q_metadata: Quantitative metadata vocabulary for entities
In samWieczorek/Dapar2: Tools for the Differential Analysis of Proteins Abundance with R

q_metadata

R Documentation

Quantitative metadata vocabulary for entities

Description

This function gives the vocabulary used for the quantitative metadata of each entity in each condition.

This function is based on the qMetadata dataframe to look for either missing values (used to update an initial dataset) or imputed values (used when post processing protein qMetadata after aggregation)

In the quantitative columns, a missing value is identified by no value rather than a value equal to 0. Conversion rules Quanti Tag NA or 0 NA

In the quantitative columns, a missing value is identified by no value rather than a value equal to 0. Conversion rules Initial conversion rules for maxquant

|————–|—————–|—–|

Quanti	PSM count	Tag
== 0	N.A.	whatever
> 0	> 0	1.1
> 0	== 0	1.2
> 0	unknown col	1.0
--------------	-----------------	-----

Initial conversion rules for maxquant |————|———————–|——–|

Quanti	Identification	Tag
== 0	whatever	2.0
> 0	'By MS/MS'	1.1
> 0	'By matching'	1.2
> 0	unknown col	1.0
------------	-----------------------	--------

Similar to the function is.na but focused on the equality with the paramter 'type'.

Update the quantitative metadata information of missing values that were imputed

Gives all the tags of the metadata vocabulary containing the pattern (parent and all its children).

Agregation rules for the cells quantitative metadata of peptides. Please refer to the qMetadata.def vocabulary in qMetadata.def()

Usage

qMetadata.def(level = "all")

custom_qMetadata_colors()

Set_POV_MEC_tags(conds, df, level)

BuildqMetadata(from = NULL, level, qdata = NULL, conds = NULL, df = NULL)

qMetadata_generic(qdata, conds, level)

qMetadata_proline(qdata, conds, df, level = NULL)

qMetadata_maxquant(qdata, conds, df, level = NULL)

match.qMetadata(df, pattern, level)

## S4 method for signature 'SummarizedExperiment'
UpdateqMetadata(object, from, to, ...)

search.qMetadata.tags(pattern, level, depth = "1")

qMetadata_combine(met, level)

Arguments

`level`	xxx
`conds`	xxx
`df`	A data.frame
`from`	xxx
`qdata`	A matrix of quantitative data
`pattern`	The string to search.
`object`	xxx
`to`	xxx
`...`	xxx
`depth`	xxx
`met`	xxx

Value

A data.frame containing the different tags and corresponding colors for the level given in parameter

An instance of class MSnSet.

xxxxx

A boolean dataframe

Glossary

Peptide-level vocabulary

Protein-level vocabulary:

Conversion to the glossary

A generic conversion

Conversion for Proline datasets

Conversion from Maxquant datasets

Basic agreagtion

Agregation of non imputed values (2.X) with quantitative values

(1.0, 1.X, 3.0, 3.X)

Not possible

----------------------------

Agregation of different types of missing values (among 2.1, 2.2)

Agregation of 2.1 peptides between each other gives a missing value non imputed (2.0)
Agreagtion of 2.2 peptides between each other givesa missing value non imputed (2.0)
Agregation of a mix of 2.1 and 2.2 gives a missing value non imputed (2.0) |—————————-

Agregation of a mix of quantitative values (among 1.0, 1.1, 1.2, 3.0, 3.X)

if the type of all the peptides to agregate is 1.0, 1.1 or 1.2, then the final metadata is set the this tag
if the set of metacell to agregate is a mix of 1.0, 1.1 or 1.2, then the final metadata is set to 1.0
if the set of metacell to agregate is a mix of 3.X and 3.0, then the final metadata is set to 3.0
if the set of metacell to agregate is a mix of 3.X and 3.0 and other (X.X), then the final metadata is set to 4.0 |—————————-

Post processing

Update metacell with POV/MEC status for the categories 2.0 and 3.0 TODO

Author(s)

Thomas Burger, Samuel Wieczorek

Samuel Wieczorek

Examples




#-----------------------------------------------
# A shiny app to view color legends 
#-----------------------------------------------
if(interactive()){
data(ft)
ui <- mod_qMetadataLegend_ui("legend")

server <- function(input, output, session) {
  mod_qMetadataLegend_server('legend',
                                object = reactive({ft[[1]]}))
  }
  
 shinyApp(ui = ui, server = server)
  
  
}
data(ft_na)
df <- assay(ft_na, 2)
level <- typeDataset(ft_na, 1)
df <- Set_POV_MEC_tags(ft_na, 1, level)

file <- system.file("extdata", "Exp1_R25_pept.txt", package="DaparToolshedData")
data <- read.table(file, header=TRUE, sep="\t",stringsAsFactors = FALSE)
metadataFile <- system.file("extdata", "samples_Exp1_R25.txt", 
package="DaparToolshedData")
metadata <- read.table(metadataFile, header=TRUE, sep="\t", as.is=TRUE, 
stringsAsFactors = FALSE)
conds <- metadata$Condition
qdata <- data[,56:61]
df <- data[ , 43:48]
df <- BuildqMetadata('maxquant', 'peptide', qdata, conds, df)
df <- BuildqMetadata('proline', 'peptide', qdata, conds, df)

file <- system.file("extdata", "Exp1_R25_pept.txt", package="DaparToolshedData")
data <- read.table(file, header=TRUE, sep="\t",stringsAsFactors = FALSE)
metadataFile <- system.file("extdata", "samples_Exp1_R25.txt", 
package="DaparToolshedData")
metadata <- read.table(metadataFile, header=TRUE, sep="\t", as.is=TRUE, 
stringsAsFactors = FALSE)
conds <- metadata$Condition
qdata <- data[seq_len(100), seq(56, 61)]
df <- data[seq_len(100) , seq(43,48)]
df <- qMetadata_generic(qdata, conds, 'peptide')

 
file <- system.file("extdata", "Exp1_R25_pept.txt", package="DaparToolshedData")
data <- read.table(file, header=TRUE, sep="\t",stringsAsFactors = FALSE)
metadataFile <- system.file("extdata", "samples_Exp1_R25.txt", 
package="DaparToolshedData")
metadata <- read.table(metadataFile, header=TRUE, sep="\t", as.is=TRUE, 
stringsAsFactors = FALSE)
conds <- metadata$Condition
qdata <- data[seq_len(100), seq(56, 61)]
df <- data[seq_len(100) , seq(43, 48)]
df <- qMetadata_proline(qdata, conds, df, 'peptide')


file <- system.file("extdata", "Exp1_R25_pept.txt", package="DaparToolshedData")
data <- read.table(file, header=TRUE, sep="\t",stringsAsFactors = FALSE)
metadataFile <- system.file("extdata", "samples_Exp1_R25.txt", 
package="DaparToolshedData")
metadata <- read.table(metadataFile, header=TRUE, sep="\t", as.is=TRUE, 
stringsAsFactors = FALSE)
conds <- metadata$Condition
qdata <- data[seq_len(10),seq(56, 61)]
df <- data[seq_len(10) , seq(43, 48)]
df2 <- qMetadata_maxquant(qdata, conds, df, 'peptide')

data(ft)
obj <- ft[[1]]
metadata <- qMetadata(obj)
m <- match.qMetadata(metadata, "missing", 'peptide')


obj <- Exp1_R25_pept[seq_len(10),]
obj[[2]] <- UpdateqMetadata(obj[[2]], 'missing', 'imputed')

search.qMetadata.tags('missing POV', 'peptide')
search.qMetadata.tags('quanti', 'peptide')

## Not run: 
ll <- qMetadata.def('peptide')$node
for (i in 1:length(ll))
test <- lapply(combn(ll, i, simplify = FALSE), 
function(x) tag <- qMetadata_combine(x, 'peptide'))

## End(Not run)

samWieczorek/Dapar2 documentation built on May 13, 2022, 9:23 a.m.