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GDC API query to map HT-seq FPKM UUID to a Case UUID
2
2
Entering edit mode
3.7 years ago
mk ▴ 210

I have a bunch of FPKM normalized mRNA sequencing from TCGA-PAAD, which covers pancreatic cancer patients. I wrote a script which goes through that data and makes a big data frame with all 60k+ Ensemble mappings for all 180+ patients, so that I can analyze various hypotheses regarding the clinical data.

I have the clinical metadata in a separate table, which I obtained using the TCGABiolinks package.

The problem is that all the columns in the sequencing data table are "HT-seq FPKM UUIDs".

Now all I need to do is map the HT-seq FPKM UUID to a Case UUID (patient)

I need an API query for this, or a call to some TCGABiolinks function.

Thanks in advance for your help!

Ok I have followed the advice here with some modifications. I obtained a json encoded case and file manifest which take the form given below.

## File manifest (first few records)

[{
"file_name": "232f085b-6201-4e4d-8473-e592b8d8e16d.FPKM.txt.gz",
"data_format": "TXT",
"access": "open",
"data_category": "Transcriptome Profiling",
"file_size": 514027,
"cases": [
{
"project": {
"project_id": "TCGA-PAAD"
},
"case_id": "620e0648-ec20-4a12-a6cb-5546fe829c77"
}
],
"annotations": [
{
"annotation_id": "050203a0-12ab-5025-973d-e070d94f722b"
}
]
},{
"file_name": "b0159d01-f1eb-490d-875b-cfdabed6f529.FPKM.txt.gz",
"data_format": "TXT",
"access": "open",
"data_category": "Transcriptome Profiling",
"file_size": 515800,
"cases": [
{
"project": {
"project_id": "TCGA-PAAD"
},
"case_id": "16b38977-aea1-4c75-89ec-4fb551f652dd"
}
]
},{
"file_name": "f2389819-b8fc-460e-821c-01dba313cce1.FPKM.txt.gz",
"data_format": "TXT",
"access": "open",
"data_category": "Transcriptome Profiling",
"file_size": 510184,
"cases": [
{
"project": {
"project_id": "TCGA-PAAD"
},
"case_id": "23908554-b98e-4ff8-98e7-dee3e2c5feaf"
}
]
},{

## Cases manifest (first few records)

[{
"diagnoses": [
{
"days_to_death": null
}
],
"case_id": "33833131-1482-42d5-9cf5-01cade540234",
"submitter_id": "TCGA-2J-AAB4"
},{
"diagnoses": [
{
"days_to_death": 738.0
}
],
"case_id": "67e9abc1-4b6f-4054-bdc4-29906c55c682",
"submitter_id": "TCGA-3A-A9IC"
},{
"diagnoses": [
{
"days_to_death": null
}
],
"case_id": "a53c919a-4e08-46f1-af3f-30b16b597c33",
"submitter_id": "TCGA-IB-AAUU"
},{
"diagnoses": [
{
"days_to_death": 278.0
}
],
"case_id": "ab449860-46e5-485e-abd5-31c5abef2c58",
"submitter_id": "TCGA-L1-A7W4"
},{

## Here is the code that was run:

rm(list = ls())
output_logfile <- file("log_output.txt", open="wt")
message_logfile <- file("log_message.txt", open="wt")
sink(file=output_logfile, type = "output")
sink(file=message_logfile, type = "message")
library(GSAR)
library(org.Hs.eg.db)
library(GSVAdata)
library(GSEABase)
library(GSVA)
library(dplyr)
data(c2BroadSets)
#### The following should work with data from the GDC, where we have a shopping cart
#### of zipped single patient sequencing data
#### Example workflow for a GDC-hosted study (TCGA-PAAD):
####    Go to the GDC homepage of the study: https://portal.gdc.cancer.gov/projects/TCGA-PAAD
####    Click on "Cases"
####    download the json manifest for cases
####    move it to the project directory
####    Click on "Files"
####    download the json manifest for files
####    move it to the project directory
####    download the actual data by selecting appropriate filters at left and clicking "add all files to cart"
####    unzip the downloaded archive and move the directory to the project folder

####    This Creates a Matrix with all our data
file.loc <- "PAAD-FPKM"  # the name of the data dir
file.manifest <- "files.json" # the name of the files manifest
cases.manifest <- "cases.json" # the name of the cases manifest
dsep <- "/"
files <- fromJSON(file=file.manifest)
cases <- fromJSON(file=cases.manifest)
dlfiles <- list.files(file.loc, recursive = TRUE)
#### remove the manifest, since we are using a separately downloaded json object
unlink(paste0(file.loc, dsep, "MANIFEST.txt"))
#for (file in 1:length(dlfiles)){
for (file in 1:5){
if(!exists("rna_table")){
## get the patient barcode
case_id = strsplit(dlfiles[file],"/")
## create a table with the expression profile where the count column is named with the patient barcode
rna_table<-read.delim(paste0(file.loc,dsep,dlfiles[file]), sep="\t", col.names = c("ensemble_id",case_id[[1]][2]))
} else {
case_id = strsplit(dlfiles[file],"/")
new_data<-read.delim(paste0(file.loc,dsep,dlfiles[file]), sep="\t", col.names = c("ensemble_id",case_id[[1]][2]))
rna_table <- full_join(rna_table, new_data, by = NULL)
}
}
rna_data <- as.matrix(rna_table)
rownames(rna_data) <- rna_data[,1]
rna_data <- rna_data[,-1]

####    This creates a Matrix of all the death dates
death_data <- matrix(nrow=ncol(rna_data),ncol=2)
colnames(death_data) <- c("filename", "days_to_death")

####    Do a sanity check
list.files(file.loc, recursive = TRUE)[1:10] # here are the files we read to get the read counts
str(death_data[1:5,]) # what our clinical data looks like
str(rna_data[1:5,1:5]) # what our sequencing data looks like

for (case in 1:ncol(rna_data)){
case_id <- files[[grep(colnames(rna_data)[case],files)]]$cases[[1]]$case_id
#days_to_death <- cases[grep(case_id, cases)][[1]]$diagnoses[[1]]$days_to_death
death_data[case,1] <- colnames(rna_data)[case]
#death_data[case,2] <- days_to_death
death_data[case,2] <- case_id
}

## Now the output (non errors) is fairly straightforward. Notice that I am running this on data from only the first 5 patients in the data folder in the interest of time:

> sink(file=message_logfile, type = "message")

> library(GSAR)

> library(org.Hs.eg.db)

> library(GSVAdata)

> library(GSEABase)

> library(GSVA)

> library(dplyr)

> data(c2BroadSets)

> #### The following should work with data from the GDC, where we have a shopping cart
> #### of zipped single patient sequencing data
> #### Example  .... [TRUNCATED]

> file.manifest <- "files.json" # the name of the files manifest

> cases.manifest <- "cases.json" # the name of the cases manifest

> dsep <- "/"

> files <- fromJSON(file=file.manifest)

> cases <- fromJSON(file=cases.manifest)

> dlfiles <- list.files(file.loc, recursive = TRUE)

> #### remove the manifest, since we are using a separately downloaded json object
> unlink(paste0(file.loc, dsep, "MANIFEST.txt"))

> #for (file in 1:length(dlfiles)){
> for (file in 1:5){
+   if(!exists("rna_table")){
+     ## get the patient barcode
+     case_id = strsplit(dlfil .... [TRUNCATED]

> rna_data <- as.matrix(rna_table)

> rownames(rna_data) <- rna_data[,1]

> rna_data <- rna_data[,-1]

> ####    This creates a Matrix of all the death dates
> death_data <- matrix(nrow=ncol(rna_data),ncol=2)

> colnames(death_data) <- c("filename", "days_to_death")

> ####    Do a sanity check
> list.files(file.loc, recursive = TRUE)[1:10] # here are the files we read to get the read counts
[1] "005c0660-3700-40ea-b037-b456319d369a/bb15d7d0-8705-49af-89e4-fc13c01de642.FPKM.txt.gz"
[2] "030cf06f-890c-4193-9c7d-254980c73a48/3d771128-9e90-49c2-8ee5-23d994ee6398.FPKM.txt.gz"
[3] "03a162ee-0be2-484d-ad86-17bba311a3f8/4172e3f8-3578-4f33-9168-6f8c2b8d0783.FPKM.txt.gz"
[4] "051918c1-9bb2-4146-bf85-4e4a55c5759e/5aed2227-1f31-4159-9eed-430bc45c61dc.FPKM.txt.gz"
[5] "0882ecec-b533-4912-adc1-8ffd6eaa47c1/c19f102d-47a0-48c6-9443-63730d9ea6d1.FPKM.txt.gz"
[6] "0ae4ff1f-e2d3-46e0-95a2-0ea80a4ebb63/574df2fc-a608-49c5-8e83-f26d03ef8bb3.FPKM.txt.gz"
[7] "0c2840a2-3a49-4f22-ae21-1cfbb0034212/fef65b57-c58d-4050-8de4-f09f5cd616ce.FPKM.txt.gz"
[8] "0dfe7aef-a105-4a32-89ca-49a30a1b59ed/65a45bca-b5d4-4763-a51f-f7b9ad9efcb9.FPKM.txt.gz"
[9] "0e7871dc-a721-4dae-8938-28a73ec3f968/232f085b-6201-4e4d-8473-e592b8d8e16d.FPKM.txt.gz"
[10] "101e042e-efa2-4c6c-b629-55ecbde859d2/3de80dcb-4ff2-4125-b8e6-9e06ec1cd833.FPKM.txt.gz"

> str(death_data[1:5,]) # what our clinical data looks like
logi [1:5, 1:2] NA NA NA NA NA NA ...
- attr(*, "dimnames")=List of 2
..$: NULL ..$ : chr [1:2] "filename" "days_to_death"

> str(rna_data[1:5,1:5]) # what our sequencing data looks like
chr [1:5, 1:5] "3.009793e-03" "2.945653e+00" "0.000000e+00" "3.861741e+00" ...
- attr(*, "dimnames")=List of 2
..$: chr [1:5] "ENSG00000270112.3" "ENSG00000167578.15" "ENSG00000273842.1" "ENSG00000078237.5" ... ..$ : chr [1:5] "bb15d7d0.8705.49af.89e4.fc13c01de642.FPKM.txt.gz" "X3d771128.9e90.49c2.8ee5.23d994ee6398.FPKM.txt.gz" "X4172e3f8.3578.4f33.9168.6f8c2b8d0783.FPKM.txt.gz" "X5aed2227.1f31.4159.9eed.430bc45c61dc.FPKM.txt.gz" ...

> for (case in 1:ncol(rna_data)){
+   case_id <- files[[grep(colnames(rna_data)[case],files)]]$cases[[1]]$case_id
+   #days_to_death <- cases[grep(cas .... [TRUNCATED]

## However, Somehow I am only able to locate the first data file in my files manifest. The last loop in the included source causes the following error to be recorded in my logged messages:

Joining, by = "ensemble_id"
Joining, by = "ensemble_id"
Joining, by = "ensemble_id"
Joining, by = "ensemble_id"
Error in files[[grep(colnames(rna_data)[case], files)]] :
attempt to select less than one element in get1index

## Thanks everybody for your help and looking forward to what you have to say :)

R RNA-Seq TCGA GDC • 3.2k views
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4
Entering edit mode
3.7 years ago

Edit: 21st September 2018:

More rapid ways of looking up TCGA barcodes from UUIDs or file-names:

## ----------------------------------------

Hello,

With the TCGA data, it can indeed be difficult to just figure out which sample is which. A lot of time and effort has to be invested just to organise the data for a particular project. Here's how I did it for a recent TCGA dataset that I re-analysed:

In order to search for the Case UUID from these filenames:

• download the manifest for your data in JSon format, from here
• In R, get all of your HTseq FPKM count filenames in a vector or list called filenames (e.g. c("657e19a6-e481-4d06-8613-1a93677f3425.FPKM.txt.gz", "b244f324-fd8a-4d4b-b8f5-bad973c649d5.FPKM.txt.gz", ..., et cetera))
• Loop through each filename and look up their Case UUID in the JSon file, with a loop like this:

.

require(rjson)
manifest <- fromJSON(file="RNAseqFPKM.json")
caseUUIDs <- c()

for (i in 1:length(filenames))
{
record <- manifest[[grep(filenames[i], manifest, fixed=TRUE, ignore.case=FALSE)]]
if (filenames[i]!=record$file_name) { print("FALSE") } caseUUIDs[i] <- record$cases[[1]]\$case_id
}

I added the inner if statement to print 'FALSE' to screen if any file has no matching record (I never encountered such a situation).

The loop will take a while to run (maybe 5-10 minutes for 150 filenames) - I could possibly develop it further with lapply or mclappy but it's one of those nuisance bit of codings that I always put on the back burner and just tolerate.

Hope that this helps you out!

Kevin

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0
Entering edit mode

Hi Kevin, I've used the downloaded manifest strategy above with limited success. Can you have a look?

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0
Entering edit mode

Hey, I see that you edited your original question with code - thanks!

The problem seems to be 2-fold:

1. The filenames in the Json file have segments (within the name) separated by hyphens, not dots, e.g., bb15d7d0-8705-49af-89e4-fc13c01de642.FPKM.txt.gz and not bb15d7d0.8705.49af.89e4.fc13c01de642.FPKM.txt.gz. Your filenames have dots, so, they won't match.
2. Some of your filenames have an 'X' at the beginning, which was added automatically by R if it sees a number at the start of the name

Both of these problems are related to the fact that your filenames were assigned as colnames, which in R means that you cannot have a hyphen in the colname, neither can the colname begin with a number. You'll have to save the filenames earlier / use an earlier listing of the filenames.

Hope that this makes sense?

Kevin

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0
Entering edit mode

Hi Kevin, Those suggestions solved the problem. I ended up just renaming the columns in the RNA seq matrix generically, then building a lookup table for the file names. Now I have several questions about the analysis of this data and I created a second post to address these. Would you be willing to take a look?

Linking patient survival to gene-set analysis

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0
Entering edit mode

Hi! Okay, I will take a look but not until later today (Saturday) or Sunday. I will give someone else a chance to take a look too.

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1
Entering edit mode
3.7 years ago

Hi Matt,

I've in fact just done this but with a different TCGA dataset (SKCM). What I did was download the JSON dump of all the cases in your dataset (which, if I have understood correctly, in your case should be here:

https://portal.gdc.cancer.gov/repository?facetTab=files&filters=%7B%22op%22%3A%22and%22%2C%22content%22%3A%5B%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22TCGA-PAAD%22%5D%7D%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22files.analysis.workflow_type%22%2C%22value%22%3A%5B%22HTSeq%20-%20FPKM%22%5D%7D%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22files.data_category%22%2C%22value%22%3A%5B%22Transcriptome%20Profiling%22%5D%7D%7D%5D%7D&searchTableTab=files

And click in JSON (Export all)).

Then, that file gives you the mapping from file ID to case UUID (patient). Object example:

{
"file_name": "232f085b-6201-4e4d-8473-e592b8d8e16d.FPKM.txt.gz",
"data_format": "TXT",
"access": "open",
"data_category": "Transcriptome Profiling",
"file_size": 514027,
"cases": [
{
"project": {
"project_id": "TCGA-PAAD"
},
"case_id": "620e0648-ec20-4a12-a6cb-5546fe829c77"
}
],
"annotations": [
{
"annotation_id": "050203a0-12ab-5025-973d-e070d94f722b"
}
]
}

I needed the TCGA Barcode so with the case ID I just did a query like this:

curl https://api.gdc.cancer.gov/cases/620e0648-ec20-4a12-a6cb-5546fe829c77?pretty=true

And grep for "submitter_id". ("submitter_id": "TCGA-HZ-7918").

Hope this helps! :)

Daniela

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0
Entering edit mode

Hi Daniela, I think I obtained the right manifest and it includes records with relevant parts of the data model, but somehow I am still failing to match my files to patient IDs. I have updated my post above. Can you take a look? Thanks for all your help!

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