Closed iditkosti closed 2 years ago
ChristopherEeles
commented
3 years ago Hi @iditkosti,
Can you please provide some more context. What code are you executing to load the CMAP data? Where was this data retrieved from?
Also include the results of running sessionInfo() in your current R session so I can see the version of the package being used.
Best, Christopher Eeles Software Developer BHKLab | PM-Research | UHN
iditkosti
commented
3 years ago Hi Christopher, The package description mentions CMAP as a source of knowledge, and the examples have a CMAPsmall example, but when you look at the possible perturbation database to download, it’s not there.
Thanks, Idit
From: Christopher Eeles @.> Date: Tuesday, October 26, 2021 at 2:16 PM To: bhklab/PharmacoGx @.> Cc: Idit Kosti @.>, Mention @.> Subject: Re: [bhklab/PharmacoGx] Unable to load CMap data (Issue #99)
Can you please provide some more context. What code are you executing to load the CMAP data? Where was this data retrieved from?
Also include the results of running sessionInfo() in your current R session so I can see the version of the package being used.
Best, Christopher Eeles Software Developer BHKLab | PM-Research | UHN
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ChristopherEeles
commented
3 years ago Hi @iditkosti,
I understand your question, but it is a standard courtesy to include a minimally reproducible code example demonstrating what is going wrong as well as your R sessionInfo() for context on your R development environment.
I am happy to provide assistance, but you need to be more specific. It is not my responsibility as a developer to guess what code your are trying to run, what version of R or the PharmacoGx package you are using, etc.
To answer your question please provide:
(1) The code you are running to check CMAP availability, what it returns and what you expected it to do.
(2) The full text returned by R sessionInfo().
Best, Christopher Eeles Software Developer BHKLab | PM-Research | UHN
iditkosti
commented
3 years ago Good morning,
I am attaching both requested files. In the vignette on Bioconductor (https://bioconductor.org/packages/release/bioc/vignettes/PharmacoGx/inst/doc/PharmacoGx.pdf) it clearly says CMAP is a possible source of data. It does not appear in the list of possible perturbation datasets, by PharmacoGx::availablePSets().
I came across the package while browsing the CMap space, and have no plan to use this package as I plan to use CMap directly. I am reporting this out of courtesy alone so other users can benefit from it and did my best to follow what you asked.
All the best, Idit
From: Christopher Eeles @.> Date: Wednesday, October 27, 2021 at 8:14 AM To: bhklab/PharmacoGx @.> Cc: Idit Kosti @.>, Mention @.> Subject: Re: [bhklab/PharmacoGx] Unable to load CMap data (Issue #99)
I understand your question, but it is a standard courtesy to include a minimally reproducible code example demonstrating what is going wrong as well as your R sessionInfo() for context on your R development environment.
I am happy to provide assistance, but you need to be more specific. It is not my responsibility as a developer to guess what code your are trying to run, what version of the R or the PharmacoGx package you are using, etc.
To answer your question please provide: (1) The code you are running to check CMAP availability, what it returns are what you expected it to do. (2) The full text returned by R sessionInfo().
Best, Christopher Eeles Software Developer BHKLab | PM-Research | UHN
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sessionInfo() R version 4.0.4 (2021-02-15) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 18.04.3 LTS
Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages: [1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] PharmacoGx_2.2.4 CoreGx_1.2.0 readxl_1.3.1 DESeq2_1.30.1 SummarizedExperiment_1.20.0
[6] MatrixGenerics_1.2.1 matrixStats_0.60.0 GenomicRanges_1.42.0 GenomeInfoDb_1.26.4 tidyr_1.1.3
[11] reshape2_1.4.4 longevityTools_1.0.6 ggplot2_3.3.3 uatools_0.1.0.0.0.0.1610386026 org.Mm.eg.db_3.12.0
[16] eply_0.1.2 org.Hs.eg.db_3.12.0 AnnotationDbi_1.52.0 IRanges_2.24.1 S4Vectors_0.28.1
[21] Biobase_2.50.0 BiocGenerics_0.36.0 clusterProfiler_3.18.1 cmapR_1.5.1
loaded via a namespace (and not attached):
[1] utf8_1.2.2 shinydashboard_0.7.2 tidyselect_1.1.0 RSQLite_2.2.8 htmlwidgets_1.5.4 grid_4.0.4
[7] BiocParallel_1.24.1 scatterpie_0.1.7 flowCore_2.2.0 munsell_0.5.0 DT_0.19 withr_2.4.1
[13] colorspace_2.0-2 GOSemSim_2.16.1 filelock_1.0.2 knitr_1.31 rstudioapi_0.13 DOSE_3.16.0
[19] labeling_0.4.2 slam_0.1-48 GenomeInfoDbData_1.2.4 polyclip_1.10-0 bit64_4.0.5 farver_2.1.0
[25] rhdf5_2.34.0 rprojroot_2.0.2 downloader_0.4 vctrs_0.3.8 generics_0.1.0 xfun_0.20
[31] sets_1.0-19 lsa_0.73.2 R6_2.5.0 graphlayouts_0.7.1 celestial_1.4.6 locfit_1.5-9.4
[37] bitops_1.0-6 rhdf5filters_1.2.0 cachem_1.0.5 fgsea_1.16.0 DelayedArray_0.16.3 promises_1.2.0.1
[43] scales_1.1.1 ggraph_2.0.5 enrichplot_1.10.2 gtable_0.3.0 processx_3.5.2 RProtoBufLib_2.2.0
[49] tidygraph_1.2.0 rlang_0.4.10 genefilter_1.72.1 splines_4.0.4 BiocManager_1.30.10 yaml_2.2.1
[55] backports_1.2.1 httpuv_1.6.1 qvalue_2.22.0 tools_4.0.4 relations_0.6-9 ellipsis_0.3.2
[61] gplots_3.1.1 RColorBrewer_1.1-2 Rcpp_1.0.7 plyr_1.8.6 visNetwork_2.1.0 zlibbioc_1.36.0
[67] purrr_0.3.4 RCurl_1.98-1.4 ps_1.5.0 prettyunits_1.1.1 viridis_0.6.1 cowplot_1.1.1
[73] ggrepel_0.9.1 cluster_2.1.0 magrittr_2.0.1 data.table_1.14.0 DO.db_2.9 RANN_2.6.1
[79] SnowballC_0.7.0 shinyjs_2.0.0 mime_0.11 evaluate_0.14 xtable_1.8-4 XML_3.99-0.8
[85] gridExtra_2.3 compiler_4.0.4 tibble_3.1.3 maps_3.4.0 KernSmooth_2.23-18 crayon_1.4.1
[91] shadowtext_0.0.8 htmltools_0.5.1.1 ggfun_0.0.3 later_1.2.0 geneplotter_1.68.0 RcppParallel_5.1.4
[97] DBI_1.1.1 tweenr_1.0.2 MASS_7.3-53 rappdirs_0.3.3 Matrix_1.3-2 cli_2.5.0
[103] marray_1.68.0 igraph_1.2.6 pkgconfig_2.0.3 sm_2.2-5.7 rvcheck_0.1.8 magicaxis_2.2.1
[109] piano_2.6.0 annotate_1.68.0 pins_0.4.5 XVector_0.30.0 stringr_1.4.0 callr_3.7.0
[115] digest_0.6.27 pracma_2.3.3 rmarkdown_2.10 cellranger_1.1.0 fastmatch_1.1-3 curl_4.3
[121] shiny_1.6.0 gtools_3.8.2 lifecycle_1.0.0 jsonlite_1.7.2 Rhdf5lib_1.12.1 mapproj_1.2.7
[127] viridisLite_0.4.0 limma_3.46.0 fansi_0.4.2 pillar_1.6.2 lattice_0.20-41 fastmap_1.1.0
[133] httr_1.4.2 plotrix_3.8-2 pkgbuild_1.2.0 survival_3.2-7 GO.db_3.12.1 glue_1.4.2
[139] remotes_2.4.0 bit_4.0.4 NISTunits_1.0.1 ggforce_0.3.3 stringi_1.7.3 blob_1.2.2
[145] caTools_1.18.1 memoise_2.0.0 renv_0.13.0 dplyr_1.0.4 cytolib_2.2.1
library(PharmacoGx) PharmacoGx::availablePSets() Dataset Name Date Created PSet Name version type 1 CCLE 2020-06-24T14:39:26.588Z CCLE_2015 2015 sensitivity 2 CTRPv2 2020-06-24T14:39:26.588Z CTRPv2_2015 2015 sensitivity 3 FIMM 2020-06-24T14:39:26.588Z FIMM_2016 2016 sensitivity 4 gCSI 2021-06-11T21:58:16.390Z gCSI_2019 2019
5 GDSC 2020-06-24T14:39:26.588Z GDSC_2020(v2-8.2) 2020(v2-8.2) sensitivity 6 GDSC 2020-06-24T14:39:26.588Z GDSC_2020(v1-8.2) 2020(v1-8.2) sensitivity 7 GRAY 2021-02-23T14:39:26.588Z GRAY_2017 2017 sensitivity 8 NCI60 2021-08-18T16:28:45.207Z NCI60_2021 2021 sensitivity 9 PRISM 2021-08-18T16:28:45.207Z PRISM_2020 2020 sensitivity 10 UHNBreast 2020-06-24T14:39:26.588Z UHNBreast_2019 2019 both publication 1 Barretina, Jordi et al. “The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.” Nature vol. 483,7391 603-7. 28 Mar. 2012, doi:10.1038/nature11003, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3320027/ 2 Rees, Matthew G et al. “Correlating chemical sensitivity and basal gene expression reveals mechanism of action.” Nature chemical biology vol. 12,2 (2016): 109-16. doi:10.1038/nchembio.1986, Seashore-Ludlow, Brinton et al. “Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset.” Cancer discovery vol. 5,11 (2015): 1210-23. doi:10.1158/2159-8290.CD-15-0235, Basu, Amrita et al. “An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.” Cell vol. 154,5 (2013): 1151-1161. doi:10.1016/j.cell.2013.08.003, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4718762/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4631646/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954635/ 3 Mpindi, J., Yadav, B., Östling, P. et al. Consistency in drug response profiling. Nature 540, E5–E6 (2016) doi:10.1038/nature20171, https://www.nature.com/articles/nature20171 4 Petr Smirnov, Ian Smith, Zhaleh Safikhani, Wail Ba-alawi, Farnoosh Khodakarami, Eva Lin, Yihong Yu, Scott Martin, Janosch Ortmann, Tero Aittokallio, Marc Hafner, Benjamin Haibe-Kains. Evaluation of statistical approaches for association testing in noisy drug screening data. arXiv (2021), https://arxiv.org/abs/2104.14036 5 Yang, Wanjuan et al. “Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.” Nucleic acids research vol. 41,Database issue (2013): D955-61. doi:10.1093/nar/gks1111, Iorio F, Knijnenburg TA, Vis DJ, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166(3):740‐754. doi:10.1016/j.cell.2016.06.017, Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570‐575. Published 2012 Mar 28. doi:10.1038/nature11005, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531057/, https://pubmed.ncbi.nlm.nih.gov/27397505/, https://pubmed.ncbi.nlm.nih.gov/22460902/ 6 Yang, Wanjuan et al. “Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.” Nucleic acids research vol. 41,Database issue (2013): D955-61. doi:10.1093/nar/gks1111, Iorio F, Knijnenburg TA, Vis DJ, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166(3):740‐754. doi:10.1016/j.cell.2016.06.017, Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570‐575. Published 2012 Mar 28. doi:10.1038/nature11005, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531057/, https://pubmed.ncbi.nlm.nih.gov/27397505/, https://pubmed.ncbi.nlm.nih.gov/22460902/ 7 Hafner, Marc et al. “Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics.” Scientific data vol. 4 170166. 7 Nov. 2017, doi:10.1038/sdata.2017.166, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5674849/ 8 Shoemaker, R. The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6, 813–823 (2006). https://doi.org/10.1038/nrc1951, https://www.nature.com/articles/nrc1951 9 Corsello SM, Nagari RT, Spangler RD, et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat Cancer. 2020;1(2):235-248. doi:10.1038/s43018-019-0018-6, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328899/ 10 Mammoliti, Anthony et al. “Creating reproducible pharmacogenomic analysis pipelines.” Scientific data vol. 6,1 166. 3 Sep. 2019, doi:10.1038/s41597-019-0174-7, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722117/ rawSeqDataRNA DOI Download 1 https://portal.gdc.cancer.gov/legacy-archive/search/f 10.5281/zenodo.3905462 https://zenodo.org/record/3905462/files/CCLE.rds?download=1 2 10.5281/zenodo.3905470 https://zenodo.org/record/3905470/files/CTRPv2.rds?download=1 3 https://portal.gdc.cancer.gov/legacy-archive/search/f 10.5281/zenodo.3905448 https://zenodo.org/record/3905448/files/FIMM.rds?download=1 4 https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-2706/ 10.5281/zenodo.4737437 https://zenodo.org/record/4737437/files/gCSI2.rds?download=1 5 https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3983/ 10.5281/zenodo.3905481 https://zenodo.org/record/3905481/files/GDSC2.rds?download=1 6 https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3983/ 10.5281/zenodo.3905485 https://zenodo.org/record/3905485/files/GDSC1.rds?download=1 7 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48213 10.5281/zenodo.4557735 https://zenodo.org/record/4557735/files/GRAY2017.rds?download=1 8 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32474 10.5281/zenodo.5570629 https://zenodo.org/record/5570629/files/NCI60.rds?download=1 9 10.5281/zenodo.5570757 https://zenodo.org/record/5570757/files/PRISM.rds?download=1 10 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73526 10.5281/zenodo.3905460 https://zenodo.org/record/3905460/files/UHNBreast.rds?download=1
ChristopherEeles
commented
3 years ago Hi @iditkosti,
Thank you for opening the issue, it is appreciated. It seems like our vignette may have some outdated information. I will look into it today and post a solution here when I find it.
Best, Christopher Eeles Software Developer BHKLab | PM-Research | UHN
ChristopherEeles
commented
3 years ago Hi @iditkosti,
We added a parameter to availablePSets() called canonical which defaults to TRUE. This filters to only show PharmacoSets we have deemed to be sufficiently well curated and reproducible to meet the highest data quality standards.
CMAP is not a canonical PSet, thus it does now show by default. To view it, use;
availablePSets(canonical=FALSE)In downloadPSet all PSets are available, you just need to specify the 'PSet Name' value from availablePSets. In this case to download CMAP, you must use:
CMAP <- downloadPSet("CMAP_2016")I will review our PSet creation documentation and try to make this more clear. We haven't modified that vignette in a few versions.
Sorry for any confusion.
Best, Christopher Eeles Software Developer BHK Lab | PM-Research | UHN
ChristopherEeles
commented
2 years ago Hi @iditkosti,
I am closing this issue since I believe we answer your question. If this is not the case please feel free to re-open it.
Best, Christopher Eeles Software Developer BHK Lab | PM-Research | UHN
Unable to load the full CMap data (just the CMAPsmall example)