This is a tutorial I have presented for the class Genomics and Systems Biology at the University of Chicago. In this course the students learn about study design, normalization, and statistical testing for genomic studies. This is meant to introduce them to how these ideas are implemented in practice. The specific example is a differential expression analysis with edgeR starting with a table of counts and ending with a list of differentially expressed genes.
Past versions:
| --- | |
| title: "Untitled" | |
| output: html_document | |
| date: "2025-02-28" | |
| --- | |
| ```{r setup, include=FALSE} | |
| # knitr output hook to redact alphanumeric strings | |
| # | |
| # Adapted from |
| NAME := main | |
| all: $(NAME).pdf | |
| %.pdf: %.tex | |
| sed -i s/'\\includeonly'/'%\\includeonly'/ $(NAME).tex | |
| pdflatex -shell-escape $(NAME) | |
| # Insert additional calls to pdflatex or bibtex here, | |
| # depending on the complexity of your document |
| # HGEN 473 - Genomics | |
| # Spring 2017 | |
| # Tuesday, May 9 & Thursday, May 11 | |
| # | |
| # RNA-seq analysis with R/Bioconductor | |
| # | |
| # John Blischak | |
| # | |
| # Last updated: 2020-04-08 |
| #!/usr/bin/env python | |
| # Unfollow all Twitter accounts that aren't following you back | |
| # | |
| # Twitter API: | |
| # https://developer.twitter.com/en/docs/twitter-api | |
| # https://github.com/python-twitter-tools/twitter | |
| # https://developer.twitter.com/en/portal/projects/new | |
| # | |
| # Authentication: |
| #!/usr/bin/env Rscript | |
| # This example code implements the fork and pull request workflow for GitHub | |
| # directly from R using the packages gh and git2r. gh provides an R interface to | |
| # the GitHub REST API v3, and git2r provides an R interface to libgit2. It | |
| # submits the Pull Request to the practice repository Spoon-Knife. | |
| # | |
| # The following sequence of steps are performed: | |
| # | |
| # authenticate -> fork -> clone -> branch -> edit -> add -> commit -> push -> |
| Additional_repositories | |
| Author | |
| Authors@R | |
| Biarch | |
| BugReports | |
| BuildVignettes | |
| Built | |
| ByteCompile | |
| Classification/ACM | |
| Classification/ACM-2012 |
| calc3 <- function(sets) | |
| { | |
| sets <- check_sets(sets) | |
| set_lengths <- vapply(sets, length, 0) | |
| set_order <- order(set_lengths) | |
| sets <- sets[set_order] | |
| set_lengths <- set_lengths[set_order] | |
| n_sets <- length(sets) | |
| set_names <- names(sets) |
| #!/usr/bin/env Rscript | |
| # Do CRAN packages that depend on the stats package use a copyleft license? | |
| # https://twitter.com/cimentadaj/status/1154420408508043264 | |
| Sys.Date() | |
| ## [1] "2019-07-26" | |
| library(stringr) |
[kallisto][] is a new method for processing RNA-seq data. By pseudoaligning reads to a transcriptome instead of aligning reads to a genome, the quantification step is much faster. While the computational speedup will be huge for projects with many samples and/or with organisms with large genomes, I was curious how much time would be saved using [kallisto][] on a small RNA-seq project for an organism with a smaller genome. To perform this comparison, I downloaded 6 fastq files from a recent yeast RNA-seq study on GEO. I chose [Subread][subread] as the comparison method because it performs read alignment but is optimized for quickly obtaining gene counts (it soft clips reads instead of trying to map exact exon-exon boundaries).