R/3.2_read_proteingroups.R
In bhagwataditya/importomics: Unified statistal Modeling of Omics Data
Defines functions
read_maxquant_phosphosites
read_proteingroups
read_maxquant_proteingroups
is_file
label2index
dequantify
mqdt_to_mat
drop_differing_uniprots
.read_maxquant_phosphosites
.read_maxquant_proteingroups
un_int64
guess_maxquant_quantity
Documented in
dequantify
guess_maxquant_quantity
is_file
label2index
.read_maxquant_phosphosites
read_maxquant_phosphosites
.read_maxquant_proteingroups
read_maxquant_proteingroups
read_proteingroups
#---------------------------------------------------------------------------
#
# guess_maxquant_quantity
#
#---------------------------------------------------------------------------
#' maxquant quantity patterns
#' @examples
#' MAXQUANT_PATTERNS
#' @export
MAXQUANT_PATTERNS <- c(
`normalizedratio` = '^Ratio ([HM]/[ML]) normalized (.+)$',
`ratio` = '^Ratio ([HM]/[ML]) (?!count|type|variability|iso-count|normalized)(.+)',
`correctedreporterintensity` = '^Reporter intensity corrected ([0-9]+) (.+)$',
`reporterintensity` = '^Reporter intensity ([0-9]+) (.+)$',
`maxlfq` = '^LFQ intensity ([HML] )? ?(.+)$',
`labeledintensity` = '^Intensity ([HML]) (.+)$',
`intensity` = '^Intensity (.+)$'
)
#' Guess maxquant quantity from snames
#'
#' @param x character vector
#' @return string: value from names(MAXQUANT_PATTERNS)
#' @examples
#' # file
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' guess_maxquant_quantity(file)
#'
#' # character vector
#' x <- "Ratio M/L normalized STD(L)_E00(M)_E01(H)_R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "Ratio M/L STD(L)_E00(M)_E01(H)_R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "LFQ intensity E00.R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "Reporter intensity corrected 0 STD(0)E00(1)E01(2)_R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "Reporter intensity 0 STD(0)E00(1)E01(2)_R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "Intensity H STD(L)_E00(M)_E01(H)_R1"
#' guess_maxquant_quantity(x)
#'
#' @export
guess_maxquant_quantity <- function(x){
# Assert
assert_is_character(x)
# read if x is filename
if (is_scalar(x)){
if (is_existing_file(x)){
x <- names(fread(x, header = TRUE, nrows = 1))
}
}
# guess from character vector
for (quantity in names(MAXQUANT_PATTERNS)){
pattern <- MAXQUANT_PATTERNS[[quantity]]
if (any(stri_detect_regex(x, pattern))) return(quantity)
}
stop('quantity could not be infered')
}
#---------------------------------------------------------------------------
#
# .read_maxquant_proteingroups
# .read_maxquant_phosphosites
#
#---------------------------------------------------------------------------
utils::globalVariables('where')
un_int64 <- function(x) {
dplyr::mutate(x, dplyr::across(where(bit64::is.integer64), as.numeric))
}
#' Read proteingroups/phosphosites as-is
#' @param file proteingroups / phosphosites file
#' @param profile proteingroups file
#' @param quantity string
#' @param verbose TRUE / FALSE
#' @return data.table
#' @examples
#' profile <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
#' fosfile <- system.file('extdata/billing19.phosphosites.txt', package = 'autonomics')
#' prodt <- .read_maxquant_proteingroups(file = profile)
#' fosdt <- .read_maxquant_phosphosites( file = fosfile, profile = profile)
#' @export
.read_maxquant_proteingroups <- function(file, quantity = guess_maxquant_quantity(file), verbose = TRUE){
# Assert
assert_maxquant_proteingroups(file)
assert_is_subset(quantity, names(MAXQUANT_PATTERNS))
# Read
if (verbose) cmessage('%sRead%sproteingroups %s', spaces(4), spaces(6), file)
prodt <- fread(file, colClasses = c(id = 'character'), integer64 = 'numeric')
prodt %<>% un_int64()
# prodt[Reverse == '+', `Majority protein IDs` := split_extract_fixed(`Majority protein IDs`, ';', 1)]
# In FOS `Proteins` column is empty for Reverse, so `Protein` (singular!) is copied over
# In PRO therefore take leading protein only for naming identity with FOS.
# Upon further insight naming parity is probably not so important
# So dont unnecessarily modify
# Extract relevant columns
pattern <- MAXQUANT_PATTERNS[[quantity]] # the proteins column is empty in (only) reverse
anncols <- c('id', 'Majority protein IDs', 'Reverse',
'Potential contaminant', 'Contaminant', 'Fasta headers')#, 'Phospho (STY) site IDs')
anncols %<>% intersect(names(prodt))
valcols <- grep(pattern, names(prodt), value = TRUE)
pepcols <- grep('Razor + unique peptides ', names(prodt), fixed = TRUE, value = TRUE)
prodt %<>% extract(, c(anncols, pepcols, valcols), with = FALSE)
digits <- ceiling(log10(nrow(prodt)))
# if (verbose) message('\t\t', nrow(prodt), ' proteins, contaminants, reverse')
# Return
names(prodt) %<>% stri_replace_first_fixed( 'Reverse', 'reverse')
names(prodt) %<>% stri_replace_first_fixed( 'Contaminant', 'contaminant') # older MaxQuant
names(prodt) %<>% stri_replace_first_fixed('Potential contaminant', 'contaminant') # newer MaxQuant
setnames(prodt, 'id', 'proId')
setnames(prodt, 'Majority protein IDs', 'uniprot')
prodt[]
}
#' @rdname dot-read_maxquant_proteingroups
#' @export
.read_maxquant_phosphosites <- function(
file, profile, quantity = guess_maxquant_quantity(file), verbose = TRUE
){
# Assert
assert_maxquant_proteingroups(profile)
assert_maxquant_phosphosites(file)
`Protein group IDs` <- Reverse <- Proteins <- Protein <- NULL
# Read
if (verbose) cmessage('%sphosphosites %s', spaces(14), file)
colclasses <- c(id = 'character', `Protein group IDs` = 'character')
fosdt <- fread(file, colClasses = colclasses, integer64 = 'numeric')
fosdt %<>% un_int64()
# Extract relevant columns
fosdt[Reverse == '+', Proteins := Protein] # the proteins column is empty in (only) reverse
pattern <- MAXQUANT_PATTERNS[[quantity]] # this fails the feature_id naming
anncols <- c('id', 'Protein group IDs', 'Proteins',
'Positions within proteins', 'Amino acid',
'Reverse', 'Contaminant', 'Potential contaminant', 'Fasta headers')
anncols %<>% intersect(names(fosdt))
valcols <- grep(pattern, names(fosdt), value = TRUE)
valcols %<>% extract(stri_detect_fixed(., '___1'))
fosdt %<>% extract(, c(anncols, valcols), with = FALSE)
digits <- ceiling(log10(nrow(fosdt)))
if (verbose) message(spaces(38), 'Read ', formatC(nrow(fosdt), digits = digits),
' phosphosites in proteins, contaminants, reverse')
names(fosdt) %<>% stri_replace_first_fixed( 'Reverse', 'reverse')
names(fosdt) %<>% stri_replace_first_fixed( 'Contaminant', 'contaminant') # older MaxQuant
names(fosdt) %<>% stri_replace_first_fixed('Potential contaminant', 'contaminant') # newer MaxQuant
# Filter
fosdt <- fosdt[stri_count_fixed(`Protein group IDs`, ';') == 0]
if (verbose) message(spaces(38), 'Retain ', formatC(nrow(fosdt), digits = digits),
' phosphosites in single proteingroup')
idx <- rowSums(fosdt[, valcols, with = FALSE], na.rm = TRUE) > 0
fosdt <- fosdt[which(idx)]
if (verbose) message(spaces(38), 'Retain ', formatC(nrow(fosdt), digits = digits),
' phosphosites with signal in some sample')
# Rename
names(fosdt) %<>% stri_replace_first_fixed('___1', '')
setnames(fosdt, 'id', 'fosId')
setnames(fosdt, 'Protein group IDs', 'proId')
setnames(fosdt, 'Proteins', 'uniprot')
fosdt[]
}
#---------------------------------------------------------------------------
#
# drop_differing_uniprots
#
#---------------------------------------------------------------------------
drop_differing_uniprots <- function(fosdt, prodt, verbose){
# Assert
contaminant <- fosId <- `Positions within proteins` <- proId <- NULL
reverse <- uniprot <- NULL
# Avoid changing the global env
prodt %<>% copy()
fosdt %<>% copy()
# Extract annotation cols
if (verbose) message(spaces(38), 'Keep proteingroup uniprots')
proanncols <- c('proId', 'uniprot', 'reverse', 'contaminant')
fosanncols <- c('fosId', 'proId', 'uniprot', 'Positions within proteins', 'reverse', 'contaminant')
proanndt <- prodt[, proanncols, with = FALSE]
fosanndt <- fosdt[, fosanncols, with = FALSE]
# Separate contaminants-reverse from other proteins.
conrevdt <- fosanndt[reverse == '+' | contaminant == '+']
fosanndt <- fosanndt[reverse == '' & contaminant == '']
proanndt <- proanndt[reverse == '' & contaminant == '']
fosanndt[, c('reverse', 'contaminant') := NULL]
proanndt[, c('reverse', 'contaminant') := NULL]
conrevdt[, c('reverse', 'contaminant') := NULL]
# Merge
fosanndt %<>% uncollapse(uniprot, `Positions within proteins`, sep = ';')
proanndt %<>% uncollapse(uniprot, sep = ';')
fosanndt %<>% merge(proanndt, by = c('proId', 'uniprot'))
fosanndt %<>% extract(order(as.integer(fosId)))
fosanndt %<>% extract(, lapply(.SD, paste0, collapse = ';'), by = 'fosId')
# Add back contaminants/reverse
fosanndt %<>% rbind(conrevdt)
fosanndt[, proId := NULL]
fosdt[, c('uniprot', 'Positions within proteins') := NULL]
fosdt %<>% merge(fosanndt, by = 'fosId', all.x = TRUE, sort = FALSE)
fosdt %<>% pull_columns(names(fosanndt))
fosdt
}
#---------------------------------------------------------------------------
#
# mqdt_to_mat
# dequantify
# label2index
#
#---------------------------------------------------------------------------
#' Convert maxquant data.table to matrix
#' @param dt data.table
#' @param pattern string
#' @param verbose TRUE / FALSE
#' @return matrix
#' @examples
#' profile <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
#' fastafile <- system.file('extdata/uniprot_hsa_20140515.fasta', package = 'autonomics')
#' prodt <- .read_maxquant_proteingroups(file = profile)
#' uniprothdrs <- read_uniprotdt(fastafile)
#' contaminanthdrs <- read_contaminantdt()
#' maxquanthdrs <- parse_maxquant_hdrs(prodt$`Fasta headers`)
#' prodt %<>% annotate_maxquant(uniprothdrs, contaminanthdrs, maxquanthdrs)
#' quantity <- guess_maxquant_quantity(profile)
#' pattern <- MAXQUANT_PATTERNS[[quantity]]
#' mqdt_to_mat(prodt, pattern = pattern)[1:2, 1:2]
#' @noRd
mqdt_to_mat <- function(dt, pattern, verbose = TRUE){
mat <- dt[, .SD, .SDcols = patterns(pattern)]
mat %<>% data.matrix()
rownames(mat) <- dt$feature_id
mat %<>% zero_to_na(verbose = verbose)
mat %<>% nan_to_na( verbose = verbose)
mat <- log2(mat)
mat
}
#' Dequantify maxquant snames
#'
#' Drop quantity ('Reporter intensity'). \cr
#' Encode {channel} as suffix.
#'
#' ` Ratio H/L WT(L).KD(H).R1 -> WT(L).KD(H).R1{H/L}`
#' ` LFQ intensity WT.R1 -> WT.R1`
#' `Reporter intensity 0 WT(126).KD(127).R1 -> WT(1).KD(2).R1{1}`
#' @param x `character`
#' @param quantity `'ratio', 'normalizedratio'`, \cr
#' `'LFQ intensity'`, \cr
#' `'intensity', 'labeledintensity'`
#' `'reporterintensity', 'correctedreporterintensity'`
#' @param verbose `TRUE` or `FALSE`
#' @return `character`
#' @examples
#' dequantify(c('Ratio H/L WT(L).KD(M).OE(H).R1', # Ratios
#' 'Ratio M/L WT(L).KD(M).OE(H).R1'))
#' dequantify(c('Ratio H/L normalized WT(L).KD(M).OE(H).R1', # Norm. Ratios
#' 'Ratio M/L normalized WT(L).KD(M).OE(H).R1'))
#' dequantify(c('LFQ intensity WT.R1', # LFQ intensity
#' 'LFQ intensity KD.R1'))
#' dequantify(c('Reporter intensity 1 WT(126).KD(127).R1', # Rep.intensities
#' 'Reporter intensity 2 WT(126).KD(127).R1'))
#' @md
#' @export
dequantify <- function(
x, quantity = guess_maxquant_quantity(x), verbose = FALSE
){
# x = multiplex + channel. Return multiplex if single channel.
# Decompose multiplex and channel
pattern <- MAXQUANT_PATTERNS[[quantity]]
if (quantity == 'intensity'){
multiplex <- stri_replace_first_regex(x, pattern, '$1')
channel <- rep('', length(multiplex))
} else {
multiplex <- stri_replace_first_regex(x, pattern, '$2')
channel <- stri_replace_first_regex(x, pattern, '$1')
channel %<>% stri_replace_first_fixed(' ', '')
}
# Reporter intensity
if (quantity %in% c('reporterintensity', 'correctedreporterintensity')){
multiplex %<>% lapply(label2index)
channel %<>% as.numeric()
if (0 %in% channel){ # pre-2018 mq is 0-based
channel %<>% as.numeric() %>% add(1) %>% as.character()
}
}
# Standardize
if (all(channel=='')){
cleanx <- multiplex
} else {
cleanx <- sprintf('%s{%s}', multiplex, channel)
}
if (verbose) cmessage('%sStandardize snames: %s -> %s', spaces(14), x[1], cleanx[1])
return(cleanx)
}
#' Convert labels into indices
#' @param x `character`
#' @examples
#' label2index(x = 'Reporter intensity 0 WT(0).KD(1).OE(2).R1')
#' label2index(x = 'Reporter intensity 1 WT(1).KD(2).OE(3).R1')
#' label2index(x = 'Reporter intensity 0 WT(126).KD(127).OE(128).R1')
#' label2index(x = 'Reporter intensity 1 WT(126).KD(127).OE(128).R1')
#' label2index(x = 'Reporter intensity 1 Mix1')
#' @export
label2index <- function(x){
labels <- unlist(stri_extract_all_regex(x, '\\(.+?\\)'))
if (any(is.na(labels))) return(x)
for (i in rev(seq_along(labels))){
# important to do this in rev order!!! otherwise ...
# Reporter intensity 0 WT(0).KD(1).OE(2).R1
# i=1: (0) -> 1 : Reporter intensity 0 WT(1).KD(1).OE(2).R1
# i=2: (1) -> 2 : Reporter intensity 0 WT(2).KD(1).OE(2).R1
# not what we want!!!
x %<>% stri_replace_first_fixed(labels[[i]], paste0('(',i,')'))
}
x
}
#---------------------------------------------------------------------------
#
# read_maxquant_proteingroups
# read_maxquant_phosphosites
#
#---------------------------------------------------------------------------
#' Is a file?
#'
#' Is a file (and not a dir)
#'
#' This function distinguishies between dir and file.
#' Others dont: is.file, fs::file_exists, assertive::is_existing_file
#' @param file filepath
#' @examples
#' dir <- tempdir(); dir.create(dir, showWarnings = FALSE)
#' file <- tempfile(); invisible(file.create(file))
#' is_file(dir)
#' is_file(file)
#' @export
is_file <- function(file){
file.exists(file) & !dir.exists(file)
}
#' Read maxquant proteingroups
#' @param dir proteingroups directory
#' @param file proteingroups file
#' @param fastafile uniprot fastafile
#' @param restapi TRUE or FALSE : use uniprot restapi to annotate uniprots not in fastadt ?
#' @param quantity 'normalizedratio', 'ratio', 'correctedreporterintensity',
#' 'reporterintensity', 'maxlfq', 'labeledintensity',
#' 'intensity' or NULL
#' @param subgroups NULL or string vector : subgroups to retain
#' @param contaminants TRUE or FALSE : retain contaminants ?
#' @param reverse TRUE or FALSE : include reverse hits ?
#' @param rm_missing_in_all_samples TRUE or FALSE
#' @param invert string vector : subgroups which require inversion
#' @param impute TRUE or FALSE: impute group-specific NA values?
#' @param plot TRUE or FALSE: plot ?
#' @param label fvar
#' @param pca TRUE or FALSE: run pca ?
#' @param pls TRUE or FALSE: run pls ?
#' @param fit model engine: 'limma', 'lm', 'lme(r)', 'wilcoxon' or NULL
#' @param formula model formula
#' @param block model blockvar: string or NULL
#' @param coefs model coefficients of interest: character vector or NULL
#' @param contrasts coefficient contrasts of interest: character vector or NULL
#' @param palette color palette : named character vector
#' @param verbose TRUE or FALSE : message ?
#' @param ... maintain deprecated functions
#' @return SummarizedExperiment
#' @examples
#' # fukuda20 - LFQ
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' pro <- read_maxquant_proteingroups(file = file)
#'
#' # billing19 - Normalized Ratios
#' file <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
#' fastafile <- system.file('extdata/uniprot_hsa_20140515.fasta', package = 'autonomics')
#' subgroups <- sprintf('%s_STD', c('E00', 'E01', 'E02', 'E05', 'E15', 'E30', 'M00'))
#' pro <- read_maxquant_proteingroups(file = file, subgroups = subgroups)
#' pro <- read_maxquant_proteingroups(file = file, fastafile = fastafile, subgroups = subgroups)
#' @export
read_maxquant_proteingroups <- function(
dir = getwd(),
file = if (is_file(dir)) dir else file.path(dir, 'proteinGroups.txt'),
fastafile = NULL,
restapi = FALSE,
quantity = NULL,
subgroups = NULL,
invert = character(0),
contaminants = FALSE,
reverse = FALSE,
rm_missing_in_all_samples = TRUE,
impute = FALSE,
plot = FALSE,
label = 'feature_id',
pca = plot,
pls = plot,
fit = if (plot) 'limma' else NULL,
formula = ~ subgroup,
block = NULL,
coefs = NULL,
contrasts = NULL,
palette = NULL,
verbose = TRUE
){
# Assert
assert_maxquant_proteingroups(file)
if (is.null(quantity)) quantity <- guess_maxquant_quantity(file)
assert_is_a_string(quantity)
assert_is_subset(quantity, names(MAXQUANT_PATTERNS))
assert_is_a_bool(verbose)
`Fasta headers` <- NULL
# Read/Annotate
prodt <- .read_maxquant_proteingroups(file = file, quantity = quantity, verbose = verbose)
uniprothdrs <- read_uniprotdt(fastafile)
contaminanthdrs <- read_contaminantdt()
maxquanthdrs <- parse_maxquant_hdrs(prodt$`Fasta headers`); prodt[, `Fasta headers` := NULL ]
prodt %<>% annotate_maxquant( uniprothdrs = uniprothdrs,
contaminanthdrs = contaminanthdrs,
maxquanthdrs = maxquanthdrs,
restapi = restapi,
verbose = verbose )
# SumExp
if (verbose) cmessage('%sSumExp', spaces(4))
pattern <- MAXQUANT_PATTERNS[[quantity]]
promat <- mqdt_to_mat(prodt, pattern, verbose = verbose)
pepcols <- names(prodt) %>% extract(stri_detect_fixed(., 'eptides'))
pepdt <- prodt[, pepcols, with = FALSE]
prodt %<>% extract(, names(prodt) %>% setdiff(colnames(promat)) %>% setdiff(names(pepdt)), with = FALSE)
object <- list(promat)
names(object) <- paste0('log2', quantity)
object %<>% SummarizedExperiment(rowData = prodt)
# Dequantify. Add pepcounts
object$mqcol <- colnames(object)
colnames(object) %<>% dequantify(quantity = quantity, verbose = verbose)
pepcols <- paste0('Razor + unique peptides ', gsub('\\{.+\\}', '', colnames(object)))
pepmat <- pepdt[, pepcols, with = FALSE ]
pepmat %<>% data.matrix()
dimnames(pepmat) <- dimnames(object)
assays(object)$pepcounts <- pepmat
object %<>% process_maxquant( subgroups = subgroups,
invert = invert,
reverse = reverse,
contaminants = contaminants,
rm_missing_in_all_samples = rm_missing_in_all_samples,
impute = impute,
verbose = verbose )
assays <- c(assayNames(object)[1], 'pepcounts')
for (assay in assays) object %<>% add_assay_means(assay)
object %<>% analyze(
pca = pca, pls = pls,
fit = fit, formula = formula,
block = block, coefs = coefs,
contrasts = contrasts, plot = plot,
label = label,
palette = palette, verbose = verbose )
object
}
#' @rdname read_maxquant_proteingroups
#' @export
read_proteingroups <- function(...){
.Deprecated('read_maxquant_proteingroups')
read_maxquant_proteingroups(...)
}
#' Read maxquant phosphosites
#' @param dir proteingroups directory
#' @param fosfile phosphosites file
#' @param profile proteingroups file
#' @param fastafile uniprot fastafile
#' @param restapi TRUE or FALSE : annotate non-fastadt uniprots using uniprot restapi
#' @param quantity 'normalizedratio', 'ratio', 'correctedreporterintensity',
#' 'reporterintensity', 'maxlfq', 'labeledintensity',
#' 'intensity' or NULL
#' @param subgroups NULL or string vector : subgroups to retain
#' @param contaminants TRUE or FALSE: retain contaminants ?
#' @param reverse TRUE or FALSE: include reverse hits
#' @param rm_missing_in_all_samples TRUE or FALSE
#' @param localization number: min localization probability (for phosphosites)
#' @param invert string vector: subgroups which require inversion
#' @param impute TRUE or FALSE: impute group-specific NA values?
#' @param plot TRUE or FALSE
#' @param label fvar
#' @param pca TRUE or FALSE: run pca ?
#' @param pls TRUE or FALSE: run pls ?
#' @param fit model engine: 'limma', 'lm', 'lme(r)', 'wilcoxon' or NULL
#' @param formula model formula
#' @param block model blockvar: string or NULL
#' @param coefs model coefficients of interest: string vector or NULL
#' @param contrasts model coefficient contrasts of interest: string vector or NULL
#' @param palette color palette: named string vector
#' @param verbose TRUE or FALSE: message ?
#' @param ... maintain deprecated functions
#' @return SummarizedExperiment
#' @examples
#' profile <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
#' fosfile <- system.file('extdata/billing19.phosphosites.txt', package = 'autonomics')
#' fastafile <- system.file('extdata/uniprot_hsa_20140515.fasta', package = 'autonomics')
#' subgroups <- sprintf('%s_STD', c('E00', 'E01', 'E02', 'E05', 'E15', 'E30', 'M00'))
#' pro <- read_maxquant_proteingroups(file = profile, subgroups = subgroups)
#' fos <- read_maxquant_phosphosites( fosfile = fosfile, profile = profile, subgroups = subgroups)
#' fos <- read_maxquant_phosphosites( fosfile = fosfile, profile = profile, fastafile = fastafile, subgroups = subgroups)
#' @export
read_maxquant_phosphosites <- function(
dir = getwd(),
fosfile = if (is_file(dir)) dir else file.path(dir, 'phospho (STY)Sites.txt'),
profile = file.path(dirname(fosfile), 'proteinGroups.txt'),
fastafile = NULL,
restapi = FALSE,
quantity = NULL,
subgroups = NULL,
invert = character(0),
contaminants = FALSE,
reverse = FALSE,
rm_missing_in_all_samples = TRUE,
localization = 0.75,
impute = FALSE,
plot = FALSE,
label = 'feature_id',
pca = plot,
pls = plot,
fit = if (plot) 'limma' else NULL,
formula = ~ subgroup,
block = NULL,
coefs = NULL,
contrasts = NULL,
palette = NULL,
verbose = TRUE
){
# Assert
assert_all_are_existing_files(c(fosfile, profile))
if (is.null(quantity)) quantity <- guess_maxquant_quantity(fosfile)
assert_is_a_string(quantity)
assert_is_subset(quantity, names(MAXQUANT_PATTERNS))
assert_is_a_bool(verbose)
`Fasta headers` <- NULL
# Read
prodt <- .read_maxquant_proteingroups(file = profile, quantity = quantity, verbose = verbose)
fosdt <- .read_maxquant_phosphosites( file = fosfile, quantity = quantity, profile = profile, verbose = verbose)
fosdt %<>% drop_differing_uniprots(prodt, verbose = verbose)
uniprothdrs <- read_uniprotdt(fastafile)
contaminanthdrs <- read_contaminantdt()
maxquanthdrs <- parse_maxquant_hdrs(prodt$`Fasta headers`); fosdt[, `Fasta headers` := NULL ]
fosdt %<>% annotate_maxquant( uniprothdrs = uniprothdrs,
contaminanthdrs = contaminanthdrs,
maxquanthdrs = maxquanthdrs,
restapi = restapi,
verbose = verbose )
prodt %<>% extract(fosdt$proId, on = 'proId')
# SumExp
if (verbose) cmessage('%sSumExp', spaces(4))
pattern <- MAXQUANT_PATTERNS[[quantity]]
promat <- mqdt_to_mat(prodt, pattern, verbose = verbose)
pepcols <- names(prodt) %>% extract(stri_detect_fixed(., 'eptides'))
pepdt <- prodt[, pepcols, with = FALSE]
prodt %<>% extract(, names(prodt) %>% setdiff(colnames(promat)) %>% setdiff(names(pepdt)), with = FALSE)
fosmat <- mqdt_to_mat(fosdt, pattern, verbose = verbose)
fosdt <- fosdt %>% extract(, setdiff(names(.), colnames(fosmat)), with = FALSE)
object <- SummarizedExperiment::SummarizedExperiment(
assays = list(log2sites = fosmat,
log2proteins = promat,
log2diffs = fosmat - promat),
rowData = fosdt)
# Dequantify. Add pepcounts
object$mqcol <- colnames(object)
colnames(object) %<>% dequantify()
pepcols <- paste0('Razor + unique peptides ', gsub('\\{.+\\}', '', colnames(object)))
pepmat <- pepdt[, pepcols, with = FALSE ]
pepmat %<>% data.matrix()
dimnames(pepmat) <- dimnames(object)
assays(object)$pepcounts <- pepmat
# Process / Analyze
object %<>% process_maxquant( subgroups = subgroups,
invert = invert,
reverse = reverse,
contaminants = contaminants,
rm_missing_in_all_samples = rm_missing_in_all_samples,
localization = localization,
impute = impute,
verbose = verbose )
assays <- c('log2sites', 'pepcounts')
for (assay in assays) object %<>% add_assay_means(assay)
object %<>% analyze(
pca = pca, pls = pls,
fit = fit, formula = formula,
block = block, coefs = coefs,
contrasts = contrasts, plot = plot,
label = label,
palette = palette, verbose = verbose )
object
}
#' @rdname read_maxquant_phosphosites
#' @export
read_phosphosites <- function(...){
.Deprecated('read_maxquant_phosphosites')
read_maxquant_phosphosites(...)
}
#-----------------------------------------------------------------------------
#
# invert_subgroups
#
#-----------------------------------------------------------------------------
# x <- c('Ctrl_A', 'Ctrl_B')
# .invert_subgroups(x)
.invert_subgroups <- function(x, sep = guess_sep(x)){
stri_split_fixed(x, sep) %>%
lapply(rev) %>%
vapply(paste, character(1), collapse = sep)
}
#' Invert subgroups
#'
#' Invert expressions , subgroups, and sample ids
#'
#' @param object SummarizedExperiment
#' @param subgroups character vector: subgroup levels to be inversed
#' @param sep string: collapsed string separator
#' @return character vector or SummarizedExperiment
#' @examples
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' object <- read_maxquant_proteingroups(file)
#' invert_subgroups(object)
#' @export
invert_subgroups <- function(
object,
subgroups = slevels(object, 'subgroup'),
sep = guess_sep(object, 'subgroup')
){
# Assert / Msg
if (length(subgroups)==0) return(object)
assert_is_all_of(object, 'SummarizedExperiment')
assert_is_subset('subgroup', svars(object))
assert_is_subset(subgroups, subgroup_levels(object))
idx <- which(object$subgroup %in% subgroups)
first <- values(object)[, idx[1]] %>% (function(y) which(!is.na(y))[[1]])
oldvalue <- values(object)[first, idx[1]] %>% round(2) %>% as.character()
cmessage('%sInvert subgroups %s', spaces(14), paste0(subgroups, collapse = ', '))
# Invert (log) ratios
if (all(values(object)>0, na.rm=TRUE)){ values(object)[, idx] %<>% (function(object){1/object})
} else { values(object)[, idx] %<>% (function(object){ -object})}
newvalue <- as.character(round(values(object)[first, idx[1]], 2))
cmessage('%sexprs : %s -> %s', spaces(21), as.character(oldvalue),
as.character(newvalue))
# Invert subgroup and sampleid values
oldsubgroups <- sdata(object)$subgroup[idx]
newsubgroups <- vapply(oldsubgroups, .invert_subgroups, character(1),sep=sep)
oldsampleids <- sdata(object)$sample_id[idx]
for (i in seq_along(idx)){
sdata(object)$subgroup[ idx[i]] <- newsubgroups[i]
sdata(object)$sample_id[idx[i]] %<>% stri_replace_first_fixed(oldsubgroups[i], newsubgroups[i])
snames(object)[ idx[i]] %<>% stri_replace_first_fixed(oldsubgroups[i], newsubgroups[i])
}
newsampleids <- sdata(object)$sample_id[idx]
cmessage('%ssubgroups: %s -> %s', spaces(21), oldsubgroups[1], newsubgroups[1])
cmessage('%ssampleids: %s -> %s', spaces(21), oldsampleids[1], newsampleids[1])
# Order on subgroup
object %<>% arrange_samples_('subgroup')
cmessage('%sOrder on subgroup', spaces(14))
object$subgroup %<>% droplevels()
# Return
return(object)
}
arrange_samples_ <- function(object, svars){
idx <- do.call(order, sdata(object)[, svars, drop = FALSE])
object %<>% extract(, idx)
return(object)
}
#---------------------------------------------------------------------------
#
# demultiplex
# .is_multiplexed
# .demultiplex
#
#---------------------------------------------------------------------------
#' Demultiplex snames
#'
#' Demultiplex maxquant samplenames
#'
#' `WT(L).KD(H).R1{H/L} -> KD_WT.R1`
#' `WT(1).KD(2).R1{1} -> WT.R1`
#' ` WT.R1 -> WT.R1`
#' @param x character vector
#' @param verbose TRUE or FALSE
#' @return character
#' @examples
#' # uniplexed / intensity / ratio
#' demultiplex(c('KD.R1','OE.R1'))
#' demultiplex(c('WT(L).KD(M).OE(H).R1{M}', 'WT(L).KD(M).OE(H).R1{H}'))
#' demultiplex(c('WT(L).KD(M).OE(H).R1{M/L}','WT(L).KD(M).OE(H).R1{H/L}'))
#' # run / replicate
#' demultiplex(c('WT(L).OE(H).R1{L}', 'WT(L).OE(H).R1{H}')) # run
#' demultiplex(c('WT.R1(L).OE.R1(H){L}', 'WT.R1(L).OE.R1(H){H}')) # repl
#' # label / index
#' demultiplex(c('WT(L).OE(H).R1{L}', 'WT(L).OE(H).R1{H}')) # label
#' demultiplex(c('WT(1).OE(2).R1{1}', 'WT(1).OE(2).R1{2}')) # index
#' # with unused channels
#' demultiplex('WT(1).KD(2).OE(3).R1{6}')
#' @md
#' @export
demultiplex <- function(x, verbose = FALSE){
assert_is_character(x)
assert_is_a_bool(verbose)
. <- NULL
if (!.is_multiplexed(x)) return(x)
y <- unname(vapply(x, .demultiplex, character(1)))
make.unique(y, sep = '-')
}
.is_multiplexed <- function(x){
# Components
pattern <- '(.+)\\{(.+)\\}'
n_open <- stri_count_fixed(x, '(')
n_closed <- stri_count_fixed(x, ')')
channel <- gsub(pattern, '\\2', x)
# Multiplexed consistently ?
y <- all(stri_detect_regex(x, pattern) &
n_open > 0 &
n_open == n_closed)
# All channels defined (TMT) ?
if (all(is_numeric_string(channel))){
channel %<>% as.numeric()
y %<>% and(all(as.numeric(channel) <= n_open))
}
y
}
.demultiplex <- function(y){
y0 <- y
channel <- split_extract_fixed(y, '{', 2) %>% substr(1, nchar(.)-1)
y %<>% stri_replace_last_fixed(paste0('{', channel, '}'), '')
labels <- y %>% stri_extract_all_regex('\\([^()]+\\)') %>% unlist() %>% substr(2, nchar(.)-1)
run <- y %>% stri_split_regex('\\([^[()]]+\\)') %>% unlist() %>% extract(length(.))
biosamples <- y %>% stri_split_regex('\\([^[()]]+\\)') %>% unlist() %>% extract(-length(.))
biosamples %<>% stri_replace_first_regex('^[._ ]', '')
assert_are_same_length(biosamples, labels)
names(biosamples) <- labels
channel %<>% stri_split_fixed('/') %>% unlist()
if (!all(channel %in% labels)) return(y0)
result <- paste0(biosamples[channel], collapse = '_')
result %<>% paste0(run)
result
}
#----------------------------------------------------------------------------
#
# process_maxquant
#
#----------------------------------------------------------------------------
process_maxquant <- function(
object, subgroups, invert, contaminants, reverse, rm_missing_in_all_samples = TRUE,
localization = 0.75, impute, verbose
){
# Demultiplex. Infer Subgroup
contaminant <- `Localization prob` <- NULL
colnames(object) %<>% demultiplex(verbose = verbose)
object$sample_id <- colnames(object)
object %<>% add_subgroup(verbose = verbose)
cols <- c('sample_id', 'subgroup', 'replicate', 'mqcol')
cols %<>% intersect(svars(object))
sdt(object) %<>% pull_columns(cols)
# Samples
object %<>% filter_samples_available_for_some_feature(verbose = verbose)
if (!is.null(subgroups)){
assert_is_subset(subgroups, as.character(object$subgroup))
object %<>% filter_samples(subgroup %in% subgroups, verbose = verbose)
}
object %<>% invert_subgroups(invert)
# Features
analysis(object)$nfeatures <- c(nrow(object))
if (!reverse) object %<>% filter_features( reverse == '', verbose = verbose)
if (!contaminants) object %<>% filter_features(contaminant== '', verbose = verbose)
if ({rm_missing_in_all_samples}) object %<>% rm_missing_in_all_samples(verbose = verbose)
#object %<>% filter_exprs_replicated_in_some_subgroup(verbose = verbose) # doesnt work for single-instance subgroups
if ('Localization prob' %in% fvars(object)){ # subgroup could be increasing concentrations or so
object %<>% filter_features(
`Localization prob` >= localization, verbose = verbose) }
# Impute
if ({{impute}}) object %<>% impute(plot = FALSE)
object
}
#---------------------------------------------------------------------------
#
# log2proteins
#
#---------------------------------------------------------------------------
#' Get/Set log2proteins
#' @param object SummarizedExperiment
#' @param value occupancy matrix (features x samples)
#' @return occpuancy matrix (get) or updated object (set)
#' @examples
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' object <- read_maxquant_proteingroups(file)
#' log2proteins(object)[1:3, 1:3]
#' @rdname log2proteins
#' @export
setGeneric('log2proteins', function(object) standardGeneric("log2proteins"))
#' @rdname log2proteins
setMethod("log2proteins", signature("SummarizedExperiment"),
function(object) assays(object)$log2proteins)
#' @rdname log2proteins
#' @export
setGeneric('log2proteins<-',
function(object, value) standardGeneric("log2proteins<-"))
#' @rdname log2proteins
setReplaceMethod("log2proteins", signature("SummarizedExperiment", "matrix"),
function(object, value){
assays(object)$log2proteins <- value
object })
#' @rdname log2proteins
setReplaceMethod("log2proteins", signature("SummarizedExperiment", "numeric"),
function(object, value){
assays(object)$log2proteins[] <- value
object })
#---------------------------------------------------------------------------
#
# log2sites
#
#---------------------------------------------------------------------------
#' Get/Set log2sites
#' @param object SummarizedExperiment
#' @param value occupancy matrix (features x samples)
#' @return occpuancy matrix (get) or updated object (set)
#' @examples
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' object <- read_maxquant_proteingroups(file)
#' log2sites(object)[1:3, 1:3]
#' @rdname log2sites
#' @export
setGeneric('log2sites', function(object) standardGeneric("log2sites"))
#' @rdname log2sites
setMethod("log2sites", signature("SummarizedExperiment"),
function(object) assays(object)$log2sites)
#' @rdname log2sites
#' @export
setGeneric('log2sites<-',
function(object, value) standardGeneric("log2sites<-"))
#' @rdname log2sites
setReplaceMethod("log2sites", signature("SummarizedExperiment", "matrix"),
function(object, value){
assays(object)$log2sites <- value
object })
#' @rdname log2sites
setReplaceMethod("log2sites", signature("SummarizedExperiment", "numeric"),
function(object, value){
assays(object)$log2sites[] <- value
object })
#---------------------------------------------------------------------------
#
# log2diffs
#
#---------------------------------------------------------------------------
#' Get/Set log2diffs
#' @param object SummarizedExperiment
#' @param value occupancy matrix (features x samples)
#' @return occpuancy matrix (get) or updated object (set)
#' @examples
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' object <- read_maxquant_proteingroups(file)
#' log2diffs(object)[1:3, 1:3]
#' @rdname log2diffs
#' @export
setGeneric('log2diffs', function(object) standardGeneric("log2diffs"))
#' @rdname log2diffs
setMethod("log2diffs", signature("SummarizedExperiment"),
function(object) assays(object)$log2diffs)
#' @rdname log2diffs
#' @export
setGeneric('log2diffs<-',
function(object, value) standardGeneric("log2diffs<-"))
#' @rdname log2diffs
setReplaceMethod("log2diffs", signature("SummarizedExperiment", "matrix"),
function(object, value){
assays(object)$log2diffs <- value
object })
#' @rdname log2diffs
setReplaceMethod("log2diffs", signature("SummarizedExperiment", "numeric"),
function(object, value){
assays(object)$log2diffs[] <- value
object })
bhagwataditya/importomics documentation built on May 1, 2024, 2:01 a.m.
R Package Documentation
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Defines functions read_maxquant_phosphosites read_proteingroups read_maxquant_proteingroups is_file label2index dequantify mqdt_to_mat drop_differing_uniprots .read_maxquant_phosphosites .read_maxquant_proteingroups un_int64 guess_maxquant_quantity
Documented in dequantify guess_maxquant_quantity is_file label2index .read_maxquant_phosphosites read_maxquant_phosphosites .read_maxquant_proteingroups read_maxquant_proteingroups read_proteingroups
#---------------------------------------------------------------------------
#
# guess_maxquant_quantity
#
#---------------------------------------------------------------------------
#' maxquant quantity patterns
#' @examples
#' MAXQUANT_PATTERNS
#' @export
MAXQUANT_PATTERNS <- c(
`normalizedratio` = '^Ratio ([HM]/[ML]) normalized (.+)$',
`ratio` = '^Ratio ([HM]/[ML]) (?!count|type|variability|iso-count|normalized)(.+)',
`correctedreporterintensity` = '^Reporter intensity corrected ([0-9]+) (.+)$',
`reporterintensity` = '^Reporter intensity ([0-9]+) (.+)$',
`maxlfq` = '^LFQ intensity ([HML] )? ?(.+)$',
`labeledintensity` = '^Intensity ([HML]) (.+)$',
`intensity` = '^Intensity (.+)$'
)
#' Guess maxquant quantity from snames
#'
#' @param x character vector
#' @return string: value from names(MAXQUANT_PATTERNS)
#' @examples
#' # file
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' guess_maxquant_quantity(file)
#'
#' # character vector
#' x <- "Ratio M/L normalized STD(L)_E00(M)_E01(H)_R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "Ratio M/L STD(L)_E00(M)_E01(H)_R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "LFQ intensity E00.R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "Reporter intensity corrected 0 STD(0)E00(1)E01(2)_R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "Reporter intensity 0 STD(0)E00(1)E01(2)_R1"
#' guess_maxquant_quantity(x)
#'
#' x <- "Intensity H STD(L)_E00(M)_E01(H)_R1"
#' guess_maxquant_quantity(x)
#'
#' @export
guess_maxquant_quantity <- function(x){
# Assert
assert_is_character(x)
# read if x is filename
if (is_scalar(x)){
if (is_existing_file(x)){
x <- names(fread(x, header = TRUE, nrows = 1))
}
}
# guess from character vector
for (quantity in names(MAXQUANT_PATTERNS)){
pattern <- MAXQUANT_PATTERNS[[quantity]]
if (any(stri_detect_regex(x, pattern))) return(quantity)
}
stop('quantity could not be infered')
}
#---------------------------------------------------------------------------
#
# .read_maxquant_proteingroups
# .read_maxquant_phosphosites
#
#---------------------------------------------------------------------------
utils::globalVariables('where')
un_int64 <- function(x) {
dplyr::mutate(x, dplyr::across(where(bit64::is.integer64), as.numeric))
}
#' Read proteingroups/phosphosites as-is
#' @param file proteingroups / phosphosites file
#' @param profile proteingroups file
#' @param quantity string
#' @param verbose TRUE / FALSE
#' @return data.table
#' @examples
#' profile <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
#' fosfile <- system.file('extdata/billing19.phosphosites.txt', package = 'autonomics')
#' prodt <- .read_maxquant_proteingroups(file = profile)
#' fosdt <- .read_maxquant_phosphosites( file = fosfile, profile = profile)
#' @export
.read_maxquant_proteingroups <- function(file, quantity = guess_maxquant_quantity(file), verbose = TRUE){
# Assert
assert_maxquant_proteingroups(file)
assert_is_subset(quantity, names(MAXQUANT_PATTERNS))
# Read
if (verbose) cmessage('%sRead%sproteingroups %s', spaces(4), spaces(6), file)
prodt <- fread(file, colClasses = c(id = 'character'), integer64 = 'numeric')
prodt %<>% un_int64()
# prodt[Reverse == '+', `Majority protein IDs` := split_extract_fixed(`Majority protein IDs`, ';', 1)]
# In FOS `Proteins` column is empty for Reverse, so `Protein` (singular!) is copied over
# In PRO therefore take leading protein only for naming identity with FOS.
# Upon further insight naming parity is probably not so important
# So dont unnecessarily modify
# Extract relevant columns
pattern <- MAXQUANT_PATTERNS[[quantity]] # the proteins column is empty in (only) reverse
anncols <- c('id', 'Majority protein IDs', 'Reverse',
'Potential contaminant', 'Contaminant', 'Fasta headers')#, 'Phospho (STY) site IDs')
anncols %<>% intersect(names(prodt))
valcols <- grep(pattern, names(prodt), value = TRUE)
pepcols <- grep('Razor + unique peptides ', names(prodt), fixed = TRUE, value = TRUE)
prodt %<>% extract(, c(anncols, pepcols, valcols), with = FALSE)
digits <- ceiling(log10(nrow(prodt)))
# if (verbose) message('\t\t', nrow(prodt), ' proteins, contaminants, reverse')
# Return
names(prodt) %<>% stri_replace_first_fixed( 'Reverse', 'reverse')
names(prodt) %<>% stri_replace_first_fixed( 'Contaminant', 'contaminant') # older MaxQuant
names(prodt) %<>% stri_replace_first_fixed('Potential contaminant', 'contaminant') # newer MaxQuant
setnames(prodt, 'id', 'proId')
setnames(prodt, 'Majority protein IDs', 'uniprot')
prodt[]
}
#' @rdname dot-read_maxquant_proteingroups
#' @export
.read_maxquant_phosphosites <- function(
file, profile, quantity = guess_maxquant_quantity(file), verbose = TRUE
){
# Assert
assert_maxquant_proteingroups(profile)
assert_maxquant_phosphosites(file)
`Protein group IDs` <- Reverse <- Proteins <- Protein <- NULL
# Read
if (verbose) cmessage('%sphosphosites %s', spaces(14), file)
colclasses <- c(id = 'character', `Protein group IDs` = 'character')
fosdt <- fread(file, colClasses = colclasses, integer64 = 'numeric')
fosdt %<>% un_int64()
# Extract relevant columns
fosdt[Reverse == '+', Proteins := Protein] # the proteins column is empty in (only) reverse
pattern <- MAXQUANT_PATTERNS[[quantity]] # this fails the feature_id naming
anncols <- c('id', 'Protein group IDs', 'Proteins',
'Positions within proteins', 'Amino acid',
'Reverse', 'Contaminant', 'Potential contaminant', 'Fasta headers')
anncols %<>% intersect(names(fosdt))
valcols <- grep(pattern, names(fosdt), value = TRUE)
valcols %<>% extract(stri_detect_fixed(., '___1'))
fosdt %<>% extract(, c(anncols, valcols), with = FALSE)
digits <- ceiling(log10(nrow(fosdt)))
if (verbose) message(spaces(38), 'Read ', formatC(nrow(fosdt), digits = digits),
' phosphosites in proteins, contaminants, reverse')
names(fosdt) %<>% stri_replace_first_fixed( 'Reverse', 'reverse')
names(fosdt) %<>% stri_replace_first_fixed( 'Contaminant', 'contaminant') # older MaxQuant
names(fosdt) %<>% stri_replace_first_fixed('Potential contaminant', 'contaminant') # newer MaxQuant
# Filter
fosdt <- fosdt[stri_count_fixed(`Protein group IDs`, ';') == 0]
if (verbose) message(spaces(38), 'Retain ', formatC(nrow(fosdt), digits = digits),
' phosphosites in single proteingroup')
idx <- rowSums(fosdt[, valcols, with = FALSE], na.rm = TRUE) > 0
fosdt <- fosdt[which(idx)]
if (verbose) message(spaces(38), 'Retain ', formatC(nrow(fosdt), digits = digits),
' phosphosites with signal in some sample')
# Rename
names(fosdt) %<>% stri_replace_first_fixed('___1', '')
setnames(fosdt, 'id', 'fosId')
setnames(fosdt, 'Protein group IDs', 'proId')
setnames(fosdt, 'Proteins', 'uniprot')
fosdt[]
}
#---------------------------------------------------------------------------
#
# drop_differing_uniprots
#
#---------------------------------------------------------------------------
drop_differing_uniprots <- function(fosdt, prodt, verbose){
# Assert
contaminant <- fosId <- `Positions within proteins` <- proId <- NULL
reverse <- uniprot <- NULL
# Avoid changing the global env
prodt %<>% copy()
fosdt %<>% copy()
# Extract annotation cols
if (verbose) message(spaces(38), 'Keep proteingroup uniprots')
proanncols <- c('proId', 'uniprot', 'reverse', 'contaminant')
fosanncols <- c('fosId', 'proId', 'uniprot', 'Positions within proteins', 'reverse', 'contaminant')
proanndt <- prodt[, proanncols, with = FALSE]
fosanndt <- fosdt[, fosanncols, with = FALSE]
# Separate contaminants-reverse from other proteins.
conrevdt <- fosanndt[reverse == '+' | contaminant == '+']
fosanndt <- fosanndt[reverse == '' & contaminant == '']
proanndt <- proanndt[reverse == '' & contaminant == '']
fosanndt[, c('reverse', 'contaminant') := NULL]
proanndt[, c('reverse', 'contaminant') := NULL]
conrevdt[, c('reverse', 'contaminant') := NULL]
# Merge
fosanndt %<>% uncollapse(uniprot, `Positions within proteins`, sep = ';')
proanndt %<>% uncollapse(uniprot, sep = ';')
fosanndt %<>% merge(proanndt, by = c('proId', 'uniprot'))
fosanndt %<>% extract(order(as.integer(fosId)))
fosanndt %<>% extract(, lapply(.SD, paste0, collapse = ';'), by = 'fosId')
# Add back contaminants/reverse
fosanndt %<>% rbind(conrevdt)
fosanndt[, proId := NULL]
fosdt[, c('uniprot', 'Positions within proteins') := NULL]
fosdt %<>% merge(fosanndt, by = 'fosId', all.x = TRUE, sort = FALSE)
fosdt %<>% pull_columns(names(fosanndt))
fosdt
}
#---------------------------------------------------------------------------
#
# mqdt_to_mat
# dequantify
# label2index
#
#---------------------------------------------------------------------------
#' Convert maxquant data.table to matrix
#' @param dt data.table
#' @param pattern string
#' @param verbose TRUE / FALSE
#' @return matrix
#' @examples
#' profile <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
#' fastafile <- system.file('extdata/uniprot_hsa_20140515.fasta', package = 'autonomics')
#' prodt <- .read_maxquant_proteingroups(file = profile)
#' uniprothdrs <- read_uniprotdt(fastafile)
#' contaminanthdrs <- read_contaminantdt()
#' maxquanthdrs <- parse_maxquant_hdrs(prodt$`Fasta headers`)
#' prodt %<>% annotate_maxquant(uniprothdrs, contaminanthdrs, maxquanthdrs)
#' quantity <- guess_maxquant_quantity(profile)
#' pattern <- MAXQUANT_PATTERNS[[quantity]]
#' mqdt_to_mat(prodt, pattern = pattern)[1:2, 1:2]
#' @noRd
mqdt_to_mat <- function(dt, pattern, verbose = TRUE){
mat <- dt[, .SD, .SDcols = patterns(pattern)]
mat %<>% data.matrix()
rownames(mat) <- dt$feature_id
mat %<>% zero_to_na(verbose = verbose)
mat %<>% nan_to_na( verbose = verbose)
mat <- log2(mat)
mat
}
#' Dequantify maxquant snames
#'
#' Drop quantity ('Reporter intensity'). \cr
#' Encode {channel} as suffix.
#'
#' ` Ratio H/L WT(L).KD(H).R1 -> WT(L).KD(H).R1{H/L}`
#' ` LFQ intensity WT.R1 -> WT.R1`
#' `Reporter intensity 0 WT(126).KD(127).R1 -> WT(1).KD(2).R1{1}`
#' @param x `character`
#' @param quantity `'ratio', 'normalizedratio'`, \cr
#' `'LFQ intensity'`, \cr
#' `'intensity', 'labeledintensity'`
#' `'reporterintensity', 'correctedreporterintensity'`
#' @param verbose `TRUE` or `FALSE`
#' @return `character`
#' @examples
#' dequantify(c('Ratio H/L WT(L).KD(M).OE(H).R1', # Ratios
#' 'Ratio M/L WT(L).KD(M).OE(H).R1'))
#' dequantify(c('Ratio H/L normalized WT(L).KD(M).OE(H).R1', # Norm. Ratios
#' 'Ratio M/L normalized WT(L).KD(M).OE(H).R1'))
#' dequantify(c('LFQ intensity WT.R1', # LFQ intensity
#' 'LFQ intensity KD.R1'))
#' dequantify(c('Reporter intensity 1 WT(126).KD(127).R1', # Rep.intensities
#' 'Reporter intensity 2 WT(126).KD(127).R1'))
#' @md
#' @export
dequantify <- function(
x, quantity = guess_maxquant_quantity(x), verbose = FALSE
){
# x = multiplex + channel. Return multiplex if single channel.
# Decompose multiplex and channel
pattern <- MAXQUANT_PATTERNS[[quantity]]
if (quantity == 'intensity'){
multiplex <- stri_replace_first_regex(x, pattern, '$1')
channel <- rep('', length(multiplex))
} else {
multiplex <- stri_replace_first_regex(x, pattern, '$2')
channel <- stri_replace_first_regex(x, pattern, '$1')
channel %<>% stri_replace_first_fixed(' ', '')
}
# Reporter intensity
if (quantity %in% c('reporterintensity', 'correctedreporterintensity')){
multiplex %<>% lapply(label2index)
channel %<>% as.numeric()
if (0 %in% channel){ # pre-2018 mq is 0-based
channel %<>% as.numeric() %>% add(1) %>% as.character()
}
}
# Standardize
if (all(channel=='')){
cleanx <- multiplex
} else {
cleanx <- sprintf('%s{%s}', multiplex, channel)
}
if (verbose) cmessage('%sStandardize snames: %s -> %s', spaces(14), x[1], cleanx[1])
return(cleanx)
}
#' Convert labels into indices
#' @param x `character`
#' @examples
#' label2index(x = 'Reporter intensity 0 WT(0).KD(1).OE(2).R1')
#' label2index(x = 'Reporter intensity 1 WT(1).KD(2).OE(3).R1')
#' label2index(x = 'Reporter intensity 0 WT(126).KD(127).OE(128).R1')
#' label2index(x = 'Reporter intensity 1 WT(126).KD(127).OE(128).R1')
#' label2index(x = 'Reporter intensity 1 Mix1')
#' @export
label2index <- function(x){
labels <- unlist(stri_extract_all_regex(x, '\\(.+?\\)'))
if (any(is.na(labels))) return(x)
for (i in rev(seq_along(labels))){
# important to do this in rev order!!! otherwise ...
# Reporter intensity 0 WT(0).KD(1).OE(2).R1
# i=1: (0) -> 1 : Reporter intensity 0 WT(1).KD(1).OE(2).R1
# i=2: (1) -> 2 : Reporter intensity 0 WT(2).KD(1).OE(2).R1
# not what we want!!!
x %<>% stri_replace_first_fixed(labels[[i]], paste0('(',i,')'))
}
x
}
#---------------------------------------------------------------------------
#
# read_maxquant_proteingroups
# read_maxquant_phosphosites
#
#---------------------------------------------------------------------------
#' Is a file?
#'
#' Is a file (and not a dir)
#'
#' This function distinguishies between dir and file.
#' Others dont: is.file, fs::file_exists, assertive::is_existing_file
#' @param file filepath
#' @examples
#' dir <- tempdir(); dir.create(dir, showWarnings = FALSE)
#' file <- tempfile(); invisible(file.create(file))
#' is_file(dir)
#' is_file(file)
#' @export
is_file <- function(file){
file.exists(file) & !dir.exists(file)
}
#' Read maxquant proteingroups
#' @param dir proteingroups directory
#' @param file proteingroups file
#' @param fastafile uniprot fastafile
#' @param restapi TRUE or FALSE : use uniprot restapi to annotate uniprots not in fastadt ?
#' @param quantity 'normalizedratio', 'ratio', 'correctedreporterintensity',
#' 'reporterintensity', 'maxlfq', 'labeledintensity',
#' 'intensity' or NULL
#' @param subgroups NULL or string vector : subgroups to retain
#' @param contaminants TRUE or FALSE : retain contaminants ?
#' @param reverse TRUE or FALSE : include reverse hits ?
#' @param rm_missing_in_all_samples TRUE or FALSE
#' @param invert string vector : subgroups which require inversion
#' @param impute TRUE or FALSE: impute group-specific NA values?
#' @param plot TRUE or FALSE: plot ?
#' @param label fvar
#' @param pca TRUE or FALSE: run pca ?
#' @param pls TRUE or FALSE: run pls ?
#' @param fit model engine: 'limma', 'lm', 'lme(r)', 'wilcoxon' or NULL
#' @param formula model formula
#' @param block model blockvar: string or NULL
#' @param coefs model coefficients of interest: character vector or NULL
#' @param contrasts coefficient contrasts of interest: character vector or NULL
#' @param palette color palette : named character vector
#' @param verbose TRUE or FALSE : message ?
#' @param ... maintain deprecated functions
#' @return SummarizedExperiment
#' @examples
#' # fukuda20 - LFQ
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' pro <- read_maxquant_proteingroups(file = file)
#'
#' # billing19 - Normalized Ratios
#' file <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
#' fastafile <- system.file('extdata/uniprot_hsa_20140515.fasta', package = 'autonomics')
#' subgroups <- sprintf('%s_STD', c('E00', 'E01', 'E02', 'E05', 'E15', 'E30', 'M00'))
#' pro <- read_maxquant_proteingroups(file = file, subgroups = subgroups)
#' pro <- read_maxquant_proteingroups(file = file, fastafile = fastafile, subgroups = subgroups)
#' @export
read_maxquant_proteingroups <- function(
dir = getwd(),
file = if (is_file(dir)) dir else file.path(dir, 'proteinGroups.txt'),
fastafile = NULL,
restapi = FALSE,
quantity = NULL,
subgroups = NULL,
invert = character(0),
contaminants = FALSE,
reverse = FALSE,
rm_missing_in_all_samples = TRUE,
impute = FALSE,
plot = FALSE,
label = 'feature_id',
pca = plot,
pls = plot,
fit = if (plot) 'limma' else NULL,
formula = ~ subgroup,
block = NULL,
coefs = NULL,
contrasts = NULL,
palette = NULL,
verbose = TRUE
){
# Assert
assert_maxquant_proteingroups(file)
if (is.null(quantity)) quantity <- guess_maxquant_quantity(file)
assert_is_a_string(quantity)
assert_is_subset(quantity, names(MAXQUANT_PATTERNS))
assert_is_a_bool(verbose)
`Fasta headers` <- NULL
# Read/Annotate
prodt <- .read_maxquant_proteingroups(file = file, quantity = quantity, verbose = verbose)
uniprothdrs <- read_uniprotdt(fastafile)
contaminanthdrs <- read_contaminantdt()
maxquanthdrs <- parse_maxquant_hdrs(prodt$`Fasta headers`); prodt[, `Fasta headers` := NULL ]
prodt %<>% annotate_maxquant( uniprothdrs = uniprothdrs,
contaminanthdrs = contaminanthdrs,
maxquanthdrs = maxquanthdrs,
restapi = restapi,
verbose = verbose )
# SumExp
if (verbose) cmessage('%sSumExp', spaces(4))
pattern <- MAXQUANT_PATTERNS[[quantity]]
promat <- mqdt_to_mat(prodt, pattern, verbose = verbose)
pepcols <- names(prodt) %>% extract(stri_detect_fixed(., 'eptides'))
pepdt <- prodt[, pepcols, with = FALSE]
prodt %<>% extract(, names(prodt) %>% setdiff(colnames(promat)) %>% setdiff(names(pepdt)), with = FALSE)
object <- list(promat)
names(object) <- paste0('log2', quantity)
object %<>% SummarizedExperiment(rowData = prodt)
# Dequantify. Add pepcounts
object$mqcol <- colnames(object)
colnames(object) %<>% dequantify(quantity = quantity, verbose = verbose)
pepcols <- paste0('Razor + unique peptides ', gsub('\\{.+\\}', '', colnames(object)))
pepmat <- pepdt[, pepcols, with = FALSE ]
pepmat %<>% data.matrix()
dimnames(pepmat) <- dimnames(object)
assays(object)$pepcounts <- pepmat
object %<>% process_maxquant( subgroups = subgroups,
invert = invert,
reverse = reverse,
contaminants = contaminants,
rm_missing_in_all_samples = rm_missing_in_all_samples,
impute = impute,
verbose = verbose )
assays <- c(assayNames(object)[1], 'pepcounts')
for (assay in assays) object %<>% add_assay_means(assay)
object %<>% analyze(
pca = pca, pls = pls,
fit = fit, formula = formula,
block = block, coefs = coefs,
contrasts = contrasts, plot = plot,
label = label,
palette = palette, verbose = verbose )
object
}
#' @rdname read_maxquant_proteingroups
#' @export
read_proteingroups <- function(...){
.Deprecated('read_maxquant_proteingroups')
read_maxquant_proteingroups(...)
}
#' Read maxquant phosphosites
#' @param dir proteingroups directory
#' @param fosfile phosphosites file
#' @param profile proteingroups file
#' @param fastafile uniprot fastafile
#' @param restapi TRUE or FALSE : annotate non-fastadt uniprots using uniprot restapi
#' @param quantity 'normalizedratio', 'ratio', 'correctedreporterintensity',
#' 'reporterintensity', 'maxlfq', 'labeledintensity',
#' 'intensity' or NULL
#' @param subgroups NULL or string vector : subgroups to retain
#' @param contaminants TRUE or FALSE: retain contaminants ?
#' @param reverse TRUE or FALSE: include reverse hits
#' @param rm_missing_in_all_samples TRUE or FALSE
#' @param localization number: min localization probability (for phosphosites)
#' @param invert string vector: subgroups which require inversion
#' @param impute TRUE or FALSE: impute group-specific NA values?
#' @param plot TRUE or FALSE
#' @param label fvar
#' @param pca TRUE or FALSE: run pca ?
#' @param pls TRUE or FALSE: run pls ?
#' @param fit model engine: 'limma', 'lm', 'lme(r)', 'wilcoxon' or NULL
#' @param formula model formula
#' @param block model blockvar: string or NULL
#' @param coefs model coefficients of interest: string vector or NULL
#' @param contrasts model coefficient contrasts of interest: string vector or NULL
#' @param palette color palette: named string vector
#' @param verbose TRUE or FALSE: message ?
#' @param ... maintain deprecated functions
#' @return SummarizedExperiment
#' @examples
#' profile <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
#' fosfile <- system.file('extdata/billing19.phosphosites.txt', package = 'autonomics')
#' fastafile <- system.file('extdata/uniprot_hsa_20140515.fasta', package = 'autonomics')
#' subgroups <- sprintf('%s_STD', c('E00', 'E01', 'E02', 'E05', 'E15', 'E30', 'M00'))
#' pro <- read_maxquant_proteingroups(file = profile, subgroups = subgroups)
#' fos <- read_maxquant_phosphosites( fosfile = fosfile, profile = profile, subgroups = subgroups)
#' fos <- read_maxquant_phosphosites( fosfile = fosfile, profile = profile, fastafile = fastafile, subgroups = subgroups)
#' @export
read_maxquant_phosphosites <- function(
dir = getwd(),
fosfile = if (is_file(dir)) dir else file.path(dir, 'phospho (STY)Sites.txt'),
profile = file.path(dirname(fosfile), 'proteinGroups.txt'),
fastafile = NULL,
restapi = FALSE,
quantity = NULL,
subgroups = NULL,
invert = character(0),
contaminants = FALSE,
reverse = FALSE,
rm_missing_in_all_samples = TRUE,
localization = 0.75,
impute = FALSE,
plot = FALSE,
label = 'feature_id',
pca = plot,
pls = plot,
fit = if (plot) 'limma' else NULL,
formula = ~ subgroup,
block = NULL,
coefs = NULL,
contrasts = NULL,
palette = NULL,
verbose = TRUE
){
# Assert
assert_all_are_existing_files(c(fosfile, profile))
if (is.null(quantity)) quantity <- guess_maxquant_quantity(fosfile)
assert_is_a_string(quantity)
assert_is_subset(quantity, names(MAXQUANT_PATTERNS))
assert_is_a_bool(verbose)
`Fasta headers` <- NULL
# Read
prodt <- .read_maxquant_proteingroups(file = profile, quantity = quantity, verbose = verbose)
fosdt <- .read_maxquant_phosphosites( file = fosfile, quantity = quantity, profile = profile, verbose = verbose)
fosdt %<>% drop_differing_uniprots(prodt, verbose = verbose)
uniprothdrs <- read_uniprotdt(fastafile)
contaminanthdrs <- read_contaminantdt()
maxquanthdrs <- parse_maxquant_hdrs(prodt$`Fasta headers`); fosdt[, `Fasta headers` := NULL ]
fosdt %<>% annotate_maxquant( uniprothdrs = uniprothdrs,
contaminanthdrs = contaminanthdrs,
maxquanthdrs = maxquanthdrs,
restapi = restapi,
verbose = verbose )
prodt %<>% extract(fosdt$proId, on = 'proId')
# SumExp
if (verbose) cmessage('%sSumExp', spaces(4))
pattern <- MAXQUANT_PATTERNS[[quantity]]
promat <- mqdt_to_mat(prodt, pattern, verbose = verbose)
pepcols <- names(prodt) %>% extract(stri_detect_fixed(., 'eptides'))
pepdt <- prodt[, pepcols, with = FALSE]
prodt %<>% extract(, names(prodt) %>% setdiff(colnames(promat)) %>% setdiff(names(pepdt)), with = FALSE)
fosmat <- mqdt_to_mat(fosdt, pattern, verbose = verbose)
fosdt <- fosdt %>% extract(, setdiff(names(.), colnames(fosmat)), with = FALSE)
object <- SummarizedExperiment::SummarizedExperiment(
assays = list(log2sites = fosmat,
log2proteins = promat,
log2diffs = fosmat - promat),
rowData = fosdt)
# Dequantify. Add pepcounts
object$mqcol <- colnames(object)
colnames(object) %<>% dequantify()
pepcols <- paste0('Razor + unique peptides ', gsub('\\{.+\\}', '', colnames(object)))
pepmat <- pepdt[, pepcols, with = FALSE ]
pepmat %<>% data.matrix()
dimnames(pepmat) <- dimnames(object)
assays(object)$pepcounts <- pepmat
# Process / Analyze
object %<>% process_maxquant( subgroups = subgroups,
invert = invert,
reverse = reverse,
contaminants = contaminants,
rm_missing_in_all_samples = rm_missing_in_all_samples,
localization = localization,
impute = impute,
verbose = verbose )
assays <- c('log2sites', 'pepcounts')
for (assay in assays) object %<>% add_assay_means(assay)
object %<>% analyze(
pca = pca, pls = pls,
fit = fit, formula = formula,
block = block, coefs = coefs,
contrasts = contrasts, plot = plot,
label = label,
palette = palette, verbose = verbose )
object
}
#' @rdname read_maxquant_phosphosites
#' @export
read_phosphosites <- function(...){
.Deprecated('read_maxquant_phosphosites')
read_maxquant_phosphosites(...)
}
#-----------------------------------------------------------------------------
#
# invert_subgroups
#
#-----------------------------------------------------------------------------
# x <- c('Ctrl_A', 'Ctrl_B')
# .invert_subgroups(x)
.invert_subgroups <- function(x, sep = guess_sep(x)){
stri_split_fixed(x, sep) %>%
lapply(rev) %>%
vapply(paste, character(1), collapse = sep)
}
#' Invert subgroups
#'
#' Invert expressions , subgroups, and sample ids
#'
#' @param object SummarizedExperiment
#' @param subgroups character vector: subgroup levels to be inversed
#' @param sep string: collapsed string separator
#' @return character vector or SummarizedExperiment
#' @examples
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' object <- read_maxquant_proteingroups(file)
#' invert_subgroups(object)
#' @export
invert_subgroups <- function(
object,
subgroups = slevels(object, 'subgroup'),
sep = guess_sep(object, 'subgroup')
){
# Assert / Msg
if (length(subgroups)==0) return(object)
assert_is_all_of(object, 'SummarizedExperiment')
assert_is_subset('subgroup', svars(object))
assert_is_subset(subgroups, subgroup_levels(object))
idx <- which(object$subgroup %in% subgroups)
first <- values(object)[, idx[1]] %>% (function(y) which(!is.na(y))[[1]])
oldvalue <- values(object)[first, idx[1]] %>% round(2) %>% as.character()
cmessage('%sInvert subgroups %s', spaces(14), paste0(subgroups, collapse = ', '))
# Invert (log) ratios
if (all(values(object)>0, na.rm=TRUE)){ values(object)[, idx] %<>% (function(object){1/object})
} else { values(object)[, idx] %<>% (function(object){ -object})}
newvalue <- as.character(round(values(object)[first, idx[1]], 2))
cmessage('%sexprs : %s -> %s', spaces(21), as.character(oldvalue),
as.character(newvalue))
# Invert subgroup and sampleid values
oldsubgroups <- sdata(object)$subgroup[idx]
newsubgroups <- vapply(oldsubgroups, .invert_subgroups, character(1),sep=sep)
oldsampleids <- sdata(object)$sample_id[idx]
for (i in seq_along(idx)){
sdata(object)$subgroup[ idx[i]] <- newsubgroups[i]
sdata(object)$sample_id[idx[i]] %<>% stri_replace_first_fixed(oldsubgroups[i], newsubgroups[i])
snames(object)[ idx[i]] %<>% stri_replace_first_fixed(oldsubgroups[i], newsubgroups[i])
}
newsampleids <- sdata(object)$sample_id[idx]
cmessage('%ssubgroups: %s -> %s', spaces(21), oldsubgroups[1], newsubgroups[1])
cmessage('%ssampleids: %s -> %s', spaces(21), oldsampleids[1], newsampleids[1])
# Order on subgroup
object %<>% arrange_samples_('subgroup')
cmessage('%sOrder on subgroup', spaces(14))
object$subgroup %<>% droplevels()
# Return
return(object)
}
arrange_samples_ <- function(object, svars){
idx <- do.call(order, sdata(object)[, svars, drop = FALSE])
object %<>% extract(, idx)
return(object)
}
#---------------------------------------------------------------------------
#
# demultiplex
# .is_multiplexed
# .demultiplex
#
#---------------------------------------------------------------------------
#' Demultiplex snames
#'
#' Demultiplex maxquant samplenames
#'
#' `WT(L).KD(H).R1{H/L} -> KD_WT.R1`
#' `WT(1).KD(2).R1{1} -> WT.R1`
#' ` WT.R1 -> WT.R1`
#' @param x character vector
#' @param verbose TRUE or FALSE
#' @return character
#' @examples
#' # uniplexed / intensity / ratio
#' demultiplex(c('KD.R1','OE.R1'))
#' demultiplex(c('WT(L).KD(M).OE(H).R1{M}', 'WT(L).KD(M).OE(H).R1{H}'))
#' demultiplex(c('WT(L).KD(M).OE(H).R1{M/L}','WT(L).KD(M).OE(H).R1{H/L}'))
#' # run / replicate
#' demultiplex(c('WT(L).OE(H).R1{L}', 'WT(L).OE(H).R1{H}')) # run
#' demultiplex(c('WT.R1(L).OE.R1(H){L}', 'WT.R1(L).OE.R1(H){H}')) # repl
#' # label / index
#' demultiplex(c('WT(L).OE(H).R1{L}', 'WT(L).OE(H).R1{H}')) # label
#' demultiplex(c('WT(1).OE(2).R1{1}', 'WT(1).OE(2).R1{2}')) # index
#' # with unused channels
#' demultiplex('WT(1).KD(2).OE(3).R1{6}')
#' @md
#' @export
demultiplex <- function(x, verbose = FALSE){
assert_is_character(x)
assert_is_a_bool(verbose)
. <- NULL
if (!.is_multiplexed(x)) return(x)
y <- unname(vapply(x, .demultiplex, character(1)))
make.unique(y, sep = '-')
}
.is_multiplexed <- function(x){
# Components
pattern <- '(.+)\\{(.+)\\}'
n_open <- stri_count_fixed(x, '(')
n_closed <- stri_count_fixed(x, ')')
channel <- gsub(pattern, '\\2', x)
# Multiplexed consistently ?
y <- all(stri_detect_regex(x, pattern) &
n_open > 0 &
n_open == n_closed)
# All channels defined (TMT) ?
if (all(is_numeric_string(channel))){
channel %<>% as.numeric()
y %<>% and(all(as.numeric(channel) <= n_open))
}
y
}
.demultiplex <- function(y){
y0 <- y
channel <- split_extract_fixed(y, '{', 2) %>% substr(1, nchar(.)-1)
y %<>% stri_replace_last_fixed(paste0('{', channel, '}'), '')
labels <- y %>% stri_extract_all_regex('\\([^()]+\\)') %>% unlist() %>% substr(2, nchar(.)-1)
run <- y %>% stri_split_regex('\\([^[()]]+\\)') %>% unlist() %>% extract(length(.))
biosamples <- y %>% stri_split_regex('\\([^[()]]+\\)') %>% unlist() %>% extract(-length(.))
biosamples %<>% stri_replace_first_regex('^[._ ]', '')
assert_are_same_length(biosamples, labels)
names(biosamples) <- labels
channel %<>% stri_split_fixed('/') %>% unlist()
if (!all(channel %in% labels)) return(y0)
result <- paste0(biosamples[channel], collapse = '_')
result %<>% paste0(run)
result
}
#----------------------------------------------------------------------------
#
# process_maxquant
#
#----------------------------------------------------------------------------
process_maxquant <- function(
object, subgroups, invert, contaminants, reverse, rm_missing_in_all_samples = TRUE,
localization = 0.75, impute, verbose
){
# Demultiplex. Infer Subgroup
contaminant <- `Localization prob` <- NULL
colnames(object) %<>% demultiplex(verbose = verbose)
object$sample_id <- colnames(object)
object %<>% add_subgroup(verbose = verbose)
cols <- c('sample_id', 'subgroup', 'replicate', 'mqcol')
cols %<>% intersect(svars(object))
sdt(object) %<>% pull_columns(cols)
# Samples
object %<>% filter_samples_available_for_some_feature(verbose = verbose)
if (!is.null(subgroups)){
assert_is_subset(subgroups, as.character(object$subgroup))
object %<>% filter_samples(subgroup %in% subgroups, verbose = verbose)
}
object %<>% invert_subgroups(invert)
# Features
analysis(object)$nfeatures <- c(nrow(object))
if (!reverse) object %<>% filter_features( reverse == '', verbose = verbose)
if (!contaminants) object %<>% filter_features(contaminant== '', verbose = verbose)
if ({rm_missing_in_all_samples}) object %<>% rm_missing_in_all_samples(verbose = verbose)
#object %<>% filter_exprs_replicated_in_some_subgroup(verbose = verbose) # doesnt work for single-instance subgroups
if ('Localization prob' %in% fvars(object)){ # subgroup could be increasing concentrations or so
object %<>% filter_features(
`Localization prob` >= localization, verbose = verbose) }
# Impute
if ({{impute}}) object %<>% impute(plot = FALSE)
object
}
#---------------------------------------------------------------------------
#
# log2proteins
#
#---------------------------------------------------------------------------
#' Get/Set log2proteins
#' @param object SummarizedExperiment
#' @param value occupancy matrix (features x samples)
#' @return occpuancy matrix (get) or updated object (set)
#' @examples
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' object <- read_maxquant_proteingroups(file)
#' log2proteins(object)[1:3, 1:3]
#' @rdname log2proteins
#' @export
setGeneric('log2proteins', function(object) standardGeneric("log2proteins"))
#' @rdname log2proteins
setMethod("log2proteins", signature("SummarizedExperiment"),
function(object) assays(object)$log2proteins)
#' @rdname log2proteins
#' @export
setGeneric('log2proteins<-',
function(object, value) standardGeneric("log2proteins<-"))
#' @rdname log2proteins
setReplaceMethod("log2proteins", signature("SummarizedExperiment", "matrix"),
function(object, value){
assays(object)$log2proteins <- value
object })
#' @rdname log2proteins
setReplaceMethod("log2proteins", signature("SummarizedExperiment", "numeric"),
function(object, value){
assays(object)$log2proteins[] <- value
object })
#---------------------------------------------------------------------------
#
# log2sites
#
#---------------------------------------------------------------------------
#' Get/Set log2sites
#' @param object SummarizedExperiment
#' @param value occupancy matrix (features x samples)
#' @return occpuancy matrix (get) or updated object (set)
#' @examples
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' object <- read_maxquant_proteingroups(file)
#' log2sites(object)[1:3, 1:3]
#' @rdname log2sites
#' @export
setGeneric('log2sites', function(object) standardGeneric("log2sites"))
#' @rdname log2sites
setMethod("log2sites", signature("SummarizedExperiment"),
function(object) assays(object)$log2sites)
#' @rdname log2sites
#' @export
setGeneric('log2sites<-',
function(object, value) standardGeneric("log2sites<-"))
#' @rdname log2sites
setReplaceMethod("log2sites", signature("SummarizedExperiment", "matrix"),
function(object, value){
assays(object)$log2sites <- value
object })
#' @rdname log2sites
setReplaceMethod("log2sites", signature("SummarizedExperiment", "numeric"),
function(object, value){
assays(object)$log2sites[] <- value
object })
#---------------------------------------------------------------------------
#
# log2diffs
#
#---------------------------------------------------------------------------
#' Get/Set log2diffs
#' @param object SummarizedExperiment
#' @param value occupancy matrix (features x samples)
#' @return occpuancy matrix (get) or updated object (set)
#' @examples
#' file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
#' object <- read_maxquant_proteingroups(file)
#' log2diffs(object)[1:3, 1:3]
#' @rdname log2diffs
#' @export
setGeneric('log2diffs', function(object) standardGeneric("log2diffs"))
#' @rdname log2diffs
setMethod("log2diffs", signature("SummarizedExperiment"),
function(object) assays(object)$log2diffs)
#' @rdname log2diffs
#' @export
setGeneric('log2diffs<-',
function(object, value) standardGeneric("log2diffs<-"))
#' @rdname log2diffs
setReplaceMethod("log2diffs", signature("SummarizedExperiment", "matrix"),
function(object, value){
assays(object)$log2diffs <- value
object })
#' @rdname log2diffs
setReplaceMethod("log2diffs", signature("SummarizedExperiment", "numeric"),
function(object, value){
assays(object)$log2diffs[] <- value
object })
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Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.