lgatto

## Installation instructions for the R for Mass Spectrometry tutorial
##      https://rformassspectrometry.github.io/docs/

## Install packages

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("tidyverse", ask = FALSE)
BiocManager::install("factoextra", ask = FALSE)

lgatto

;; Pulls and pushes a few github repos from emacs
;; Laurent Gatto (https://github.com/lgatto)
;; Thanks to Stephen J. Eglen for his help in redirecting to a
;; dedicated buffer (https://github.com/sje30)

(setq git-dirs '(
                "~/wd"
                "~/bin"
                "~/Documents/org"
                "~/Documents/roam"

lgatto

library(tidyverse)
library(scp)

## Read the data table
x <- read.delim("data/02ng/Task2-SearchTask/AllQuantifiedPeptides.tsv") |>
    select(-18) |> ## column 18 is all NAs
    janitor::clean_names() |>
    rename_with(~ gsub("intensity_ex_auto_", "int_", .x, fixed = TRUE)) |>
    rename_with(~ gsub("detection_type_ex_auto_", "det_", .x, fixed = TRUE))

lgatto

library(dplyr)
library(tibble)
library(SummarizedExperiment)
library(DEP)
library(shiny)
library(shinydashboard)

ui <- shinyUI(
  dashboardPage(
    dashboardHeader(title = "DEP - LFQ"),

lgatto

ggPlotMzDelta <- function(delta, aaLabels = TRUE) {
    stopifnot(require("ggplot2"))
    ## from PSM::getAminoAcids()
    amino_acids <-
        structure(list(AA = c("peg", "A", "R", "N", "D", "C", "E", "Q",
                              "G", "H", "I", "L", "K", "M", "F", "P", "S",
                              "T", "W", "Y", "V"),
                       ResidueMass = c(44, 71.03711, 156.10111, 114.04293,
                                       115.02694, 103.00919, 129.04259,
                                       128.05858, 57.02146, 137.05891,

lgatto

##' @title Compute the MZ deltas
##'
##' @description
##'
##' The M/Z delta plot illustrates the suitability of MS2 spectra for
##' identification by plotting the M/Z differences of the most intense
##' peaks. The resulting histogram should optimally shown outstanding
##' bars at amino acid residu masses. The plots have been described in
##' Foster et al. 2011.
##'

lgatto

library(magrittr)
library(ggplot2)
library(rpx)

rpx:::apply_fix_issue_5(FALSE)

## https://www.ebi.ac.uk/pride/archive/projects/PXD022816
## RawBeans: A Simple, Vendor-Independent, Raw-Data Quality-Control
## Tool (10.1021/acs.jproteome.0c00956)

lgatto

set.seed(123)
max_corrs <- function(d, n = 60, n_iter = 1000)
    replicate(n_iter, {
        m <- matrix(rnorm(n * d), ncol = d)
        max(cor(m)[-1, 1])
    })

r1 <- data.frame(d = 800, r = max_corrs(800))
r2 <- data.frame(d = 6400, r = max_corrs(6400))
r <- rbind(r1, r2)

lgatto

• open != reproducible
• it's not about open vs closed - there is are multiple dimensions/gradients of openness
• it's not about reproducible vs non-reproducible
• The goal is trust, verification and guarantees
• Five selfish reasons to work reproducibly
• Technical solutions
• Inclusivity: Open research and open research

lgatto

setClass("CharacterVariableFilter",
         contains = "CharacterFilter")

setClass("NumericVariableFilter",
         contains = "DoubleFilter")

VariableFilter <- function(field,
                           value,
                           condition = "==") {
    if (is.numeric(value))