fast_MaxLFQ(norm_data, row_names = NULL, col_names = NULL)

Arguments

norm_data

A list of four vectors with equal length protein_list, sample_list, id and quant as prepared by the fast_preprocess function or the quant_table component returned by the fast_read function. Note that quant should contain log2 intensities.
row_names

A vector of character strings for row names. If NULL, unique values in the protein_list component of norm_data will be used. Otherwise, it should be the first column of the protein component returned by the fast_read.
col_names

A vector of character strings for column names. If NULL, unique values in the sample_list component of norm_data will be used. Otherwise, it should be the sample component returned by the fast_read.

Value

A list is returned with two components

estimate

A quantification result table in log2 space.
annotation

A vector of strings indicating membership in case of multiple connected components for each row of estimate.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

See Also

fast_read, fast_preprocess

Data filtering and normalization

Description

Filters out low intensities and performs median normalization.

Usage

fast_preprocess(quant_table,
                median_normalization = TRUE,
                log2_intensity_cutoff = 0,
                pdf_out = "qc-plots-fast.pdf",
                pdf_width = 12,
                pdf_height = 8,
                show_boxplot = TRUE)

Arguments

quant_table

The quant_table component as returned by fast_read.

median_normalization

A logical value. The default TRUE value is to perform median normalization.
log2_intensity_cutoff

Entries lower than this value in log2 space are ignored. Plot a histogram of all intensities to set this parameter.
pdf_out

A character string specifying the name of the PDF output. A NULL value will suppress the PDF output.
pdf_width

Width of the pdf output in inches.
pdf_height

Height of the pdf output in inches.
show_boxplot

A logical value. The default TRUE value is to create boxplots of fragment intensities for each sample.

Value

A list is returned with the same components as input data in which low intensities are filtered out and median normalization is performed if requested.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

See Also

fast_read

Reading data from an input file

Description

A highly efficient reading of a tab-separated text file for iq processing.

Usage

fast_read(filename,
          sample_id = "R.Condition",
          primary_id = "PG.ProteinGroups",
          secondary_id = c("EG.ModifiedSequence", "FG.Charge", "F.FrgIon", "F.Charge"),
          intensity_col = "F.PeakArea",
          annotation_col = c("PG.Genes", "PG.ProteinNames"),
          filter_string_equal = c("F.ExcludedFromQuantification" = "False"),
          filter_string_not_equal = NULL,
          filter_double_less = c("PG.Qvalue" = "0.01", "EG.Qvalue" = "0.01"),
          filter_double_greater = NULL,
          intensity_col_sep = NULL,
          intensity_col_id = NULL,
          na_string = "0")

Arguments

filename

A long-format tab-separated text file with a primary column of protein identification, secondary columns of fragment ions, a column of sample names, a column for quantitative intensities, and extra columns for annotation.

primary_id

Unique values in this column form the list of proteins to be quantified.
secondary_id

A concatenation of these columns determines the fragment ions used for quantification.
sample_id

Unique values in this column form the list of samples.
intensity_col

The column for intensities.
annotation_col

Annotation columns
filter_string_equal

A named vector of strings. Only rows satisfying the condition are kept.
filter_string_not_equal

A named vector of strings. Only rows satisfying the condition are kept.
filter_double_less

A named vector of strings. Only rows satisfying the condition are kept. Default PG.Qvalue < 0.01 and EG.Qvalue < 0.01.
filter_double_greater

A named vector of strings. Only rows satisfying the condition are kept.
intensity_col_sep

A separator character when entries in the intensity column contain multiple values.
intensity_col_id

The column for identities of multiple quantitative values.
na_string

The value considered as NA.

Details

When entries in the intensity column contain multiple values, this function will replicate entries in other column and the secondary_id will be appended with corresponding entries in intensity_col_id when it is provided. Otherwise, integer values 1, 2, 3, etc... will be used.

Value

A list is returned with following components

protein

A table of proteins in the first column followed by annotation columns.
sample

A vector of samples.
ion

A vector of fragment ions to be used for quantification.
quant_table

A list of four components: protein_list (index pointing to protein)), sample_list (index pointing to sample), id (index pointing to ion), and quant (intensities).

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

Long format input data to iq format

Description

This function reads long-format input data, filters it, and writes the results to the iq format.

Usage

long_format_to_iq_format(input_data, 
                         output_data,
                         sample_id = "Run",
                         primary_id = "Protein.Group",
                         secondary_id = c("Precursor.Id"),
                         intensity_col = "Intensities",
                         annotation_col = NULL,
                         filter_string_equal = NULL,
                         filter_string_not_equal = NULL,
                         filter_double_less = NULL,
                         filter_double_greater = NULL,
                         intensity_col_sep = ";",
                         intensity_col_id = NULL, 
                         na_string = "0",
                         normalization = "median",
                         log2_intensity_cutoff = 0,
                         pdf_out = "qc-plots-iq.pdf",
                         pdf_width = 12,
                         pdf_height = 8,
                         show_boxplot = FALSE)

Arguments

input_data

See filename in fast_read.
output_data

Output data.
sample_id

See sample_id in fast_read.
primary_id

See primary_id in fast_read.
secondary_id

See secondary_id in fast_read.
intensity_col

See intensity_col in fast_read.
annotation_col

See annotation_col in fast_read.
filter_string_equal

See filter_string_equal in fast_read.
filter_string_not_equal

See filter_string_not_equal in fast_read.
filter_double_less

See filter_double_less in fast_read.
filter_double_greater

See filter_double_greater in fast_read.
intensity_col_sep

See intensity_col_sep in fast_read.
intensity_col_id

See intensity_col_id in fast_read.
na_string

See intensity_col_id in fast_read.
normalization

Normalization type. Possible values are median and none. The default value median is for median normalization in fast_preprocess.
log2_intensity_cutoff

See log2_intensity_cutoff in fast_preprocess.
pdf_out

See pdf_out in fast_preprocess.
pdf_width

See pdf_width in fast_preprocess.
pdf_height

See pdf_height in fast_preprocess.
show_boxplot

See show_boxplot in fast_preprocess.

Value

The output is written to a new directory named output_data.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

See Also

fast_read, fast_preprocess

The MaxLFQ algorithm for protein quantification

Description

Estimates protein abundances by aiming to maintain the fragment intensity ratios between samples.

Usage

maxLFQ(X)

Arguments

X

A matrix of ion intensities in log2 space. Columns are samples and rows are fragment ions.

Value

A list of two components is returned

estimate

A vector with length equal to the number of columns of the input containing the protein abundances in log2 space.
annotation

An empty string if all quantified samples are connected. Otherwise, a string of membership of the connected components is returned.

Author(s)

Thang V. Pham

References

Cox J, Hein MY, Luber CA, et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics. 2014;13(9):2513–2526.

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

The meanInt algorithm for protein quantification

Description

Estimates protein abundances by averaging all associated ion intensities

Usage

meanInt(X, aggregation_in_log_space = TRUE)

Arguments

X

A matrix of ion intensities in log2 space. Columns are samples and rows are fragment ions.
aggregation_in_log_space

A logical value. If FALSE, the data aggregation is performed in the original intensity space.

Value

A list of two components is returned

estimate

A vector with length equal to the number of columns of the input containing the protein abundances in log2 space.
annotation

Reserved, currently an empty string.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

A wrapper for the R implementation of the median polish algorithm

Description

Estimates protein abundances using the Tukey median polish algorithm.

Usage

median_polish(X)

Arguments

X

A matrix of ion intensities in log2 space. Columns are samples and rows are fragment ions.

Value

A list of two components is returned

estimate

A vector with length equal to the number of columns of the input containing the protein abundances in log2 space.
annotation

Reserved, currently an empty string

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

Tukey JW. Exploratory Data Analysis, Reading Massachusetts: Addison-Wesley, 1977.

Plotting the underlying quantitative data for a protein

Description

Displays the underlying data for a protein.

Usage

plot_protein(X, main = "", col = NULL, split = 0.6, ...)

Arguments

X

Protein data matrix.
main

Title of the plot.
col

Colors of the rows of the data matrix.
split

Fraction of the plotting area for the main figure. The remaining one is for legend. Set this parameter to NULL to ignore the legend area.
...

Additional parameters for plotting.

Value

A NULL value is returned.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

Examples

data("spikeins")
head(spikeins)
# This example set of spike-in proteins has been 'median-normalized'.
norm_data <- iq::preprocess(spikeins, median_normalization = FALSE, pdf_out = NULL)
protein_list <- iq::create_protein_list(norm_data)
iq::plot_protein(protein_list$P00366, main = "Protein P00366", split = NULL)

Data preprocessing for protein quantification

Description

Prepares a long-format input including removing low-intensity ions and performing median normalization.

Usage

preprocess(quant_table,
           primary_id = "PG.ProteinGroups",
           secondary_id = c("EG.ModifiedSequence", "FG.Charge", "F.FrgIon", "F.Charge"),
           sample_id = "R.Condition",
           intensity_col = "F.PeakArea",
           median_normalization = TRUE,
           log2_intensity_cutoff = 0,
           pdf_out = "qc-plots.pdf",
           pdf_width = 12,
           pdf_height = 8,
           intensity_col_sep = NULL,
           intensity_col_id = NULL,
           na_string = "0",
           show_boxplot = TRUE)

Arguments

quant_table

A long-format table with a primary column of protein identification, secondary columns of fragment ions, a column of sample names, and a column for quantitative intensities.

primary_id

Unique values in this column form the list of proteins to be quantified.
secondary_id

A concatenation of these columns determines the fragment ions used for quantification.
sample_id

Unique values in this column form the list of samples.
intensity_col

The column for intensities.
median_normalization

A logical value. The default TRUE value is to perform median normalization.
log2_intensity_cutoff

Entries lower than this value in log2 space are ignored. Plot a histogram of all intensities to set this parameter.
pdf_out

A character string specifying the name of the PDF output. A NULL value will suppress the PDF output.
pdf_width

Width of the pdf output in inches.
pdf_height

Height of the pdf output in inches.
intensity_col_sep

A separator character when entries in the intensity column contain multiple values.
intensity_col_id

The column for identities of multiple quantitative values.
na_string

The value considered as NA.
show_boxplot

A logical value. The default TRUE value is to create boxplots of fragment intensities for each sample.

Details

When entries in the intensity column contain multiple values, this function will replicate entries in other column and the secondary_id will be appended with corresponding entries in intensity_col_id when it is provided. Otherwise, integer values 1, 2, 3, etc... will be used.

Value

A data frame is returned with following components

protein_list

A vector of proteins.
sample_list

A vector of samples.
id

A vector of fragment ions to be used for quantification.
quant

A vector of log2 intensities.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

Examples

data("spikeins")
head(spikeins)
# This example set of spike-in proteins has been 'median-normalized'.
norm_data <- iq::preprocess(spikeins, median_normalization = FALSE, pdf_out = NULL)

Process data in the iq format

Description

Estimates protein abundance of all proteins in the iq format data.

Usage

process_iq_format(input_data,
                  output_filename = NULL,
                  method = "maxlfq_bit", 
                  p1 = NULL, p2 = NULL, k = 1.345, min_M = 15, n_threads = -1,
                  rescale_method = NULL)

Arguments

input_data

Input data in the iq format.
output_filename

Output file name. If NULL, a file name derived from input_data will be used.
method

See method in process_matrix.
p1

See p1 in process_matrix.
p2

See p2 in process_matrix.
k

See k in process_matrix.
min_M

See min_M in process_matrix.
n_threads

See n_threads in process_matrix.
rescale_method

Rescale the output intensities. See method in rescale.

Value

A tab-separated table with rows representing proteins and columns representing protein annotations and samples.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

Long format to a wide format table using the MaxLFQ algorithm

Description

A convenient function combining multiple steps to process a long format table using the MaxLFQ algorithm.

Usage

process_long_format(input_data,
                    output_filename,
                    sample_id = "File.Name",
                    primary_id = "Protein.Group",
                    secondary_id = "Precursor.Id",
                    intensity_col = "Fragment.Quant.Corrected",
                    annotation_col = NULL,
                    filter_string_equal = NULL,
                    filter_string_not_equal = NULL,
                    filter_double_less = c("Q.Value" = "0.01", "PG.Q.Value" = "0.01"),
                    filter_double_greater = NULL,
                    intensity_col_sep = ";",
                    intensity_col_id = NULL,
                    na_string = "0",
                    normalization = "median",
                    log2_intensity_cutoff = 0,
                    pdf_out = "qc-plots.pdf",
                    pdf_width = 12,
                    pdf_height = 8,
                    show_boxplot = TRUE,
                    peptide_extractor = NULL,
                    rfasta = NULL)

Arguments

input_data

A data frame or a filename. See filename in fast_read.
output_filename

Output filename.
sample_id

See sample_id in fast_read.
primary_id

See primary_id in fast_read.
secondary_id

See secondary_id in fast_read.
intensity_col

See intensity_col in fast_read.
annotation_col

See annotation_col in fast_read.
filter_string_equal

See filter_string_equal in fast_read.
filter_string_not_equal

See filter_string_not_equal in fast_read.
filter_double_less

See filter_double_less in fast_read.
filter_double_greater

See filter_double_greater in fast_read.
intensity_col_sep

See intensity_col_sep in fast_read.
intensity_col_id

See intensity_col_id in fast_read.
na_string

See intensity_col_id in fast_read.
normalization

Normalization type. Possible values are median and none. The default value median is for median normalization in fast_preprocess.
log2_intensity_cutoff

See log2_intensity_cutoff in fast_preprocess.
pdf_out

See pdf_out in fast_preprocess.
pdf_width

See pdf_width in fast_preprocess.
pdf_height

See pdf_height in fast_preprocess.
show_boxplot

See show_boxplot in fast_preprocess.
peptide_extractor

A function to parse peptides
rfasta

If available, calculates the sequence coverage.

Value

Either an input data frame is processed with fast_MaxLFQ or an input file is processed with fast_read, fast_preprocess, and fast_MaxLFQ. Subsequently, the result is written to output_filename. The quantification values are in log2 space. A NULL value is returned. If peptide_extractor is not NULL, fragment statistics for each protein will be calculated based on the result of the extractor function. Counting the number of peptides contributing to a protein is possible using an appropriate extractor function. An example value for peptide_extractor is function(x) gsub("[0-9].*$", "", x), which removes the charge state and fragment descriptors in an ion descriptor to obtain unique peptide sequences. One can examine the ion component returned by the fast_read function to derive a regular expression to be used in the gsub function above. Another example is function(x) gsub("\(UniMod:\d+\)|\d|_", "", x) which removes annotations for modifications on the peptides.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

See Also

fast_read, fast_preprocess, fast_MaxLFQ

Process individual matrix

Description

Estimates a row vector best representing all rows in the input numeric matrix.

Usage

process_matrix(X, 
               method, 
               p1 = NULL, p2 = NULL, k = 1.345, min_M = 15, n_threads = -1)

Arguments

X

A matrix of intensities in the log2 space. Columns are samples and rows are fragment ions.
method

One of the quantification methods: "maxlfq", "maxlfq_bit", "weighted_maxlfq", "median_polish", "weighted_median_polish", "rlm", or "weighted_rlm".
p1

Method parameter. For "rlm" and "weighted_rlm", it is the robust parameter. For "median_polish" and "weighted_median_polish", it is the convergence parameter. Unused in other cases.
p2

Method parameter. For "maxlfq_bit", it specifies the memory level. For others, it is the maximum number of optimization rounds.
k

Weighting function parameter.
min_M

Minimum number of rows for the weighted methods.
n_threads

The number of threads to be used. When n_threads is 0, the maximal number of CPU cores is used. When n_threads is -1 (default), one CPU core less than the maximum is used, and so on.

Value

A numeric row vector.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

Merging rows with identical values in a particular column in a table

Description

Collapses rows with identical values in a particular column in a table. When the values in each row are proportional such as intensities of multiple fragments of a protein, the MaxLFQ algorithm is recommended.

Usage

process_wide_format(input_filename,
                    output_filename,
                    id_column,
                    quant_columns,
                    data_in_log_space = FALSE,
                    annotation_columns = NULL,
                    method = "maxLFQ")

Arguments

input_filename

Input filename of a tab-separated value text file.
output_filename

Output filename.
id_column

The column where unique values will be kept. Rows with identical values in this column are merged. Rows with empty values here are removed.
quant_columns

Columns containing numerical data to be merged.
data_in_log_space

A logical value. If FALSE, the numerical data will be log2-transformed.
annotation_columns

Columns in the input file apart from id_column and quant_columns that will be kept in the output.
method

Method for merging. Default value is "maxLFQ". Possible values are "maxLFQ", "maxLFQ_R", "median_polish", "top3", "top5", "meanInt", "maxInt", "sum", "least_na" and any function for collapsing a numerical matrix to a row vector.

Details

Method "maxLFQ_R" implements the MaxLFQ algorithm pure R. It is slower than "maxLFQ".

Method "maxInt" selects row with maximum intensity (top 1).

Method "sum" sum all intensities.

Method "least_na" selects row with the least number of missing values.

The value of method can be a function such as function(x) log2(colSums(2^x, na.rm = TRUE)) for summing all intensities in the original space.

Value

The result table is written to output_filename. A NULL value is returned.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

Rescale relative quantification

Description

Each connected component of the input values can be rescaled to match the level of the overall input intensities.

Usage

rescale(x, X, method)

Arguments

x

A row vector containing the quantification result.
X

An input matrix of log2-transformed intensities. Columns represent samples, and rows represent fragment ions.
method

One of the methods for rescaling: "median-mean", "median-median", "mean-mean" or "sum".

Value

A row vector of rescaled values of the same size as x, with each connected component rescaled independently. For the "median-mean" method, single-sample components are rescaled to the median of input data, while all other components are rescaled to the mean. For other methods, each component is rescaled to the median, mean, or sum of the input data.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

An example dataset of 12 spike-in proteins

Description

A subset of the Bruderer 2015 dataset containing 12 spike-in proteins. The full dataset was exported from the Spectronaut software. The complete dataset has been median-normalized.

Usage

data("spikeins")

Format

A data frame with 18189 observations on the following 9 variables.

R.Condition

Sample names.

PG.ProteinGroups

Protein identifiers.

EG.ModifiedSequence

Sequence of the fragment ions.

FG.Charge

Fragment group charge.

F.FrgIon

Fragment ions.

F.Charge

Fragment charges.

F.PeakArea

Quantitative values.

PG.Genes

Gene names.

PG.ProteinNames

Protein names.

Examples

data("spikeins")
head(spikeins)

The topN algorithm for protein quantification

Description

Estimates protein abundances using the N most intense ions.

Usage

topN(X, N = 3, aggregation_in_log_space = TRUE)

Arguments

X

A matrix of ion intensities in log2 space. Columns are samples and rows are fragment ions.
N

The number of top ions used for quantification.
aggregation_in_log_space

A logical value. If FALSE, data aggregation is performed in the original intensity space.

Value

A list of two components is returned

estimate

A vector with length equal to the number of columns of the input containing the protein abundances in log2 space.
annotation

Reserved, currently an empty string.

Author(s)

Thang V. Pham

References

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.