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.NEWS.md file to track changes to the package.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/11BUG FIXES
reproduce_ranges() to match the URL change in
Gencode from
ftp://ftp.sanger.ac.uk to ftp://ftp.ebi.ac.ukSIGNIFICANT USER-VISIBLE CHANGES
add_metadata() can now download the recount_brain_v2 data.BUG FIXES
NOTE about RefManageR.SIGNIFICANT USER-VISIBLE CHANGES
BiocManagerBUG FIXES
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.NEW FEATURES
read_counts().SIGNIFICANT USER-VISIBLE CHANGES
SIGNIFICANT USER-VISIBLE CHANGES
add_predictions() was bumped to version 0.0.05SIGNIFICANT 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.BUG FIXES
snaptron_query() to comply with recent changes in Snaptron.SIGNIFICANT USER-VISIBLE CHANGES
recount project!SIGNIFICANT USER-VISIBLE CHANGES
snaptron_query() can now access GTEx and TCGA data.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.BUF FIXES
NEW FEATURES
recountreproduce_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.