curatedCRCData: Clinically Annotated Data for the Colorectal Cancer Transcriptome

This package represents a manually curated data collection for gene expression meta-analysis of patients with colorectal cancer. This resource provides uniformly prepared microarray data with curated and documented clinical metadata. It allows a computational user to efficiently identify studies and patient subgroups of interest for analysis and to run such analyses immediately without the challenges posed by harmonizing heterogeneous microarray technologies, study designs, expression data processing methods, and clinical data formats.

In this vignette, we give a short tour of the package and will show how to use it efficiently.

Load data sets

Loading a single dataset is very easy. First we load the package:

library(curatedCRCData)

To get a listing of all the datasets, use the data function:

data(package="curatedCRCData")

Now to load a single dataset, we use the data function again:

data(TCGA.COAD_eset)
TCGA.COAD_eset

## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 17814 features, 130 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: TCGA.AA.3520 TCGA.AA.3532 ... TCGA.A6.2685 (130 total)
##   varLabels: unique_patient_ID alt_sample_name ...
##     uncurated_author_metadata (59 total)
##   varMetadata: labelDescription
## featureData
##   featureNames: 15E1.2 2'-PDE ... ZZZ3 (17814 total)
##   fvarLabels: probeset gene
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
##   pubMedIds: 22810696 
## Annotation: agilent-014850 whole human genome microarray 4x44k g4112f

The datasets are provided as Bioconductor ExpressionSet objects and we refer to the Bioconductor documentation for users unfamiliar with this data structure.

Load datasets based on rules

For a meta-analysis, we typically want to filter datasets and patients to get a population of patients we are interested in. We provide a short but powerful R script that does the filtering and provides the data as a list of ExpressionSet objects. One can use this script within R by first sourcing a config file which specifies the filters, like the minimum numbers of patients in each dataset. It is also possible to filter samples by annotation, for example to remove early stage and normal samples.

source(system.file("extdata", 
"patientselection_all.config",package="curatedCRCData"))
ls()

## [1] "keep.common.only"     "meta.required"        "min.number.of.events"
## [4] "min.sample.size"      "quantile.cutoff"      "rescale"             
## [7] "strict.checking"      "TCGA.COAD_eset"

See what the values of these variables we have loaded are. The variable names are fairly descriptive, but note that rule.1 is a character vector of length 2, where the first entry is the name of a clinical data variable, and the second entry is a Regular Expression providing a requirement for that variable. Any number of rules can be added, with increasing identifiers, e.g. rule.2, rule.3, etc.

Here strict.checking is FALSE, meaning that samples not annotated for the variables in these rules are allowed to pass the filter. If strict.checking == TRUE, samples missing this annotation will be removed.

sapply(ls(), get)

## $keep.common.only
## [1] FALSE
## 
## $meta.required
## NULL
## 
## $min.number.of.events
## [1] 0
## 
## $min.sample.size
## [1] 1
## 
## $quantile.cutoff
## [1] 0
## 
## $rescale
## [1] FALSE
## 
## $strict.checking
## [1] FALSE
## 
## $TCGA.COAD_eset
## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 17814 features, 130 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: TCGA.AA.3520 TCGA.AA.3532 ... TCGA.A6.2685 (130 total)
##   varLabels: unique_patient_ID alt_sample_name ...
##     uncurated_author_metadata (59 total)
##   varMetadata: labelDescription
## featureData
##   featureNames: 15E1.2 2'-PDE ... ZZZ3 (17814 total)
##   fvarLabels: probeset gene
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
##   pubMedIds: 22810696 
## Annotation: agilent-014850 whole human genome microarray 4x44k g4112f

Now that we have defined the sample filter, we create a list of ExpressionSet objects by sourcing the createEsetList.R file:

source(system.file("extdata", "createEsetList.R", package = "curatedCRCData"))

## 2026-10-10 11:19:21.322344 INFO::Inside script createEsetList.R - inputArgs =
## 2026-10-10 11:19:21.329538 INFO::None provided
## 2026-10-10 11:19:21.349417 INFO::Loading curatedCRCData 2.44.0
## 2026-10-10 11:19:58.057892 INFO::Clean up the esets.
## 2026-10-10 11:19:58.124293 INFO::including GSE11237_eset
## 2026-10-10 11:19:58.140548 INFO::including GSE12225.GPL3676_eset
## 2026-10-10 11:19:58.162058 INFO::including GSE12945_eset
## 2026-10-10 11:19:58.184659 INFO::including GSE13067_eset
## 2026-10-10 11:19:58.235248 INFO::including GSE13294_eset
## 2026-10-10 11:19:58.61743 INFO::including GSE14095_eset
## 2026-10-10 11:19:58.675816 INFO::including GSE14333_eset
## 2026-10-10 11:19:58.717725 INFO::including GSE16125.GPL5175_eset
## 2026-10-10 11:19:58.759736 INFO::including GSE17536_eset
## 2026-10-10 11:19:58.795478 INFO::including GSE17537_eset
## 2026-10-10 11:19:58.844567 INFO::including GSE17538.GPL570_eset
## 2026-10-10 11:19:59.209286 INFO::including GSE18105_eset
## 2026-10-10 11:19:59.272408 INFO::including GSE2109_eset
## 2026-10-10 11:19:59.336122 INFO::including GSE21510_eset
## 2026-10-10 11:19:59.380396 INFO::including GSE21815_eset
## 2026-10-10 11:19:59.40947 INFO::including GSE24549.GPL5175_eset
## 2026-10-10 11:19:59.427913 INFO::including GSE24550.GPL5175_eset
## 2026-10-10 11:19:59.448942 INFO::including GSE2630_eset
## 2026-10-10 11:19:59.46313 INFO::including GSE26682.GPL570_eset
## 2026-10-10 11:19:59.814114 INFO::including GSE26682.GPL96_eset
## 2026-10-10 11:19:59.837669 INFO::including GSE26906_eset
## 2026-10-10 11:19:59.856561 INFO::including GSE27544_eset
## 2026-10-10 11:19:59.882893 INFO::including GSE28702_eset
## 2026-10-10 11:19:59.905859 INFO::including GSE3294_eset
## 2026-10-10 11:19:59.929079 INFO::including GSE33113_eset
## 2026-10-10 11:20:00.0036 INFO::including GSE39582_eset
## 2026-10-10 11:20:00.075058 INFO::including GSE3964_eset
## 2026-10-10 11:20:00.085587 INFO::including GSE4045_eset
## 2026-10-10 11:20:00.099913 INFO::including GSE4526_eset
## 2026-10-10 11:20:00.116635 INFO::including GSE45270_eset
## 2026-10-10 11:20:00.157516 INFO::including TCGA.COAD_eset
## 2026-10-10 11:20:00.182791 INFO::including TCGA.READ_eset
## 2026-10-10 11:20:00.216861 INFO::including TCGA.RNASeqV2_eset
## 2026-10-10 11:20:00.250669 INFO::including TCGA.RNASeqV2.READ_eset
## 2026-10-10 11:20:00.407861 INFO::Ids with missing data: GSE2630_eset, GSE3294_eset, TCGA.COAD_eset, TCGA.READ_eset

It is also possible to run the script from the command line and then load the R data file within R:

R --vanilla "--args patientselection.config crc.eset.rda tmp.log"  < createEsetList.R 

Now we have 34 datasets with samples that passed our filter in a list of ExpressionSet objects called esets:

names(esets)

##  [1] "GSE11237_eset"           "GSE12225.GPL3676_eset"  
##  [3] "GSE12945_eset"           "GSE13067_eset"          
##  [5] "GSE13294_eset"           "GSE14095_eset"          
##  [7] "GSE14333_eset"           "GSE16125.GPL5175_eset"  
##  [9] "GSE17536_eset"           "GSE17537_eset"          
## [11] "GSE17538.GPL570_eset"    "GSE18105_eset"          
## [13] "GSE2109_eset"            "GSE21510_eset"          
## [15] "GSE21815_eset"           "GSE24549.GPL5175_eset"  
## [17] "GSE24550.GPL5175_eset"   "GSE2630_eset"           
## [19] "GSE26682.GPL570_eset"    "GSE26682.GPL96_eset"    
## [21] "GSE26906_eset"           "GSE27544_eset"          
## [23] "GSE28702_eset"           "GSE3294_eset"           
## [25] "GSE33113_eset"           "GSE39582_eset"          
## [27] "GSE3964_eset"            "GSE4045_eset"           
## [29] "GSE4526_eset"            "GSE45270_eset"          
## [31] "TCGA.COAD_eset"          "TCGA.READ_eset"         
## [33] "TCGA.RNASeqV2_eset"      "TCGA.RNASeqV2.READ_eset"

Non-unique gene symbols

In the standard version of curatedCRCData (the version available on Bioconductor), we collapse manufacturer probesets to official HGNC symbols using the Biomart database. Some probesets are mapped to multiple HGNC symbols in this database. For these probesets, we provide all the symbols. For example 220159_at maps to ABCA11P and ZNF721 and we provide ABCA11P///ZNF721 as probeset name. If you have an array of gene symbols for which you want to access the expression data, “ABCA11P” would not be found in curatedCRCData in this example. The following function will create a new ExpressionSet in which both ZNF721 and ABCA11P are features with identical expression data:

expandProbesets <- function (eset, sep = "///") 
{
    x <- lapply(featureNames(eset), function(x) strsplit(x, sep)[[1]])
    eset <- eset[order(sapply(x, length)), ]
    x <- lapply(featureNames(eset), function(x) strsplit(x, sep)[[1]])
    idx <- unlist(sapply(1:length(x), function(i) rep(i, length(x[[i]]))))
    xx <- !duplicated(unlist(x))
    idx <- idx[xx]
    x <- unlist(x)[xx]
    eset <- eset[idx, ]
    featureNames(eset) <- x
    eset
}

X <- TCGA.COAD_eset[head(grep("AA", featureNames(TCGA.COAD_eset))),]
exprs(X)[,1:3]

##         TCGA.AA.3520 TCGA.AA.3532 TCGA.AA.3553
## AAAS        -0.72125     -1.51150     -1.01250
## AACS         0.02225      0.82375     -0.08500
## AADAC        0.02775     -0.42900      1.12525
## AADACL1      1.06600      1.85550      0.92800
## AADACL2      0.08750     -0.61825     -0.47525
## AADACL3      0.38100      0.31100      0.33950

exprs(expandProbesets(X))[,1:3]

##         TCGA.AA.3520 TCGA.AA.3532 TCGA.AA.3553
## AAAS        -0.72125     -1.51150     -1.01250
## AACS         0.02225      0.82375     -0.08500
## AADAC        0.02775     -0.42900      1.12525
## AADACL1      1.06600      1.85550      0.92800
## AADACL2      0.08750     -0.61825     -0.47525
## AADACL3      0.38100      0.31100      0.33950

Appendix

Available Clinical Characteristics

Available clinical annotation. This heatmap visualizes for each curated clinical characteristic (rows) the availability in each dataset (columns). Red indicates that the corresponding characteristic is available for at least one sample in the dataset.

source(system.file("extdata", "summarizeEsets.R", package = "curatedCRCData"))

## Warning in min(which(km.fit$surv < 0.5)): no non-missing arguments to min;
## returning Inf
## Warning in min(which(km.fit$surv < 0.5)): no non-missing arguments to min;
## returning Inf

## Warning in min(which(reverse.km.fit$surv < 0.5)): no non-missing arguments to
## min; returning Inf
## Warning in min(which(reverse.km.fit$surv < 0.5)): no non-missing arguments to
## min; returning Inf
## Warning in min(which(reverse.km.fit$surv < 0.5)): no non-missing arguments to
## min; returning Inf

(myfile <- tempfile())

## [1] "/tmp/RtmpnXSEr8/filee8436f7fc82"

write.table(summary.table, file=myfile, row.names=FALSE, quote=TRUE, sep=",")

library(curatedCRCData)
library(Biobase)
data(TCGA.COAD_eset)
write.csv(exprs(TCGA.COAD_eset), file="TCGA.COAD_eset_exprs.csv")
write.csv(pData(TCGA.COAD_eset), file="TCGA.COAD_eset_clindata.csv")

data.to.fetch <- c("TCGA.COAD_eset", "GSE37317_eset")
for (onedata in data.to.fetch){
    print(paste("Fetching", onedata))
    data(list=onedata)
    write.csv(exprs(get(onedata)), file=paste(onedata, "_exprs.csv", sep=""))
    write.csv(pData(get(onedata)), file=paste(onedata, "_clindata.csv", sep=""))
}

## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] 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   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] knitr_1.52            logging_0.10-111      survival_3.8-12      
##  [4] curatedCRCData_2.44.0 Biobase_2.72.0        BiocGenerics_0.58.1  
##  [7] generics_0.1.4        xtable_1.8-8          sva_3.60.0           
## [10] BiocParallel_1.46.0   genefilter_1.94.0     mgcv_1.9-4           
## [13] nlme_3.1-171          BiocStyle_2.40.0     
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.10           RSQLite_3.53.3        lattice_0.23-1       
##  [4] digest_0.6.39         evaluate_1.0.5        grid_4.6.1           
##  [7] fastmap_1.2.0         blob_1.3.0            jsonlite_2.0.0       
## [10] Matrix_1.7-6          AnnotationDbi_1.74.0  limma_3.68.5         
## [13] DBI_1.3.0             BiocManager_1.30.27   httr_1.4.9           
## [16] XML_3.99-0.25         Biostrings_2.80.2     codetools_0.2-20     
## [19] jquerylib_0.1.4       cli_3.6.6             rlang_1.3.0          
## [22] crayon_1.5.3          XVector_0.52.0        bit64_4.8.6          
## [25] splines_4.6.1         cachem_1.1.0          yaml_2.3.12          
## [28] parallel_4.6.1        tools_4.6.1           annotate_1.90.0      
## [31] memoise_2.0.1         locfit_1.5-9.12       buildtools_1.0.0     
## [34] vctrs_0.7.3           R6_2.6.1              png_0.1-9            
## [37] matrixStats_1.5.0     stats4_4.6.1          lifecycle_1.0.5      
## [40] Seqinfo_1.2.0         KEGGREST_1.52.2       edgeR_4.10.5         
## [43] S4Vectors_0.50.3      IRanges_2.46.0        bit_4.6.0            
## [46] bslib_0.12.0          statmod_1.5.2         xfun_0.61            
## [49] sys_3.4.3             MatrixGenerics_1.24.0 htmltools_0.5.9      
## [52] rmarkdown_2.32        maketools_1.3.2       compiler_4.6.1