(Refer back to the RNA-seq data analysis lesson).
Take a look at the preprint: Collado-Torres, Leonardo, et al. “recount: A large-scale resource of analysis-ready RNA-seq expression data.” bioRxiv (2016): 068478. It’s available at http://biorxiv.org/content/early/2016/08/08/068478.
Here’s the abstract:
recount is a resource of processed and summarized expression data spanning nearly 60,000 human RNA-seq samples from the Sequence Read Archive (SRA). The associated recount Bioconductor package provides a convenient API for querying, downloading, and analyzing the data. Each processed study consists of meta/phenotype data, the expression levels of genes and their underlying exons and splice junctions, and corresponding genomic annotation. We also provide data summarization types for quantifying novel transcribed sequence including base-resolution coverage and potentially unannotated splice junctions. We present workflows illustrating how to use recount to perform differential expression analysis including meta-analysis, annotation-free base-level analysis, and replication of smaller studies using data from larger studies. recount provides a valuable and user-friendly resource of processed RNA-seq datasets to draw additional biological insights from existing public data. The resource is available at https://jhubiostatistics.shinyapps.io/recount/.
This preprint describes an incredible resource. The authors here have downloaded and pre-processed several thousand RNA-seq studies from the raw data available in the SRA. They make this data available through a web application1 at where you can browse, search, and sort the studies they’ve processed, and it gives you direct links to download analysis-ready pre-processed count data and metadata. As the abstract mentions, you can access recount at jhubiostatistics.shinyapps.io/recount/.
In the accession search box, search for SRP026387. This has some pre-processed data from a study looking at the Wnt/Beta-catenin pathway and how that’s affected by androgen deprivation therapy in treating prostate cancer. You can learn more about the data and the study by clicking the accession number hyperlink.
Androgen ablation therapy (AAT) is standard treatment for locally-advanced/metastatic prostate cancer (PCa). Many patients develop castration-resistance (CRPCa) after ~2-3 years, with a poor prognosis. The molecular mechanisms underlying CRPCa progression are unclear. mRNA-Seq was performed on tumours from 7 patients with locally-advanced/metastatic PCa before and ~22 weeks after AAT initiation. Differentially regulated genes were identified in treatment pairs. Overall design: Tumour biopsies from 7 patients were taken before and after AAT treatment.
You could get the gene-level counts and metadata by clicking the appropriate links. I downloaded the data and and cleaned up the metadata and gene identifiers a bit, and made that data available on the Data page. The files are called SRP026387_scaledcounts.csv and SRP026387_metadata.csv.
Go to the Data page, download these data files, and load them into R. As we did in the original lesson) under the importing data heading, you can load the tidyverse and load them with read_csv(), but before you can import them into a DESeqDataSet you’ll have to convert them back to a regular data frame. You could alternatively use base R’s read.csv() function and not have to worry about it.
# Load the count data and metadata. Here, the "data" folder
# is relative to my current working directory. Load the data
# From wherever you keep it, or just load it directly from
# the web using the bioconnector.org/data/... URLs.
mycounts <- read.csv("data/SRP026387_scaledcounts.csv")
metadata <- read.csv("data/SRP026387_metadata.csv")Take a look at the metadata:
metadata## id replicate prepost
## 1 SRR923920 R1 Pre
## 2 SRR923921 R2 Pre
## 3 SRR923923 R4 Pre
## 4 SRR923924 R5 Pre
## 5 SRR923925 R6 Pre
## 6 SRR923927 R1 Post
## 7 SRR923926 R7 Pre
## 8 SRR923929 R3 Post
## 9 SRR923928 R2 Post
## 10 SRR923930 R4 Post
## 11 SRR923931 R5 Post
## 12 SRR923933 R7 Post
## 13 SRR923932 R6 PostAnd the first few rows of the count data:
head(mycounts)## ensgene SRR923920 SRR923921 SRR923923 SRR923924 SRR923925
## 1 ENSG00000000003 159 1508 2062 1369 1625
## 2 ENSG00000000005 0 0 17 7 0
## 3 ENSG00000000419 416 312 505 404 609
## 4 ENSG00000000457 529 507 718 851 687
## 5 ENSG00000000460 214 197 330 279 249
## 6 ENSG00000000938 170 278 100 142 174
## SRR923927 SRR923926 SRR923929 SRR923928 SRR923930 SRR923931 SRR923933
## 1 401 2024 1075 719 1104 1441 1125
## 2 0 0 7 8 0 6 6
## 3 376 754 387 324 454 652 640
## 4 722 599 705 689 855 1016 883
## 5 213 217 281 220 184 379 383
## 6 537 214 206 255 110 253 147
## SRR923932
## 1 1376
## 2 2
## 3 620
## 4 671
## 5 269
## 6 185Finally, load the annotation data (best to do this with dplyr’s read_csv() because it’s pretty big. We’ll need tidyverse later to do some joins).
library(tidyverse)
anno <- read_csv("data/annotables_grch38.csv")
anno## # A tibble: 66,531 x 9
## ensgene entrez symbol chr start end strand
## <chr> <int> <chr> <chr> <int> <int> <int>
## 1 ENSG00000000003 7105 TSPAN6 X 100627109 100639991 -1
## 2 ENSG00000000005 64102 TNMD X 100584802 100599885 1
## 3 ENSG00000000419 8813 DPM1 20 50934867 50958555 -1
## 4 ENSG00000000457 57147 SCYL3 1 169849631 169894267 -1
## 5 ENSG00000000460 55732 C1orf112 1 169662007 169854080 1
## 6 ENSG00000000938 2268 FGR 1 27612064 27635277 -1
## 7 ENSG00000000971 3075 CFH 1 196651878 196747504 1
## 8 ENSG00000001036 2519 FUCA2 6 143494811 143511690 -1
## 9 ENSG00000001084 2729 GCLC 6 53497341 53616970 -1
## 10 ENSG00000001167 4800 NFYA 6 41072945 41099976 1
## # ... with 66,521 more rows, and 2 more variables: biotype <chr>,
## # description <chr>tidy=TRUE argument, with the design formula being the prepost variable from the metadata.%>% inner_join(anno, by=c("row"="ensgene"))).Here’s what your results should look like. I’m showing actual values for the first few genes so you can check to see if your results are close. Don’t worry about rounding errors.
## # A tibble: 10 x 6
## row log2FoldChange pvalue padj symbol description
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 ENSG00000151503 3.46 3.78e-37 1.03e-32 NCAPD3 ?
## 2 ENSG00000249599 3.62 1.41e-29 1.93e-25 BMPR1B-AS1 ?
## 3 ENSG00000228278 7.48 4.04e-28 3.68e-24 ORM2 ?
## 4 ? ? ? ? ? ?
## 5 ? ? ? ? ? ?
## 6 ? ? ? ? ? ?
## 7 ? ? ? ? ? ?
## 8 ? ? ? ? ? ?
## 9 ? ? ? ? ? ?
## 10 ? ? ? ? ? ?Incidentally, the web application itself was built with R using the Shiny framework (https://shiny.rstudio.com/). Pete teaches a workshop on this!↩
Note that without further specifying to the results() function, the default is to treat the alphabetically first condition as the control, and the alphabetically second condition as the experimental group. Therefore, the differential expression values will be comparing “Pre” versus “Post”, since “Post” comes first.↩
Note that you may need to explicitly call select() from the dplyr library using dplyr::select(), because loading DESeq2 will mask dplyr’s select if you loaded dplyr or tidyverse first.↩