Introduction

This vignette demonstrates how to access data resources provided by CLAMPData, the companion ExperimentHub data package for CLAMP, through the ExperimentHub interface.

CLAMPData provides curated gene-set libraries, pathway priors, and example expression matrices used by the CLAMP software package for prior-informed latent variable modeling.

Installation

Install CLAMPData from Bioconductor with BiocManager:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("CLAMPData")

Load packages

library(CLAMPData)
library(ExperimentHub)

Available Resources

CLAMPData provides three ExperimentHub resources:

You can also list these resources with list_clamp_data():

list_clamp_data()

##                               name                         accessor
## 1 GSE164416_DP_htseq_counts_txt_gz GSE164416_DP_htseq_counts_txt_gz
## 2           human_gene_v2_5_alz_h5           human_gene_v2_5_alz_h5
## 3              islets_metadata_csv              islets_metadata_csv
##                                                              description   type
## 1 HTSeq-counts (gene-level) text file from GSE164416 for CLAMP examples. TXT.gz
## 2            HDF5 file used in CLAMP vignettes (human_gene_v2.5_alz.h5).   HDF5
## 3               Sample metadata table for islet RNA-seq example (CLAMP).    CSV
##        species   eh_id version
## 1 Homo sapiens EH10279      v1
## 2 Homo sapiens EH10280      v1
## 3 Homo sapiens EH10281      v1

Accessing data resources

p_counts <- GSE164416_DP_htseq_counts_txt_gz()

p_h5     <- human_gene_v2_5_alz_h5()

p_meta   <- islets_metadata_csv()

cat("Counts path:", p_counts, "\n")

## Counts path: /github/home/.cache/R/ExperimentHub/14c147c5481f_10346

cat("H5 path:", p_h5, "\n")

## H5 path: /github/home/.cache/R/ExperimentHub/14c14086c822_10347

cat("Metadata path:", p_meta, "\n")

## Metadata path: /github/home/.cache/R/ExperimentHub/14c11dbc6ce9_10348

Reading example data

# --- Counts Data (GSE164416) ---
counts <- read_islet_counts()
cat("Counts dimensions:", nrow(counts), "genes x", ncol(counts) - 1, "samples\n\n")

## Counts dimensions: 58336 genes x 133 samples

knitr::kable(counts[1:5, 1:6], caption = "Gene counts (first 5 genes)")

# --- Sample Metadata ---
meta <- read_islet_metadata()
cat("Metadata dimensions:", nrow(meta), "samples x", ncol(meta), "variables\n")

## Metadata dimensions: 133 samples x 2 variables

cat("Sample types:", paste(unique(meta$type), collapse = ", "), "\n\n")

## Sample types: T3cD, T2D, IGT, ND, IFG

knitr::kable(head(meta), caption = "Sample metadata (first 6 rows)")

CLAMP HDF5 files follow a fixed layout, shown by clamp_h5_schema():

clamp_h5_schema()

##                          path                     type
## 1            /data/expression matrix (samples x genes)
## 2          /meta/genes/symbol                character
## 3 /meta/samples/geo_accession                character
##                                                description
## 1 Expression matrix; transposed to genes x samples at load
## 2                           Gene symbols, length = n_genes
## 3                   Sample identifiers, length = n_samples

validate_clamp_h5() checks that a file contains the datasets in this layout. It returns TRUE for a valid file and errors otherwise, so you can use it on your own HDF5 files:

if (requireNamespace("rhdf5", quietly = TRUE)) {
  print(validate_clamp_h5(human_gene_v2_5_alz_h5()))
}

## [1] TRUE

read_clamp_alz_expression() runs this same check, then returns a genes x samples matrix:

# --- HDF5 Gene-Set Priors ---
if (requireNamespace("rhdf5", quietly = TRUE)) {
  expr <- read_clamp_alz_expression()
  cat("Expression matrix:", nrow(expr), "genes x", ncol(expr), "samples\n")
} else {
    message("Install 'rhdf5' from Bioconductor to inspect HDF5 contents: ",
          "BiocManager::install('rhdf5')")
}

## Expression matrix: 67186 genes x 54 samples

Session info

sessionInfo()

## 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] ExperimentHub_3.3.2  AnnotationHub_4.3.2  BiocFileCache_3.3.0 
## [4] dbplyr_2.6.0         BiocGenerics_0.59.12 generics_0.1.4      
## [7] CLAMPData_0.99.5     BiocStyle_2.41.0    
## 
## loaded via a namespace (and not attached):
##  [1] rappdirs_0.3.4       sass_0.4.10          BiocVersion_3.24.0  
##  [4] RSQLite_3.53.3       digest_0.6.39        magrittr_2.0.5      
##  [7] evaluate_1.0.5       fastmap_1.2.0        blob_1.3.0          
## [10] jsonlite_2.0.0       AnnotationDbi_1.75.2 DBI_1.3.0           
## [13] BiocManager_1.30.27  httr_1.4.9           purrr_1.2.2         
## [16] Biostrings_2.81.9    httr2_1.3.0          jquerylib_0.1.4     
## [19] cli_3.6.6            crayon_1.5.3         rlang_1.3.0         
## [22] XVector_0.53.0       Biobase_2.73.2       bit64_4.8.6         
## [25] withr_3.0.3          cachem_1.1.0         yaml_2.3.12         
## [28] BiocBaseUtils_1.15.1 otel_0.2.0           tools_4.6.1         
## [31] memoise_2.0.1        dplyr_1.2.1          Rhdf5lib_2.1.0      
## [34] filelock_1.0.3       curl_8.0.0           png_0.1-9           
## [37] buildtools_1.0.0     vctrs_0.7.3          R6_2.6.1            
## [40] rhdf5_2.57.20        stats4_4.6.1         lifecycle_1.0.5     
## [43] Seqinfo_1.3.2        KEGGREST_1.53.6      IRanges_2.47.5      
## [46] S4Vectors_0.51.10    bit_4.6.0            pkgconfig_2.0.3     
## [49] bslib_0.12.0         pillar_1.11.1        glue_1.8.1          
## [52] xfun_0.61            tibble_3.3.1         tidyselect_1.2.1    
## [55] rhdf5filters_1.25.4  sys_3.4.3            knitr_1.52          
## [58] htmltools_0.5.9      rmarkdown_2.32       maketools_1.3.2     
## [61] compiler_4.6.1