NEWS.md
SIGNIFICANT USER-VISIBLE CHANGES
coverage_matrix() or expressed_regions() on Windows as rtracklayer::import() does work with local BigWig files on that operating system. I’m not sure if it will work with remote BigWig files given that remote BigWig file access on other operating systems is not working due to https://github.com/lawremi/rtracklayer/issues/83 and related issues.BUG FIXES
BUG FIXES
geo_info() for reading files on Windows where a trailing \r was added to all variables.implicit list embedding of S4 objects is deprecated warning that was noted at https://github.com/leekgroup/recount/runs/3286046827?check_suite_focus=true#step:20:1417.SIGNIFICANT USER-VISIBLE CHANGES
reproduce_ranges() the link to https://support.bioconductor.org/p/126148/#126173 which shows how to update the gene symbols in the RSE objects in recount.NEW FEATURES
getTPM() as discussed in https://support.bioconductor.org/p/124265 and based on Sonali Arora et al https://www.biorxiv.org/content/10.1101/445601v2.BUG FIXES
geo_characteristics() can deal with the scenario reported at https://support.bioconductor.org/p/116480/ by @Jacques.van-Helden.SIGNIFICANT USER-VISIBLE CHANGES
.load_install() as .load_check() as this function now only checks that the package(s) was installed and returns an error if missing. The error shows the user how to install the package(s) they are missing instead of installing them automatically. This complies with Marcel Ramos’ request at https://github.com/leekgroup/recount/issues/14.NEW FEATURES
download_retry() based on http://bioconductor.org/developers/how-to/web-query/ such that download_file() and other recount functions will re-try to download a file 3 times before giving up. This should help reduce the number of occasional failed Bioconductor nightly checks.SIGNIFICANT USER-VISIBLE CHANGES
add_metadata() and changed the default source from recount_brain_v1 to recount_brain_v2.NEW FEATURES
citation('recount')[5] now lists the recount-brain bioRxiv pre-print citation information.NEW FEATURES
download_study(type = 'rse-fc'). See Imada EL, Sanchez DF, et al, bioRxiv, 2019 https://www.biorxiv.org/content/10.1101/659490v1 for more information.BUG FIXES
geo_characteristics() more robust since currently rentrez can occasionally fails.NEW FEATURES
BUG FIXES
async to snaptron_query() which can be set to FALSE to address the issue reported at https://github.com/ChristopherWilks/snaptron/issues/11
BUG FIXES
reproduce_ranges() to match the URL change in Gencode from ftp://ftp.sanger.ac.uk to ftp://ftp.ebi.ac.uk
SIGNIFICANT USER-VISIBLE CHANGES
add_metadata() can now download the recount_brain_v2 data.SIGNIFICANT USER-VISIBLE CHANGES
rse_tx URLs now point to v2 to reflect recent changes by Fu et al.BUG FIXES
SIGNIFICANT USER-VISIBLE CHANGES
add_metadata() and add_predictions() now return the sample metadata or predictions when the rse argument is missing.NEW FEATURES
add_metadata() which can be used to append curated metadata to a recount rse object. Currently, add_metadata() only supports the recount_brain_v1 data available at http://lieberinstitute.github.io/recount-brain/ and to be further described in Razmara et al, in prep, 2018.BUG FIXES
geo_characteristics() which affected the Windows build machines.BUG FIXES
download_study(), add another test for the versions, and fix a NOTE in R CMD check.NEW FEATURES
download_study() can now download the transcript counts (rse_tx.RData) files. The transcript estimation is described in Fu et al, 2018.SIGNIFICANT USER-VISIBLE CHANGES
download_study() now has a version parameter (defaults to 2). This argument controls which version of the files to download based on the change on how exons were defined. Version 1 are reduced exons while version 2 are disjoint exons as described in further detail in the documentation tab of the recount website https://jhubiostatistics.shinyapps.io/recount/.recount_url and the example rse_gene_SRP009615 have been updated to match the changes in version 2.BUG FIXES
reproduce_ranges() since disjoint exons are more useful than reduced exons for downstream analyses.SIGNIFICANT USER-VISIBLE CHANGES
SIGNIFICANT USER-VISIBLE CHANGES
BiocStyle::html_document that was recently released.NEW FEATURES
coverage_matrix() now has two new arguments: scale and round. Use scale = FALSE to get raw coverage counts, which you can then scale with scale_counts(). scale is set to TRUE by default, so the counts are scaled to a library size of 40 million reads. round is set to FALSE by default, but can be set to TRUE if you want to get integer counts, just as in the default of scale_counts().SIGNIFICANT USER-VISIBLE CHANGES
add_predictions() to latest. Internally, that’s still 0.0.03.NEW FEATURES
add_predictions() function which appends the predicted phenotypes to a RSE object downloaded with recount. The phenotypes were predicted by Shannon Ellis et al, 2017 (citation coming up soon!).SIGNIFICANT USER-VISIBLE CHANGES
NEW FEATURES
getRPKM() which can be used with RangedSummarizedExperiment objects from recount and from other sources.SIGNIFICANT USER-VISIBLE CHANGES
recount_url now includes the URLs for the GTEx bigWig files.SIGNIFICANT USER-VISIBLE CHANGES
coverage_matrix() now returns a RangedSummarizedExperiment object. This matches the behavior of recount.bwtool::coverage_matrix_bwtool() and is more consistent with the use of RSE objects in recount.BUG FIXES
coverage_matrix()’s helper function .read_pheno() was failing for some projects.BUG FIXES
coverage_matrix(). They were being incorrectly multiplied by 100.SIGNIFICANT USER-VISIBLE CHANGES
SIGNIFICANT USER-VISIBLE CHANGES
TxDb.Hsapiens.UCSC.hg38.knownGene completely from recount and will be using Gencode v25 instead.SIGNIFICANT USER-VISIBLE CHANGES
recount project!SIGNIFICANT USER-VISIBLE CHANGES
snaptron_query() has been changed accordingly.SIGNIFICANT USER-VISIBLE CHANGES
reproduce_ranges() now has the db argument. By default it’s set to TxDb.Hsapiens.UCSC.hg38.knownGene to reproduce the actual information used in recount. But it can also be used with EnsDb.Hsapiens.v79 to use the ENSEMBL annotation. Then with coverage_matrix() you can get the counts for either an updated TxDb.Hsapiens.UCSC.hg38.knownGene or for EnsDb.Hsapiens.v79 at the exon and/or gene levels as shown in the vignette.SIGNIFICANT USER-VISIBLE CHANGES
SciServer compute to access all the recount data (over 6 TB) via http://www.sciserver.org/
NEW FEATURES
snaptron_query() which queries Intropolis via Snaptron to find if an exon-exon junction is present in the data.NEW FEATURES
recount
reproduce_ranges() for re-creating the gene or exon level information used in the recount project.abstract_search() for identifying SRA projects of interest by searching the abstracts.browse_study() for opening a browser tab for further exploring a project.download_study() for downloading the data from the recount project.scale_counts() for properly scaling the counts before performing a differential expression analysis with the RangedSummarizedExperiment objects hosted in the recount project.expressed_regions() for defining the expressed regions in a chromosome for a given SRA study.coverage_matrix() for computing the coverage matrix based on the regions of interest for a given SRA study.geo_info() for obtaining sample information from GEO.find_geo() for finding the GEO accession id given a SRA run accession (id). This function will be useful for SRA projects that did not have GEO entries at the time recount’s data was created.geo_characteristics() for building a data.frame() from geo_info()’s results for the characteristics.all_metadata() which downloads all the phenotype data for all projects. This function can be useful for identifying projects and/or samples of interests.