Simulate Peptide level Data

Dimulate data

knitr::opts_chunk$set(warning = FALSE, message = FALSE)

For proteins: - the proteins have a FC either equal 1, 0. or -1, 10% have 1 80% have 0 and 10% have -1.

What we however are measuring are peptide spectrum matches. Let’s assume we observing peptides.

For peptides:

The transformed protein abundances have a log normal distribution with meanlog = log(20), and sd = log(1.2).
The number of peptides per protein follow a geometric distribution, $N_{pep} \sim Geo(p)$ with $p = 0.3$
The peptide abundances of a protein have log normal distribution with meanlog = log(proteinabundance) and sd = log(1.2)
The log2 intensities of a peptide within a group follow a normal distribution distribution $I_{pep} LogNormal(,) $, where $\mu$ is the peptide abundance and $\sigma$

peptideAbundances <- prolfqua::sim_lfq_data(PEPTIDE = TRUE)

Analyse simulated data with prolfqua

library(prolfqua)

atable <- AnalysisTableAnnotation$new()
atable$fileName = "sample"
atable$factors["group_"] = "group"
atable$hierarchy[["protein_Id"]] = "proteinID"
atable$hierarchy[["peptide_Id"]] = "peptideID"
atable$set_response("abundance")

config <- AnalysisConfiguration$new(atable)
adata <- setup_analysis(peptideAbundances, config)

lfqdata <- prolfqua::LFQData$new(adata, config)
lfqdata$is_transformed(TRUE)

lfqdata$remove_small_intensities(threshold = 1)
lfqdata$filter_proteins_by_peptide_count()

lfqdata$factors()

## # A tibble: 12 × 3
##    sample  sampleName group_
##    <chr>   <chr>      <chr> 
##  1 A_V1    A_V1       A     
##  2 A_V2    A_V2       A     
##  3 A_V3    A_V3       A     
##  4 A_V4    A_V4       A     
##  5 B_V1    B_V1       B     
##  6 B_V2    B_V2       B     
##  7 B_V3    B_V3       B     
##  8 B_V4    B_V4       B     
##  9 Ctrl_V1 Ctrl_V1    Ctrl  
## 10 Ctrl_V2 Ctrl_V2    Ctrl  
## 11 Ctrl_V3 Ctrl_V3    Ctrl  
## 12 Ctrl_V4 Ctrl_V4    Ctrl

pl <- lfqdata$get_Plotter()
lfqdata$hierarchy_counts()

## # A tibble: 1 × 3
##   isotopeLabel protein_Id peptide_Id
##   <chr>             <int>      <int>
## 1 light                16         60

lfqdata$config$table$hierarchy_keys_depth()

## [1] "protein_Id"

pl$heatmap()

pl$intensity_distribution_density()

Fit peptide model

formula_Condition <-  strategy_lm("abundance ~ group_")
lfqdata$config$table$hierarchyDepth  <- 2

# specify model definition
modelName  <- "Model"
Contrasts <- c("B_over_Ctrl" = "group_B - group_Ctrl",
               "A_over_Ctrl" = "group_A - group_Ctrl")
lfqdata$subject_Id()

## [1] "protein_Id" "peptide_Id"

mod <- prolfqua::build_model(
  lfqdata,
  formula_Condition)
aovtable <- mod$get_anova()
mod$anova_histogram()

## $plot

## 
## $name
## [1] "Anova_p.values_Model.pdf"

xx <- aovtable |> dplyr::filter(FDR < 0.05)
signif <- lfqdata$get_copy()
signif$data <- signif$data |> dplyr::filter(protein_Id %in% xx$protein_Id)
hmSig <- signif$get_Plotter()$heatmap()
hmSig

Aggregate data

lfqdata$config$table$hierarchyDepth <- 1
ag <- lfqdata$get_Aggregator()
ag$medpolish()
protData <- ag$lfq_agg

protData$response()

## [1] "medpolish"

formula_Condition <-  strategy_lm("medpolish ~ group_")

mod <- prolfqua::build_model(
  protData,
  formula_Condition)

contr <- prolfqua::Contrasts$new(mod, Contrasts)
v1 <- contr$get_Plotter()$volcano()
v1$FDR

ctr <- contr$get_contrasts()

Witold E. Wolski

2024-05-30

Dimulate data

Analyse simulated data with prolfqua

Fit peptide model

Aggregate data