README.md

In alexvpickering/rkal: Run, Import, and Export 'kallisto' Quantifications

rkal installation

rkal can be installed as follows:

remotes::install.packages('alexvpickering/rkal')

kallisto installation

rkal requires that kallisto is installed and available on the command line. Please see here for instructions. To verify that kallisto is available, the following can be run from R:

system('kallisto')

Getting started using rkal

Prior to pseudoalignment, an index of the transcriptome must first be built:

#This will build the human Ensembl94 index for kallisto in the working directory
#this only needs to be run once
indices_dir <- getwd()
build_kallisto_index(indices_dir = indices_dir,
                     species = 'homo_sapiens', release = '94')

Next, we download an example fastq file with GEOfastq. Skip this step if you have your own fastq files:

library(GEOfastq)
data_dir <- tempdir()

# first get metadata needed and download example fastq file
srp_meta <- crawl_gsms('GSM4875733')
res <- get_fastqs(srp_meta, data_dir)

Next we collect fastq file metadata needed to run pseudoalignement (are fastq files paired or single-end? Are there any replicates aka samples split across multiple files?):

# we can get the necessary metadata data.frame for fastqs from GEOfastq
quant_meta <- get_quant_meta(srp_meta, data_dir)

# for personal fastq files, a GUI will request this info in the next step
# you can also create it programatically
# (see required columns in `quant_meta` description of `?run_kallisto_bulk`)

We are now ready to run pseudoalignment:

# can exclude quant_meta for personal fastqs (will invoke GUI)
res <- run_kallisto_bulk(indices_dir, data_dir, quant_meta)

If you plan to use crossmeta or dseqr, you can easily generate a suitably annotated ExpressionSet:

eset <- load_seq(data_dir)

alexvpickering/rkal documentation built on Nov. 27, 2022, 8:38 p.m.