leekgroup / recount Goto Github PK
View Code? Open in Web Editor NEWR package for the recount2 project. Documentation website: http://leekgroup.github.io/recount/
Home Page: https://jhubiostatistics.shinyapps.io/recount/
R package for the recount2 project. Documentation website: http://leekgroup.github.io/recount/
Home Page: https://jhubiostatistics.shinyapps.io/recount/
Hello,
Thanks in advance!
I'm having issues trying to install recount in R (Ubuntu 22.04 host) that I've not experienced before. Below is the terminal output and session info;
> if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
Bioconductor version 3.19 (BiocManager 1.30.22), R 4.4.0 (2024-04-24)
> BiocManager::install("recount")
'getOption("repos")' replaces Bioconductor standard repositories, see
'help("repositories", package = "BiocManager")' for details.
Replacement repositories:
CRAN: https://cloud.r-project.org
Bioconductor version 3.19 (BiocManager 1.30.22), R 4.4.0 (2024-04-24)
Installing package(s) 'recount'
Warning message:
package ‘recount’ is not available for Bioconductor version '3.19'
A version of this package for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
> sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
time zone: Australia/Adelaide
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] BiocManager_1.30.22
loaded via a namespace (and not attached):
[1] compiler_4.4.0 tools_4.4.0
Package recount3 installs without issues as do many other packages however I still require recount.
As can be seen in the error, it has to do with the very recent release of the latest version of R and BiocManager.
I am looking for any ideas about how I can overcome this incompatibility issue?
Hello,
I am usually a happy camper with recount2
and very thankful for all the work you all have put into this tool! That being said, I have noticed a bug recently in which using the RangedSummarizedExperiment
objects from recount in R causes severe slowdowns to occur. I have tested this on multiple machines now and find this only happens with the RSE objects from recount and not with typical seq datasets that I analyze.
After a little while of digging into the objects, I noticed that the slowdown appears to be caused by the rowRanges
of the recount2 objects, specifically the symbol
column. Please see my example here:
library(recount)
library(DESeq2)
library(SummarizedExperiment)
rse_gene <- rse_gene_SRP009615
# Timer without ranges
system.time({
rse <- SummarizedExperiment(
assays = assays(rse_gene),
colData = colData(rse_gene),
# rowRanges = rowRanges(rse_gene)
)
rse$condition <- gsub(res$title, pattern = ".+ targeting ([a-zA-Z0-9]+) gene.+", replacement = "sh\\1")
dds <- DESeqDataSet(rse, design = ~condition)
dds <- DESeq(dds)
})
# Timer with ranges
system.time({
rse <- SummarizedExperiment(
assays = assays(rse_gene),
colData = colData(rse_gene),
rowRanges = rowRanges(rse_gene)
)
rse$condition <- gsub(res$title, pattern = ".+ targeting ([a-zA-Z0-9]+) gene.+", replacement = "sh\\1")
dds <- DESeqDataSet(rse, design = ~condition)
dds <- DESeq(dds)
})
# Timer with ranges but NULL symbol col
system.time({
rse <- SummarizedExperiment(
assays = assays(rse_gene),
colData = colData(rse_gene),
rowRanges = rowRanges(rse_gene)
)
rse$condition <- gsub(res$title, pattern = ".+ targeting ([a-zA-Z0-9]+) gene.+", replacement = "sh\\1")
rse@rowRanges$symbol <- NULL
dds <- DESeqDataSet(rse, design = ~condition)
dds <- DESeq(dds)
})
And here is the output of running this in the console:
> system.time({
+ rse <- SummarizedExperiment(
+ assays = assays(rse_gene),
+ colData = colData(rse_gene),
+ # rowRanges = rowRanges(rse_gene)
+ )
+ rse$condition <- gsub(res$title, pattern = ".+ targeting ([a-zA-Z0-9]+) gene.+", replacement = "sh\\1")
+ dds <- DESeqDataSet(rse, design = ~condition)
+ dds <- DESeq(dds)
+ })
converting counts to integer mode
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
user system elapsed
30.018 0.040 30.070
Warning message:
In DESeqDataSet(rse, design = ~condition) :
some variables in design formula are characters, converting to factors
> system.time({
+ rse <- SummarizedExperiment(
+ assays = assays(rse_gene),
+ colData = colData(rse_gene),
+ rowRanges = rowRanges(rse_gene)
+ )
+ rse$condition <- gsub(res$title, pattern = ".+ targeting ([a-zA-Z0-9]+) gene.+", replacement = "sh\\1")
+ dds <- DESeqDataSet(rse, design = ~condition)
+ dds <- DESeq(dds)
+ })
converting counts to integer mode
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
user system elapsed
745.284 0.810 746.438
Warning message:
In DESeqDataSet(rse, design = ~condition) :
some variables in design formula are characters, converting to factors
> # Timer with ranges but NULL symbol col
> system.time({
+ rse <- SummarizedExperiment(
+ assays = assays(rse_gene),
+ colData = colData(rse_gene),
+ rowRanges = rowRanges(rse_gene)
+ )
+ rse$condition <- gsub(res$title, pattern = ".+ targeting ([a-zA-Z0-9]+) gene.+", replacement = "sh\\1")
+ rse@rowRanges$symbol <- NULL
+ dds <- DESeqDataSet(rse, design = ~condition)
+ dds <- DESeq(dds)
+ })
converting counts to integer mode
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
user system elapsed
29.840 0.028 29.890
Warning message:
In DESeqDataSet(rse, design = ~condition) :
some variables in design formula are characters, converting to factors
As you can see, with the rowRanges
included, the code took ~25x longer to finish running. However, simply NULL
ing the symbol
column of the rowRanges
was able to prevent this slowdown. I think the issue is that the symbol
column was a CharacterList
which may perform inefficiently for these application. Anyways, hopefully this helps -- thanks again for all the work you and your team does!!
> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] recount_1.18.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2
[9] ggplot2_3.3.5 tidyverse_1.3.1 DESeq2_1.32.0 SummarizedExperiment_1.22.0
[13] MatrixGenerics_1.4.0 matrixStats_0.59.0 EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.16.2
[17] AnnotationFilter_1.16.0 GenomicFeatures_1.44.0 AnnotationDbi_1.54.1 Biobase_2.52.0
[21] GenomicRanges_1.44.0 GenomeInfoDb_1.28.1 IRanges_2.26.0 S4Vectors_0.30.0
[25] BiocGenerics_0.38.0 tximport_1.20.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 Hmisc_4.5-0 BiocFileCache_2.0.0
[5] plyr_1.8.6 lazyeval_0.2.2 splines_4.1.0 BiocParallel_1.26.1
[9] digest_0.6.27 foreach_1.5.1 htmltools_0.5.1.1 fansi_0.5.0
[13] magrittr_2.0.1 checkmate_2.0.0 memoise_2.0.0 BSgenome_1.60.0
[17] cluster_2.1.2 limma_3.48.1 Biostrings_2.60.1 annotate_1.70.0
[21] modelr_0.1.8 prettyunits_1.1.1 jpeg_0.1-8.1 colorspace_2.0-2
[25] blob_1.2.1 rvest_1.0.0 rappdirs_0.3.3 haven_2.4.1
[29] xfun_0.24 crayon_1.4.1 RCurl_1.98-1.3 jsonlite_1.7.2
[33] genefilter_1.74.0 GEOquery_2.60.0 iterators_1.0.13 survival_3.2-11
[37] VariantAnnotation_1.38.0 glue_1.4.2 gtable_0.3.0 zlibbioc_1.38.0
[41] XVector_0.32.0 DelayedArray_0.18.0 rentrez_1.2.3 scales_1.1.1
[45] rngtools_1.5 DBI_1.1.1 derfinderHelper_1.26.0 derfinder_1.26.0
[49] Rcpp_1.0.7 xtable_1.8-4 progress_1.2.2 htmlTable_2.2.1
[53] bumphunter_1.34.0 foreign_0.8-81 bit_4.0.4 Formula_1.2-4
[57] htmlwidgets_1.5.3 httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2
[61] pkgconfig_2.0.3 XML_3.99-0.6 farver_2.1.0 nnet_7.3-16
[65] dbplyr_2.1.1 locfit_1.5-9.4 utf8_1.2.1 reshape2_1.4.4
[69] tidyselect_1.1.1 labeling_0.4.2 rlang_0.4.11 munsell_0.5.0
[73] cellranger_1.1.0 tools_4.1.0 cachem_1.0.5 downloader_0.4
[77] cli_3.0.0 generics_0.1.0 RSQLite_2.2.7 broom_0.7.8
[81] fastmap_1.1.0 yaml_2.2.1 knitr_1.33 bit64_4.0.5
[85] fs_1.5.0 KEGGREST_1.32.0 doRNG_1.8.2 xml2_1.3.2
[89] biomaRt_2.48.2 compiler_4.1.0 rstudioapi_0.13 filelock_1.0.2
[93] curl_4.3.2 png_0.1-7 reprex_2.0.0 geneplotter_1.70.0
[97] stringi_1.7.3 GenomicFiles_1.28.0 lattice_0.20-44 ProtGenerics_1.24.0
[101] Matrix_1.3-4 vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.0
[105] data.table_1.14.0 bitops_1.0-7 qvalue_2.24.0 rtracklayer_1.52.0
[109] R6_2.5.0 BiocIO_1.2.0 latticeExtra_0.6-29 gridExtra_2.3
[113] codetools_0.2-18 assertthat_0.2.1 rjson_0.2.20 withr_2.4.2
[117] GenomicAlignments_1.28.0 Rsamtools_2.8.0 GenomeInfoDbData_1.2.6 hms_1.1.0
[121] grid_4.1.0 rpart_4.1-15 lubridate_1.7.10 base64enc_0.1-3
[125] restfulr_0.0.13
I've received feedback from users interested in being able to plot the base-level coverage for a set of samples in recount2.
One way of doing this is via derfinderPlot::plotRegionCoverage()
. I could write a function where the user specifies:
run
for SRA and GTEx, gdc_file_id
for TCGA),Then internally run derfinder::loadCoverage()
accessing the data from either local bigwigs (if at JHPCE, SciServer or if the user downloaded them) or via the web. Something that could be a bit of a bottle neck is the annotation needed for derfinderPlot::plotRegionCoverage()
. That would normally involve creating a genomic state object from Gencode v25 hg38 which takes time even for one chromosome. I could pre-make them split by chr and have the function download the files from the web behind the scenes. In any case, right now this approach would have to deal with
rafalab/bumphunter#15 since derfinderPlot::plotRegionCoverage()
requires the output of bumphunter::matchGenes()
.
Another option would be to open the data in the UCSC genome browser or something like that.
A third option could be to use epivizr
http://bioconductor.org/packages/release/bioc/vignettes/epivizr/inst/doc/IntroToEpivizr.html. Although I'm not sure that it can read bigwig files from the web.
Thoughts @andrewejaffe @jtleek ?
Right now, users can get the list of URLs from recount::recount_url
and load them into IGV or similar programs.
I would like to compare the distribution of gene expression between samples from TCGA and GTEx. So I download the RSE from TCGA and GTEx and do scale_counts and combine two data set together for histogram plots. So after combing the two data set, do I need to do additional normalization for comparison?
Thanks
I'm running into an error with snaptron_query. It appears to be related to processing snaptron return values, i.e. 18 columns instead of the expected 19? Either the 'samples' or 'read_coverage_by_sample' column is missing and everything appears shifted by one. Have tried troubleshooting everything on my end, not sure what the issue is.
Warning messages:
1: In matrix(strsplit(jxs, "\t")[[1]], ncol = 19) :
data length [18] is not a sub-multiple or multiple of the number of columns [19]
2: In matrix(strsplit(jxs, "\t")[[1]], ncol = 19) :
data length [18] is not a sub-multiple or multiple of the number of columns [19]
3: In matrix(strsplit(jxs, "\t")[[1]], ncol = 19) :
data length [18] is not a sub-multiple or multiple of the number of columns [19]
4: In (S4Vectors:::coercerToClass(element.type))(v, ...) :
NAs introduced by coercion
5: In snaptron_query(junctions) : NAs introduced by coercion
Previously @jtleek has requested a function for loading just the RSE files for a given tissue from GTEx or TCGA. At least two people mentioned something similar at BioC2019.
This is a reminder for myself to implement this functionality :P
I have been trying to download the following GSE73721 dataset (a dataset that features on the recount website) but cannot using recount library.
Below is the list of relevant commands I ran in the session :
library(recount)
project_info <- abstract_search('GSE32465')
project_info
number_samples species
340 12 human
project_info <- abstract_search('GSE73721')
project_info
[1] number_samples species abstract project
<0 rows> (or 0-length row.names)
project_info <- abstract_search('SRP064454')
project_info
[1] number_samples species abstract project
<0 rows> (or 0-length row.names)
The data is there on recount website. I would be grateful for your help.
(below is the csv file from the recount website)
accession number of samples species abstract
SRP064454 41 human Astrocytes were purified from fetal and adult human brain tissue using an immunopanning method with the HepaCAM antibody. Samples were taken from otherwise 'healthy' pieces of tissue, unless otherwise specified. Overall design: 6 fetal astrocyte samples, 12 adult astrocyte samples, 8 GBM or sclerotic hippocampal samples, 4 whole human cortex samples, 4 adult mouse astrocyte samples, and 11 human samples of other purified CNS cell types
Thanks,
> dev.tools::sessionInfo()
Session info -----------------------------------------------------------------------------------------------------------
setting value
version R version 3.3.1 (2016-06-21)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
tz
date 2016-12-19
Packages ---------------------------------------------------------------------------------------------------------------
package * version date source
acepack 1.4.1 2016-10-29 CRAN (R 3.3.1)
AnnotationDbi 1.34.4 2016-10-06 Bioconductor
assertthat 0.1 2013-12-06 CRAN (R 3.3.1)
Biobase * 2.32.0 2016-05-16 Bioconductor
BiocGenerics * 0.18.0 2016-05-16 Bioconductor
BiocParallel 1.6.6 2016-12-02 Bioconductor
biomaRt 2.28.0 2016-09-03 Bioconductor
Biostrings 2.40.2 2016-08-10 Bioconductor
bitops 1.0-6 2013-08-17 CRAN (R 3.3.0)
BSgenome 1.40.1 2016-12-02 Bioconductor
bumphunter 1.12.0 2016-05-16 Bioconductor
cluster 2.0.5 2016-10-08 CRAN (R 3.3.1)
codetools 0.2-15 2016-10-05 CRAN (R 3.3.1)
colorspace 1.3-1 2016-11-18 CRAN (R 3.3.1)
data.table 1.9.8 2016-11-25 CRAN (R 3.3.1)
DBI 0.5-1 2016-09-10 CRAN (R 3.3.1)
derfinder 1.8.0 2016-12-18 Bioconductor
derfinderHelper 1.6.3 2016-05-17 Bioconductor
devtools 1.12.0 2016-06-24 CRAN (R 3.3.1)
digest 0.6.10 2016-08-02 CRAN (R 3.1.0)
doRNG 1.6 2014-03-07 CRAN (R 3.3.0)
downloader 0.4 2015-07-09 CRAN (R 3.3.0)
foreach 1.4.3 2015-10-13 CRAN (R 3.3.0)
foreign 0.8-67 2016-09-13 CRAN (R 3.3.1)
Formula 1.2-1 2015-04-07 CRAN (R 3.3.0)
GenomeInfoDb * 1.8.7 2016-12-02 Bioconductor
GenomicAlignments 1.8.4 2016-12-02 Bioconductor
GenomicFeatures 1.24.5 2016-12-02 Bioconductor
GenomicFiles 1.8.0 2016-05-12 Bioconductor
GenomicRanges * 1.24.3 2016-12-02 Bioconductor
GEOquery 2.38.4 2016-05-17 Bioconductor
ggplot2 2.2.0 2016-11-11 CRAN (R 3.3.1)
gridExtra 2.2.1 2016-02-29 CRAN (R 3.3.0)
gtable 0.2.0 2016-02-26 CRAN (R 3.1.0)
Hmisc 4.0-0 2016-11-01 CRAN (R 3.3.1)
htmlTable 1.7 2016-10-19 CRAN (R 3.3.1)
htmltools 0.3.5 2016-03-21 CRAN (R 3.3.1)
httr 1.2.1 2016-07-03 CRAN (R 3.3.1)
IRanges * 2.6.1 2016-12-02 Bioconductor
iterators 1.0.8 2015-10-13 CRAN (R 3.3.0)
jsonlite 1.1 2016-09-14 CRAN (R 3.3.1)
knitr 1.15.1 2016-11-22 CRAN (R 3.3.1)
lattice 0.20-34 2016-09-06 CRAN (R 3.3.1)
latticeExtra 0.6-28 2016-02-09 CRAN (R 3.3.0)
lazyeval 0.2.0 2016-06-12 CRAN (R 3.1.0)
locfit 1.5-9.1 2013-04-20 CRAN (R 3.3.0)
magrittr 1.5 2014-11-22 CRAN (R 3.1.0)
Matrix 1.2-7.1 2016-09-01 CRAN (R 3.3.1)
matrixStats 0.51.0 2016-10-09 CRAN (R 3.3.1)
memoise 1.0.0 2016-01-29 CRAN (R 3.3.0)
munsell 0.4.3 2016-02-13 CRAN (R 3.1.0)
nnet 7.3-12 2016-02-02 CRAN (R 3.3.1)
pkgmaker 0.22 2014-05-14 CRAN (R 3.3.0)
plyr 1.8.4 2016-06-08 CRAN (R 3.3.1)
qvalue 2.4.2 2016-05-17 Bioconductor
R6 2.2.0 2016-10-05 CRAN (R 3.3.1)
RColorBrewer 1.1-2 2014-12-07 CRAN (R 3.1.0)
Rcpp 0.12.8 2016-11-17 CRAN (R 3.3.1)
RCurl 1.95-4.8 2016-03-01 CRAN (R 3.3.1)
recount * 1.0.6 2016-12-18 Bioconductor
registry 0.3 2015-07-08 CRAN (R 3.3.0)
rentrez 1.0.4 2016-10-26 CRAN (R 3.3.1)
reshape2 1.4.2 2016-10-22 CRAN (R 3.3.1)
rngtools 1.2.4 2014-03-06 CRAN (R 3.3.0)
rpart 4.1-10 2015-06-29 CRAN (R 3.3.0)
Rsamtools 1.26.1 2016-12-18 Bioconductor
RSQLite 1.1 2016-11-27 CRAN (R 3.3.1)
rtracklayer 1.34.1 2016-12-18 Bioconductor
S4Vectors * 0.10.3 2016-09-27 Bioconductor
scales 0.4.1 2016-11-09 CRAN (R 3.3.1)
stringi 1.1.2 2016-10-01 CRAN (R 3.3.1)
stringr 1.1.0 2016-08-19 CRAN (R 3.3.1)
SummarizedExperiment * 1.2.3 2016-12-02 Bioconductor
survival 2.40-1 2016-10-30 CRAN (R 3.3.1)
tibble 1.2 2016-08-26 CRAN (R 3.3.1)
VariantAnnotation 1.18.7 2016-12-02 Bioconductor
withr 1.0.2 2016-06-20 CRAN (R 3.3.1)
XML 3.98-1.5 2016-11-10 CRAN (R 3.3.1)
xtable 1.8-2 2016-02-05 CRAN (R 3.1.0)
XVector 0.12.1 2016-12-02 Bioconductor
zlibbioc 1.18.0 2016-05-16 Bioconductor
Hi so there is an option to download rse-exon, however I don't understand how the annotation works. For example, here is how KRAS which only has 6 exons look like.
edit: specifically I'm intereted in reconstructing from version two if this is even possible?
download_study("DRP001219", type = "rse-exon", outdir="./data/")
load(file.path( "./data/rse_exon.Rdata"))
count = assays(rse_exon)$counts
count[ grepl("ENSG00000133703", row.names(count)), , drop=F]
DRR014113
ENSG00000133703.11 78483
ENSG00000133703.11 4095
ENSG00000133703.11 5518
ENSG00000133703.11 388
ENSG00000133703.11 2350
ENSG00000133703.11 5462
ENSG00000133703.11 1972
ENSG00000133703.11 2725
ENSG00000133703.11 235
ENSG00000133703.11 1108
ENSG00000133703.11 2495
ENSG00000133703.11 0
ENSG00000133703.11 0
So how would I know what is for exon 1, 2 or 3? There is an object call recount_exon but that only gives the coordinates.
Hi,
I downloaded the rse_tx_brain from https://jhubiostatistics.shinyapps.io/recount/ for both TCGA and GTEX. I only renamed them as "rse_tx_brain_TCGA.RData" and "rse_tx_brain_GTEX.RData" ( otherwise both objects would be called "rse_tx_brain.RData"
When I load both the objects, I notice a lot of NA's for a lot of transcripts - I was wondering why that is ? The NA's most likely make getRPKM() fail too.
> rm(list=ls())
> library(SummarizedExperiment)
> setwd("~/HollandLabShared/Sonali/recount_brain")
> gtex = get(load("rse_tx_brain_GTEX.Rdata"))
> tcga = get(load("rse_tx_brain_TCGA.RData"))
>
> tcga_rpkm = getRPKM(tcga)
Error in base::colSums(x, na.rm = na.rm, dims = dims, ...) :
'x' must be an array of at least two dimensions
> gtex_rpkm = getRPKM(gtex)
Error in base::colSums(x, na.rm = na.rm, dims = dims, ...) :
'x' must be an array of at least two dimensions
I was under the impression that maybe the same transcript is NA for all samples - but that is not the case.
> length(which(is.na(assay(gtex[,1]))))
[1] 522
> length(which(is.na(assay(gtex[,151]))))
[1] 436
>
> length(which(is.na(assay(gtex[,20]))))
[1] 892
> length(which(is.na(assay(gtex[,251]))))
[1] 436
> summary(as.numeric(assay(tcga[grep("ENST00000304053.10", rownames(tcga)), ])))
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0 0 0 0 0 0 543
Kindly advise.
Thanks,
Sonali.
I'm migrating the discussion on this topic to the issues page.
About a week ago https://gist.github.com/ronstewart reported that POU5F1 is missing from recount in an email to me. That lead to my reply involving the following gist https://gist.github.com/lcolladotor/374a0de6be5c202bbf216295989e534a:
I looked into the issue you reported and indeed POU5F1 is not part of
recount. It's because it's not part of
TxDb.Hsapiens.UCSC.hg38.knownGene which is the annotation we used.
However, you can still use the recount package to compute the coverage
matrix at the exon level (the gene level is just the sum of the exon
level) by modifying the code at
http://bioconductor.org/packages/release/bioc/vignettes/recount/inst/doc/recount-quickstart.html#using-anothernewer-annotation.
Here are the actual details of what I did
https://gist.github.com/lcolladotor/374a0de6be5c202bbf216295989e534a.
Ron replied with a comment on the gist where he is concerned that other genes might be missing in recount.
In recount::reproduce_ranges()
we use GenomicFeatures::genes()
which by default has the argument has the argument single.strand.genes.only
set to FALSE
. As seen in the documentation, this argument also drops genes that are on two different chromosomes.
single.strand.genes.only
TRUE or FALSE. If TRUE (the default), then genes that have exons located on both strands of the same chromosome or on two different chromosomes are dropped. In that case, the genes are returned in a GRanges object. Otherwise, all genes are returned in a GRangesList object with the columns specified thru the columns argument set as top level metadata columns. (Please keep in mind that the top level metadata columns of a GRangesList object are not displayed by the show method.)
This explains why POU5F1
is missing as shown below because it is present in chr6
(a canonical chr) and a few alternative chromosomes.
> library('GenomicFeatures')
> library('TxDb.Hsapiens.UCSC.hg38.knownGene')
> g <- genes(TxDb.Hsapiens.UCSC.hg38.knownGene, single.strand.genes.only = FALSE)
> g['5460']
GRangesList object of length 1:
$5460
GRanges object with 7 ranges and 0 metadata columns:
seqnames ranges strand
<Rle> <IRanges> <Rle>
[1] chr6 [31164337, 31180731] -
[2] chr6_GL000251v2_alt [ 2646761, 2653135] -
[3] chr6_GL000252v2_alt [ 2423653, 2430027] -
[4] chr6_GL000253v2_alt [ 2474838, 2481213] -
[5] chr6_GL000254v2_alt [ 2508452, 2514827] -
[6] chr6_GL000255v2_alt [ 2422370, 2428743] -
[7] chr6_GL000256v2_alt [ 2467753, 2474125] -
-------
seqinfo: 455 sequences (1 circular) from hg38 genome
>
>
>
> g2 <- genes(TxDb.Hsapiens.UCSC.hg38.knownGene, single.strand.genes.only = TRUE)
> g2['5460']
Error: subscript contains invalid names
>
For now, we are discussing internally what we'll do. There are several options and in any case it's possible to compute the coverage matrix for genes like POU5F1
that are present in a canonical chromosome and several non-canonical ones; these are genes researches like Ron Stewart might be interested in.
Best,
Leo
couldn't get raw gene counts out of rse_gene
object, needed to load library(SummarizedExperiment)
Hi all,
Not sure if this is a problem of the project I decided to work with (SRP058740), or could be more general. First, let me state that according to the SRA website, I would expect around 30-60 million counts per sample. For instance, https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR2040575 has 73M spots). For this project, I downloaded (both directly from website, and using the recount bioC package, same result) the rse_tx, rse_gene and rse_exon Rdata files, and loaded them in R.
colSums(assays(rse_gene)$counts)["SRR2040575"]/1e6
SRR2040575
13788.46
colSums(assays(rse_exon)$counts)["SRR2040575"]/1e6
SRR2040575
13788.46
colSums(assays(rse_tx)$fragments, na.rm=TRUE)["SRR2040575"]/1e6
SRR2040575
63.74166
Only the last one makes any sense. Am I missing something obvious?
Output of sessionInfo() below.
Thanks,
Cei
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.4
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] recount_1.12.1 SummarizedExperiment_1.16.1 DelayedArray_0.12.2 BiocParallel_1.20.1 matrixStats_0.56.0
[6] Biobase_2.46.0 GenomicRanges_1.38.0 GenomeInfoDb_1.22.1 IRanges_2.20.2 S4Vectors_0.24.3
[11] BiocGenerics_0.32.0
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 qvalue_2.18.0 htmlTable_1.13.3 XVector_0.26.0 base64enc_0.1-3
[6] rstudioapi_0.11 bit64_0.9-7 AnnotationDbi_1.48.0 xml2_1.2.5 codetools_0.2-16
[11] splines_3.6.2 knitr_1.28 Formula_1.2-3 jsonlite_1.6.1 Rsamtools_2.2.3
[16] cluster_2.1.0 dbplyr_1.4.2 png_0.1-7 rentrez_1.2.2 readr_1.3.1
[21] compiler_3.6.2 httr_1.4.1 backports_1.1.5 assertthat_0.2.1 Matrix_1.2-18
[26] limma_3.42.2 acepack_1.4.1 htmltools_0.4.0 prettyunits_1.1.1 tools_3.6.2
[31] gtable_0.3.0 glue_1.3.2 GenomeInfoDbData_1.2.2 reshape2_1.4.3 dplyr_0.8.5
[36] rappdirs_0.3.1 doRNG_1.8.2 Rcpp_1.0.4 bumphunter_1.28.0 vctrs_0.2.4
[41] Biostrings_2.54.0 rtracklayer_1.46.0 iterators_1.0.12 xfun_0.12 stringr_1.4.0
[46] lifecycle_0.2.0 rngtools_1.5 XML_3.99-0.3 zlibbioc_1.32.0 scales_1.1.0
[51] BSgenome_1.54.0 VariantAnnotation_1.32.0 hms_0.5.3 GEOquery_2.54.1 derfinderHelper_1.20.0
[56] RColorBrewer_1.1-2 curl_4.3 memoise_1.1.0 gridExtra_2.3 ggplot2_3.3.0
[61] downloader_0.4 biomaRt_2.42.1 rpart_4.1-15 latticeExtra_0.6-29 stringi_1.4.6
[66] RSQLite_2.2.0 foreach_1.5.0 checkmate_2.0.0 GenomicFeatures_1.38.2 rlang_0.4.5
[71] pkgconfig_2.0.3 GenomicFiles_1.22.0 bitops_1.0-6 lattice_0.20-40 purrr_0.3.3
[76] GenomicAlignments_1.22.1 htmlwidgets_1.5.1 bit_1.1-15.2 tidyselect_1.0.0 plyr_1.8.6
[81] magrittr_1.5 R6_2.4.1 Hmisc_4.4-0 DBI_1.1.0 pillar_1.4.3
[86] foreign_0.8-76 survival_3.1-11 RCurl_1.98-1.1 nnet_7.3-13 tibble_2.1.3
[91] crayon_1.3.4 derfinder_1.20.0 BiocFileCache_1.10.2 jpeg_0.1-8.1 progress_1.2.2
[96] locfit_1.5-9.4 grid_3.6.2 data.table_1.12.8 blob_1.2.1 digest_0.6.25
[101] tidyr_1.0.2 openssl_1.4.1 munsell_0.5.0 askpass_1.1
Hi Leonardo, @lcolladotor
Please remove package installation code from recount
. Assume that the package is installed.
If said package is in the Suggests
fields and it is required for a function, check using requireNamespace
, then stop
if the package is not available, and ask the user to install it first. Do not install the package for the user.
Lines 14 to 32 in 8da982b
Hi,
@ShanEllis found a bug in the TCGA metadata. Basically, a column has a mixture of data. She also found that by re-running https://github.com/leekgroup/recount-website/blob/master/metadata/tcga_prep/tcga_clinical.R the problematic column gets fixed. It looks like cgc_case_age_at_diagnosis
has the data that is weird in age_at_initial_pathologic_diagnosis
.
For now, this will be a known issue while I update the TCGA files in https://github.com/leekgroup/recount-website.
Best,
Leo
library('recount')
library('devtools')
md <- all_metadata('TCGA')
table(md$xml_age_at_initial_pathologic_diagnosis)
md <- recount::all_metadata('TCGA')
weird <- which(md$xml_age_at_initial_pathologic_diagnosis %in% c('Trigone', 'Wall Anterior', 'Wall Lateral', 'Wall NOS', 'Wall Posterior'))
md[weird, colnames(md)[grep('age', colnames(md))]]
## Reproducibility information
print('Reproducibility information:')
Sys.time()
proc.time()
options(width = 120)
session_info()
> library('recount')
> library('devtools')
>
> md <- all_metadata('TCGA')
2017-02-24 13:28:09 downloading the metadata to /var/folders/cx/n9s558kx6fb7jf5z_pgszgb80000gn/T//RtmpSiDQCg/metadata_clean_tcga.Rdata
trying URL 'https://github.com/leekgroup/recount-website/blob/master/metadata/metadata_clean_tcga.Rdata?raw=true'
Content type 'application/octet-stream' length 16351229 bytes (15.6 MB)
==================================================
downloaded 15.6 MB
> table(md$xml_age_at_initial_pathologic_diagnosis)
0 14 15 16 17 18 19 20 21 22
25 1 4 2 4 8 11 17 14 11
23 24 25 26 27 28 29 30 31 32
22 29 22 28 25 33 40 50 46 46
33 34 35 36 37 38 39 40 41 42
53 72 67 62 66 89 81 102 100 107
43 44 45 46 47 48 49 50 51 52
124 99 147 138 159 176 158 165 235 184
53 54 55 56 57 58 59 60 61 62
219 227 226 240 261 275 274 302 292 296
63 64 65 66 67 68 69 70 71 72
290 265 276 273 258 270 262 247 231 208
73 74 75 76 77 78 79 80 81 82
230 229 202 163 161 140 144 114 95 81
83 84 85 86 87 88 89 90 Trigone Wall Anterior
64 74 51 28 35 24 10 49 1 6
Wall Lateral Wall NOS Wall Posterior
11 1 9
> md <- recount::all_metadata('TCGA')
2017-02-24 13:28:19 downloading the metadata to /var/folders/cx/n9s558kx6fb7jf5z_pgszgb80000gn/T//RtmpSiDQCg/metadata_clean_tcga.Rdata
trying URL 'https://github.com/leekgroup/recount-website/blob/master/metadata/metadata_clean_tcga.Rdata?raw=true'
Content type 'application/octet-stream' length 16351229 bytes (15.6 MB)
==================================================
downloaded 15.6 MB
> weird <- which(md$xml_age_at_initial_pathologic_diagnosis %in% c('Trigone', 'Wall Anterior', 'Wall Lateral', 'Wall NOS', 'Wall Posterior'))
> md[weird, colnames(md)[grep('age', colnames(md))]]
DataFrame with 28 rows and 39 columns
gdc_cases.diagnoses.tumor_stage gdc_cases.diagnoses.age_at_diagnosis cgc_case_age_at_diagnosis cgc_case_clinical_stage cgc_case_pathologic_stage
<character> <numeric> <integer> <character> <character>
1 stage ii 25672 70 NA Stage II
2 stage iv 23236 63 NA Stage IV
3 stage iv 26893 73 NA Stage IV
4 stage iv 26874 73 NA Stage IV
5 stage ii 28204 77 NA Stage II
... ... ... ... ... ...
24 stage iv 27963 76 NA Stage IV
25 stage iii 25185 68 NA Stage III
26 stage iii 28328 77 NA Stage III
27 stage iv 27816 76 NA Stage IV
28 stage iv 21196 58 NA Stage IV
xml_primary_pathology_age_at_initial_pathologic_diagnosis xml_age_at_initial_pathologic_diagnosis xml_stage_event_system_version
<integer> <character> <character>
1 NA Wall Lateral 7th
2 NA Wall Lateral 7th
3 NA Wall Lateral 7th
4 NA Wall Lateral 7th
5 NA Wall Lateral 7th
... ... ... ...
24 NA Wall Posterior 7th
25 NA Wall Lateral 7th
26 NA Wall Posterior 7th
27 NA Wall Anterior 7th
28 NA Wall Anterior 7th
xml_stage_event_clinical_stage xml_stage_event_pathologic_stage xml_stage_event_tnm_categories xml_stage_event_psa xml_stage_event_gleason_grading
<character> <character> <character> <character> <integer>
1 NA Stage II T2aN0MX NA NA
2 NA Stage IV T2T3N2MX NA NA
3 NA Stage IV T2T3aN2MX NA 6
4 NA Stage IV T2T4bN1MX NA 7
5 NA Stage II T2aN0MX NA NA
... ... ... ... ... ...
24 NA Stage IV T3bN2M0 NA 6
25 NA Stage III T2T3bN0MX NA NA
26 NA Stage III T1T3aN0MX NA 7
27 NA Stage IV T3bN3M1 NA 6
28 NA Stage IV T3bN2MX NA 6
xml_stage_event_ann_arbor xml_stage_event_serum_markers xml_stage_event_igcccg_stage xml_stage_event_masaoka_stage xml_asbestos_exposure_age
<character> <character> <character> <character> <integer>
1 NA NA NA NA NA
2 NA NA NA NA NA
3 NA NA NA NA NA
4 NA NA NA NA NA
5 NA NA NA NA NA
... ... ... ... ... ...
24 NA NA NA NA NA
25 NA NA NA NA NA
26 NA NA NA NA NA
27 NA NA NA NA NA
28 NA NA NA NA NA
xml_asbestos_exposure_age_last xml_birth_control_pill_history_usage_category xml_age_began_smoking_in_years xml_axillary_lymph_node_stage_method_type
<integer> <character> <integer> <character>
1 NA NA 12 NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA 18 NA
... ... ... ... ...
24 NA NA 15 NA
25 NA NA 25 NA
26 NA NA NA NA
27 NA NA NA NA
28 NA NA NA NA
xml_axillary_lymph_node_stage_other_method_descriptive_text xml_er_level_cell_percentage_category xml_history_of_esophageal_cancer
<character> <character> <character>
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
... ... ... ...
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
xml_primary_pathology_esophageal_tumor_cental_location xml_primary_pathology_esophageal_tumor_involvement_sites
<character> <character>
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
... ... ...
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
xml_primary_pathology_tumor_infiltrating_macrophages xml_cumulative_agent_total_dose xml_hydroxyurea_agent_administered_day_count
<character> <integer> <integer>
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
... ... ... ...
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 NA NA NA
28 NA NA NA
xml_person_history_nonmedical_leukemia_causing_agent_type xml_lab_procedure_blast_cell_outcome_percentage_value
<character> <integer>
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
... ... ...
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
xml_prior_tamoxifen_administered_usage_category xml_radiosensitizing_agent_administered_indicator
<character> <character>
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
... ... ...
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
xml_person_concomitant_prostate_carcinoma_pathologic_t_stage xml_first_diagnosis_age_asth_ecz_hay_fev_mold_dust xml_first_diagnosis_age_of_food_allergy
<character> <character> <character>
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
... ... ... ...
24 NA NA NA
25 NA NA NA
26 NA NA NA
27 7thStage IVT3bN3M16 NA NA
28 7thStage IVT3bN2MX6 NA NA
xml_first_diagnosis_age_of_animal_insect_allergy xml_undescended_testis_corrected_age
<character> <character>
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
... ... ...
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
>
> ## Reproducibility information
> print('Reproducibility information:')
[1] "Reproducibility information:"
> Sys.time()
[1] "2017-02-24 13:28:27 EST"
> proc.time()
user system elapsed
20.786 1.699 28.800
> options(width = 120)
> session_info()
Session info -----------------------------------------------------------------------------------------------------------
setting value
version R Under development (unstable) (2016-10-26 r71594)
system x86_64, darwin13.4.0
ui AQUA
language (EN)
collate en_US.UTF-8
tz America/New_York
date 2017-02-24
Packages ---------------------------------------------------------------------------------------------------------------
package * version date source
acepack 1.4.1 2016-10-29 CRAN (R 3.4.0)
AnnotationDbi 1.37.3 2017-02-09 Bioconductor
assertthat 0.1 2013-12-06 CRAN (R 3.4.0)
backports 1.0.5 2017-01-18 CRAN (R 3.4.0)
base64enc 0.1-3 2015-07-28 CRAN (R 3.4.0)
Biobase * 2.35.1 2017-02-23 Bioconductor
BiocGenerics * 0.21.3 2017-01-12 Bioconductor
BiocParallel 1.9.5 2017-01-24 Bioconductor
biomaRt 2.31.4 2017-01-13 Bioconductor
Biostrings 2.43.4 2017-02-02 Bioconductor
bitops 1.0-6 2013-08-17 CRAN (R 3.4.0)
BSgenome 1.43.5 2017-02-02 Bioconductor
bumphunter 1.15.0 2016-10-23 Bioconductor
checkmate 1.8.2 2016-11-02 CRAN (R 3.4.0)
cluster 2.0.5 2016-10-08 CRAN (R 3.4.0)
codetools 0.2-15 2016-10-05 CRAN (R 3.4.0)
colorspace 1.3-2 2016-12-14 CRAN (R 3.4.0)
data.table 1.10.4 2017-02-01 CRAN (R 3.4.0)
DBI 0.5-1 2016-09-10 CRAN (R 3.4.0)
DelayedArray * 0.1.7 2017-02-17 Bioconductor
derfinder 1.9.6 2017-01-13 Bioconductor
derfinderHelper 1.9.3 2016-11-29 Bioconductor
devtools * 1.12.0 2016-12-05 CRAN (R 3.4.0)
digest 0.6.12 2017-01-27 CRAN (R 3.4.0)
doRNG 1.6 2014-03-07 CRAN (R 3.4.0)
downloader 0.4 2015-07-09 CRAN (R 3.4.0)
foreach 1.4.3 2015-10-13 CRAN (R 3.4.0)
foreign 0.8-67 2016-09-13 CRAN (R 3.4.0)
Formula 1.2-1 2015-04-07 CRAN (R 3.4.0)
GenomeInfoDb * 1.11.9 2017-02-08 Bioconductor
GenomeInfoDbData 0.99.0 2017-02-14 Bioconductor
GenomicAlignments 1.11.9 2017-02-02 Bioconductor
GenomicFeatures 1.27.8 2017-02-11 Bioconductor
GenomicFiles 1.11.3 2016-11-29 Bioconductor
GenomicRanges * 1.27.22 2017-02-02 Bioconductor
GEOquery 2.41.0 2016-10-25 Bioconductor
ggplot2 2.2.1 2016-12-30 CRAN (R 3.4.0)
gridExtra 2.2.1 2016-02-29 CRAN (R 3.4.0)
gtable 0.2.0 2016-02-26 CRAN (R 3.4.0)
Hmisc 4.0-2 2016-12-31 CRAN (R 3.4.0)
htmlTable 1.9 2017-01-26 CRAN (R 3.4.0)
htmltools 0.3.5 2016-03-21 CRAN (R 3.4.0)
htmlwidgets 0.8 2016-11-09 CRAN (R 3.4.0)
httr 1.2.1 2016-07-03 CRAN (R 3.4.0)
IRanges * 2.9.18 2017-02-02 Bioconductor
iterators 1.0.8 2015-10-13 CRAN (R 3.4.0)
jsonlite 1.2 2016-12-31 CRAN (R 3.4.0)
knitr 1.15.1 2016-11-22 CRAN (R 3.4.0)
lattice 0.20-34 2016-09-06 CRAN (R 3.4.0)
latticeExtra 0.6-28 2016-02-09 CRAN (R 3.4.0)
lazyeval 0.2.0 2016-06-12 CRAN (R 3.4.0)
locfit 1.5-9.1 2013-04-20 CRAN (R 3.4.0)
magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
Matrix 1.2-8 2017-01-20 CRAN (R 3.4.0)
matrixStats * 0.51.0 2016-10-09 CRAN (R 3.4.0)
memoise 1.0.0 2016-01-29 CRAN (R 3.4.0)
munsell 0.4.3 2016-02-13 CRAN (R 3.4.0)
nnet 7.3-12 2016-02-02 CRAN (R 3.4.0)
pkgmaker 0.22 2014-05-14 CRAN (R 3.4.0)
plyr 1.8.4 2016-06-08 CRAN (R 3.4.0)
qvalue 2.7.0 2016-10-23 Bioconductor
R6 2.2.0 2016-10-05 CRAN (R 3.4.0)
RColorBrewer 1.1-2 2014-12-07 CRAN (R 3.4.0)
Rcpp 0.12.9 2017-01-14 CRAN (R 3.4.0)
RCurl 1.95-4.8 2016-03-01 CRAN (R 3.4.0)
recount * 1.1.18 2017-02-22 Github (leekgroup/recount@ced5db4)
registry 0.3 2015-07-08 CRAN (R 3.4.0)
rentrez 1.0.4 2016-10-26 CRAN (R 3.4.0)
reshape2 1.4.2 2016-10-22 CRAN (R 3.4.0)
rngtools 1.2.4 2014-03-06 CRAN (R 3.4.0)
rpart 4.1-10 2015-06-29 CRAN (R 3.4.0)
Rsamtools 1.27.12 2017-01-24 Bioconductor
RSQLite 1.1-2 2017-01-08 CRAN (R 3.4.0)
rtracklayer 1.35.6 2017-02-19 cran (@1.35.6)
S4Vectors * 0.13.15 2017-02-14 cran (@0.13.15)
scales 0.4.1 2016-11-09 CRAN (R 3.4.0)
stringi 1.1.2 2016-10-01 CRAN (R 3.4.0)
stringr 1.2.0 2017-02-18 CRAN (R 3.4.0)
SummarizedExperiment * 1.5.7 2017-02-23 Bioconductor
survival 2.40-1 2016-10-30 CRAN (R 3.4.0)
tibble 1.2 2016-08-26 CRAN (R 3.4.0)
VariantAnnotation 1.21.17 2017-02-12 Bioconductor
withr 1.0.2 2016-06-20 CRAN (R 3.4.0)
XML 3.98-1.5 2016-11-10 CRAN (R 3.4.0)
xtable 1.8-2 2016-02-05 CRAN (R 3.4.0)
XVector 0.15.2 2017-02-02 Bioconductor
zlibbioc 1.21.0 2016-10-23 Bioconductor
See original at #4.
@amadeusX posted this message:
Hi,
Congratulations to the wonderful recount package and the huge dataset you compiled!
We would like to use the normalized (or, with a lot more effort, we can normalize) gene expression compendium. Say, the rows are the genes and columns are experiments . Hence the (i,j) element of the matrix is the transcript level of gene i in experiment j. We would need that for the identification of generally co-expressed pairs of genes, and for the negative set, independently expressed gene pairs.
Thank you so much and Happy Holidays,
Steve
Istvan Ladunga,
University of Nebraska-Lincoln
Hi, I noticed that the names of the columns in the row data table for a RangedSummarizedExperiment object seem to be inconsistent with the data in the columns of the table, unless I am misunderstanding something (I am an R novice).
I download a RangedSummarizedExperiment as follows:
url <- download_study('SRP009615')
load(file.path('SRP009615', 'rse_gene.Rdata'))
rowData(rse_gene)
I get a DataFrame with 21 columns. The order of the names of the columns does not seem to coincide with the data in that column. For example, the first column name is "project"; however, the first column seems to contain the run accession. Is this a bug? Or is there another way I am supposed to find the name of each column?
Thanks!
Hi there,
I was following the vignette and found that TCGA samples do not have any data points. Please help.
The code is below:
project_info <- abstract_search("TCGA")
download_study(project_info$project[7])
load(file.path(project_info$project[7], "rse_gene.Rdata"))
#unlink(project_info$project[7], recursive = TRUE)
dim(colData(rse_gene))##11284 864
rse_gene <- add_predictions(rse_gene)
dim(colData(rse_gene))#11284 876
x<-colData(rse_gene)[, 865:ncol(colData(rse_gene))]
x[1:50,1:5]
session_info()
─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.0.3 (2020-10-10)
os Red Hat Enterprise Linux Server 7.5 (Maipo)
system x86_64, linux-gnu
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Chicago
date 2021-07-06
─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────
! package * version date lib source
annotate 1.68.0 2020-10-27 [1] Bioconductor
AnnotationDbi * 1.52.0 2020-10-27 [1] Bioconductor
AnnotationFilter 1.14.0 2020-10-27 [1] Bioconductor
AnnotationHub * 2.22.1 2021-04-16 [1] Bioconductor
askpass 1.1 2019-01-13 [1] CRAN (R 4.0.3)
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Have you considered integrating with BiocFileCache to locally cache downloads, e.g., from recount::download_study()
? I just found myself re-downloading the same study (in the same directory) for the n+1 time (my fault for not running code before thinking).
This would require a bit of work but might save on downloads (and perhaps make for a good student coding project?).
Hi, is the abstract searching the canonical way to load experiment by GSE ID? If yes, there is a better way: you could download GEO metadata file: https://ftp.ncbi.nlm.nih.gov/sra/reports/Metadata/SRA_Accessions.tab and look up GSE to SRP mapping there. I think it would be a super useful feature.
Hi,
Currently in BioC release (3.16) and devel (3.17), recount
is failing. That's because neither the new IDIES location nor AWS are allowing us to read the BigWig files from the web. I manually edited a local clone of recount
to try with the IDIES location.
You can test this on AWS (through duffel) with:
regions <- expressed_regions("SRP002001", "chrY", cutoff = 5)
from
recount/tests/testthat/test-data.R
Line 112 in 7301c5f
This is the type of warning we get:
2023-02-20 12:51:10 loadCoverage: loading BigWig file http://sciserver.org/public-data/recount2/data/SRP002001/bw/mean_SRP002001.bw
In addition: Warning messages:
1: In seqinfo(con) :
No openssl available in netConnectHttps for sciserver.org : 443
2: In seqinfo(con) :
No openssl available in netConnectHttps for sciserver.org : 443
3: In seqinfo(con) :
No openssl available in netConnectHttps for sciserver.org : 443
> traceback()
8: stop(conditionMessage(output))
7: FUN(X[[i]], ...)
6: lapply(as.list(X), match.fun(FUN), ...)
5: lapply(as.list(X), match.fun(FUN), ...)
4: lapply(bList, .loadCoverageBigWig, range = which, chr = chr,
verbose = verbose)
3: lapply(bList, .loadCoverageBigWig, range = which, chr = chr,
verbose = verbose)
2: derfinder::loadCoverage(files = meanFile, chr = chr, chrlen = chrlen) at expressed_regions.R#121
1: expressed_regions("SRP002001", "chrY", cutoff = 5)
2023-02-20 12:36:04 loadCoverage: loading BigWig file http://duffel.rail.bio/recount/SRP002001/bw/mean_SRP002001.bw
In addition: Warning messages:
1: In seqinfo(con) :
No openssl available in netConnectHttps for recount-opendata.s3.amazonaws.com : 443
2: In seqinfo(con) :
No openssl available in netConnectHttps for recount-opendata.s3.amazonaws.com : 443
3: In seqinfo(con) :
No openssl available in netConnectHttps for recount-opendata.s3.amazonaws.com : 443
I'm not sure what to do @nellore @ChristopherWilks.
I can try to provide a smaller test, digging into .loadCoverageBigWig()
https://github.com/lcolladotor/derfinder/blob/5c1cbd412c5787bf2d2d778977e38dd6ae64976d/R/loadCoverage.R#L384 and well, ultimately rtracklayer
.
Best,
Leo
At BioC2019 @sonali-bioc requested having a function for loading multiple RSE files.
This is just a reminder to myself for actually doing this :-)
Dear recount team,
First of all, congratulations this great resource, which I am regularly using for training and research.
I have a problem with several studies (e.g. SRP042620), where the structure of the pheno table extracted from RData files downloaded from recount differs between transcript and gene datasets.
For the gene dataset, the characteristics field is a CompressedCharacteristics (as expected)
> phenoTable <- colData(rse_gene) ## phenotype per run
> class(phenoTable$characteristics)
[1] "CompressedCharacterList"
attr(,"package")
[1] "IRanges"
For the transcript dataset, it is a vector
> phenoTable <- colData(rse_tx) ## phenotype per run
> class(phenoTable$characteristics)
[1] "character"
Is there a way to fix this ?
Many thanks,
Jacques van Helden
Hi there,
I have read the instructions for downloading the recount R package, and it says first to upgrade R to version 3.3.0. At the CRAN website, I recently upgraded my version of R to 3.3.1; however, it seems that biocLite is unable to download recount under this newer version. Am I doing something wrong?
Here is the error:
biocLite('recount')
BioC_mirror: https://bioconductor.org
Using Bioconductor 3.3 (BiocInstaller 1.22.3), R 3.3.1 (2016-06-21).
Installing package(s) ‘recount’
Warning message:
package ‘recount’ is not available (for R version 3.3.1)
Thanks for your help in advance.
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