Results for TCGA samples having all "NA" values
boseb opened this issue · 1 comments
Hi there,
I was following the vignette and found that TCGA samples do not have any data points. Please help.
The code is below:
Find a project of interest
project_info <- abstract_search("TCGA")
Download the gene-level RangedSummarizedExperiment data
download_study(project_info$project[7])
Load the data
load(file.path(project_info$project[7], "rse_gene.Rdata"))
Delete it if you don't need it anymore
#unlink(project_info$project[7], recursive = TRUE)
Before adding predictions
dim(colData(rse_gene))##11284 864
Add the predictions
rse_gene <- add_predictions(rse_gene)
After adding the predictions
dim(colData(rse_gene))#11284 876
Explore the variables
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 ───────────────────────────────────────────────────────────────────────────────────────────────────
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Hi,
Thanks for the report. I can see what you are talking about. However, there's a small detail you missed at rse_gene <- add_predictions(rse_gene). It should have been rse_gene <- add_predictions(rse_gene, is_tcga = TRUE) as you can see below:
> rse_gene_extra <- add_predictions(rse_gene, is_tcga = TRUE)
2021-08-02 23:01:18 downloading the predictions to /var/folders/cx/n9s558kx6fb7jf5z_pgszgb80000gn/T//RtmpGOfOsK/PredictedPhenotypes_v0.0.06.rda
trying URL 'https://github.com/leekgroup/recount-website/blob/master/predictions/PredictedPhenotypes_v0.0.06.rda?raw=true'
Content type 'application/octet-stream' length 548129 bytes (535 KB)
==================================================
downloaded 535 KB
Loading objects:
PredictedPhenotypes
> dim(colData(rse_gene_extra))
[1] 11284 876
> x2 <- colData(rse_gene_extra)[, 865:ncol(colData(rse_gene_extra))]
> head(x2[, 1:5])
DataFrame with 6 rows and 5 columns
reported_sex predicted_sex accuracy_sex
<factor> <factor> <numeric>
3DFF72D2-F292-497E-ACE3-6FAA9C884205 male male 0.989506
B1E54366-42B9-463C-8615-B34D52BD14DC male male 0.989506
473713F7-EB41-4F20-A37F-ACD209E3CB75 male male 0.989506
11F18F54-9B33-4C33-BDF9-0F093F4F3336 male male 0.989506
136B7576-1108-4FA3-8254-6069F0CA879A male male 0.989506
E81FA8B7-3FFE-4F73-94AF-0B5257D7F81A male male 0.989506
reported_samplesource
<factor>
3DFF72D2-F292-497E-ACE3-6FAA9C884205 tissue
B1E54366-42B9-463C-8615-B34D52BD14DC tissue
473713F7-EB41-4F20-A37F-ACD209E3CB75 tissue
11F18F54-9B33-4C33-BDF9-0F093F4F3336 tissue
136B7576-1108-4FA3-8254-6069F0CA879A tissue
E81FA8B7-3FFE-4F73-94AF-0B5257D7F81A tissue
predicted_samplesource
<factor>
3DFF72D2-F292-497E-ACE3-6FAA9C884205 tissue
B1E54366-42B9-463C-8615-B34D52BD14DC tissue
473713F7-EB41-4F20-A37F-ACD209E3CB75 tissue
11F18F54-9B33-4C33-BDF9-0F093F4F3336 tissue
136B7576-1108-4FA3-8254-6069F0CA879A cell_line
E81FA8B7-3FFE-4F73-94AF-0B5257D7F81A tissue
> packageVersion("recount")
[1] ‘1.18.0’Best,
Leo