In leekgroup/derfinderData: Processed BigWigs from BrainSpan for examples

## Track time spent on making the vignette
startTime <- Sys.time()

## Bib setup
library("RefManageR")

## Write bibliography information
bib <- c(
    BiocStyle = citation("BiocStyle"),
    brainspan = RefManageR::BibEntry(bibtype = "Unpublished", key = "brainspan", title = "Atlas of the Developing Human Brain [Internet]. Funded by ARRA Awards 1RC2MH089921-01, 1RC2MH090047-01, and 1RC2MH089929-01.", author = "BrainSpan", year = 2011, url = "http://developinghumanbrain.org"),
    RefManageR = citation("RefManageR")[1],
    knitr = citation("knitr")[3],
    rmarkdown = citation("rmarkdown")[1]
)

Overview

derfinderData is a small data package with information extracted from BrainSpan (see here) r Citep(bib[['brainspan']]) for 24 samples restricted to chromosome 21. The BigWig files in this package can then be used by other packages for examples, such as in derfinder and derfinderPlot.

While you could download the data from BrainSpan r Citep(bib[['brainspan']]), this package is helpful for scenarios where you might encounter some difficulties such as the one described in this thread.

Data

The following code builds the phenotype table included in derfinderData. For two randomly selected structures, 12 samples were chosen with 6 of them being fetal samples and the other 6 coming from adult individuals. For the fetal samples, the age in PCW is transformed into age in years by

age_in_years = (age_in_PCW - 40) / 52

In other data sets you might want to subtract 42 instead of 40 if some observations have PCW up to 42.

## Construct brainspanPheno table
brainspanPheno <- data.frame(
    gender = c("F", "M", "M", "M", "F", "F", "F", "M", "F", "M", "M", "F", "M", "M", "M", "M", "F", "F", "F", "M", "F", "M", "M", "F"),
    lab = c("HSB97.AMY", "HSB92.AMY", "HSB178.AMY", "HSB159.AMY", "HSB153.AMY", "HSB113.AMY", "HSB130.AMY", "HSB136.AMY", "HSB126.AMY", "HSB145.AMY", "HSB123.AMY", "HSB135.AMY", "HSB114.A1C", "HSB103.A1C", "HSB178.A1C", "HSB154.A1C", "HSB150.A1C", "HSB149.A1C", "HSB130.A1C", "HSB136.A1C", "HSB126.A1C", "HSB145.A1C", "HSB123.A1C", "HSB135.A1C"),
    Age = c(-0.442307692307693, -0.365384615384615, -0.461538461538461, -0.307692307692308, -0.538461538461539, -0.538461538461539, 21, 23, 30, 36, 37, 40, -0.519230769230769, -0.519230769230769, -0.461538461538461, -0.461538461538461, -0.538461538461539, -0.519230769230769, 21, 23, 30, 36, 37, 40)
)
brainspanPheno$structure_acronym <- rep(c("AMY", "A1C"), each = 12)
brainspanPheno$structure_name <- rep(c("amygdaloid complex", "primary auditory cortex (core)"), each = 12)
brainspanPheno$file <- paste0("http://download.alleninstitute.org/brainspan/MRF_BigWig_Gencode_v10/bigwig/", brainspanPheno$lab, ".bw")
brainspanPheno$group <- factor(ifelse(brainspanPheno$Age < 0, "fetal", "adult"), levels = c("fetal", "adult"))

We can then save the phenotype information, which is included in derfinderData.

## Save pheno table
save(brainspanPheno, file = "brainspanPheno.RData")

Here is how the data looks like:

library("knitr")

## Explore pheno
p <- brainspanPheno[, -which(colnames(brainspanPheno) %in% c("structure_acronym", "structure_name", "file"))]
kable(p, format = "html", row.names = TRUE)

We can verify that this is indeed the information included in derfinderData.

## Rename our newly created pheno data
newPheno <- brainspanPheno

## Load the included data
library("derfinderData")

## Verify
identical(newPheno, brainspanPheno)

Using the phenotype information, you can use derfinder to extract the base-level coverage information for chromosome 21 from these samples. Then, you can export the data to BigWig files.

library("derfinder")

## Determine the files to use and fix the names
files <- brainspanPheno$file
names(files) <- gsub(".AMY|.A1C", "", brainspanPheno$lab)

## Load the data
system.time(fullCovAMY <- fullCoverage(
    files = files[brainspanPheno$structure_acronym == "AMY"], chrs = "chr21"
))
# user  system elapsed
# 4.505   0.178  37.676
system.time(fullCovA1C <- fullCoverage(
    files = files[brainspanPheno$structure_acronym == "A1C"], chrs = "chr21"
))
# user  system elapsed
# 2.968   0.139  27.704

## Write BigWig files
dir.create("AMY")
system.time(createBw(fullCovAMY, path = "AMY", keepGR = FALSE))
# user  system elapsed
# 5.749   0.332   6.045
dir.create("A1C")
system.time(createBw(fullCovA1C, path = "A1C", keepGR = FALSE))
# user  system elapsed
# 5.025   0.299   5.323

## Check that 12 files were created in each directory
all(c(length(dir("AMY")), length(dir("A1C"))) == 12)
# TRUE

## Save data for examples running on Windows
save(fullCovAMY, file = "fullCovAMY.RData")
save(fullCovA1C, file = "fullCovA1C.RData")

These BigWig files are available under extdata as shown below:

## Find AMY BigWigs
dir(system.file("extdata", "AMY", package = "derfinderData"))

## Find A1C BigWigs
dir(system.file("extdata", "A1C", package = "derfinderData"))

Reproducibility

Code for creating the vignette

## Create the vignette
library("rmarkdown")
system.time(render("derfinderData.Rmd", "BiocStyle::html_document"))

## Extract the R code
library("knitr")
knit("derfinderData.Rmd", tangle = TRUE)

Date the vignette was generated.

## Date the vignette was generated
Sys.time()

Wallclock time spent generating the vignette.

## Processing time in seconds
totalTime <- diff(c(startTime, Sys.time()))
round(totalTime, digits = 3)

R session information.

## Session info
library("sessioninfo")
session_info()

Bibliography

This vignette was generated using BiocStyle r Citep(bib[['BiocStyle']]) with knitr r Citep(bib[['knitr']]) and rmarkdown r Citep(bib[['rmarkdown']]) running behind the scenes.

Citations made with knitcitations r Citep(bib[['knitcitations']]).

## Print bibliography
PrintBibliography(bib, .opts = list(hyperlink = "to.doc", style = "html"))

leekgroup/derfinderData documentation built on May 10, 2023, 11:28 p.m.