Contents

1 Accessing human HNSC scRNASeq data using Bioconductor’s ExperimentHub

Transcripts per million (TPM) single cell RNA-Seq data for 5,902 cells from 18 patients–oral cavity head and neck squamous cell carcinoma (HNSC)– are available from GEO GSE103322. These data are also available as a SingleCellExpression from ExperimentHub.

In the example below, we show how this dataset can be dwnloaded from ExperimentHub.

library(ExperimentHub)
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##     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
##     pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
##     union, unique, unsplit, which.max, which.min
## Loading required package: AnnotationHub
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library(SingleCellExperiment)
## Loading required package: SummarizedExperiment
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eh = ExperimentHub()
dset <- query(eh , "GSE103322")
dset
## ExperimentHub with 1 record
## # snapshotDate(): 2024-04-29
## # names(): EH5419
## # package(): GSE103322
## # $dataprovider: GEO
## # $species: Homo sapiens
## # $rdataclass: SingleCellExperiment
## # $rdatadateadded: 2021-03-04
## # $title: Single cell RNA-seq data for human head and neck squamous cell car...
## # $description: scRNA-Sequencing data and metadata for 5902 cells  from 18 p...
## # $taxonomyid: 9606
## # $genome: hg19
## # $sourcetype: tar.gz
## # $sourceurl: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103322
## # $sourcesize: NA
## # $tags: c("CancerData", "DNASeqData", "ExpressionData", "Genome",
## #   "GEO", "Homo_sapiens_Data", "RNASeqData", "SingleCellData") 
## # retrieve record with 'object[["EH5419"]]'

One can then extract the data for this using

sce <- dset[[1]]
## see ?GSE103322 and browseVignettes('GSE103322') for documentation
## loading from cache

1.1 Exploring the metadata

The metadata is available from the SingleCellExpression object with

head(SummarizedExperiment::colData(sce))
## DataFrame with 6 rows and 5 columns
##                        processed.by.Maxima.enzyme  Lymph.node
##                                       <character> <character>
## HN28_P15_D06_S330_comb                          1           1
## HN28_P6_G05_S173_comb                           1           0
## HN26_P14_D11_S239_comb                          1           1
## HN26_P14_H05_S281_comb                          1           1
## HN26_P25_H09_S189_comb                          1           1
## HN26_P14_H06_S282_comb                          1           1
##                        classified..as.cancer.cell
##                                       <character>
## HN28_P15_D06_S330_comb                          0
## HN28_P6_G05_S173_comb                           0
## HN26_P14_D11_S239_comb                          1
## HN26_P14_H05_S281_comb                          0
## HN26_P25_H09_S189_comb                          1
## HN26_P14_H06_S282_comb                          1
##                        classified.as.non.cancer.cells non.cancer.cell.type
##                                           <character>          <character>
## HN28_P15_D06_S330_comb                              1           Fibroblast
## HN28_P6_G05_S173_comb                               1           Fibroblast
## HN26_P14_D11_S239_comb                              0                    0
## HN26_P14_H05_S281_comb                              1           Fibroblast
## HN26_P25_H09_S189_comb                              0                    0
## HN26_P14_H06_S282_comb                              0                    0

For example, to obtain the number of cells classified as non-tumor types

table(SummarizedExperiment::colData(sce)$non.cancer.cell.type)
## 
## -Fibroblast           0      B cell   Dendritic Endothelial  Fibroblast 
##          18        2539         138          51         260        1422 
##  Macrophage        Mast      T cell     myocyte 
##          98         120        1237          19

1.2 Extracting the data

The data can be extracted from the SingleCellExpression object with

dset <- SummarizedExperiment::assays(sce)$TPM
dim(dset)
## [1] 21341  5902
dset[1:4, 1:3]
##        HN28_P15_D06_S330_comb HN28_P6_G05_S173_comb HN26_P14_D11_S239_comb
## 401546                 0.0000                0.0000                0.42761
## 6205                   6.0037                7.3006                7.28850
## 63916                  0.0000                0.0000                0.00000
## 90993                  0.0000                0.0000                0.00000

2 sessionInfo()

sessionInfo()
## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [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: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] GSE103322_1.10.0            GEOquery_2.72.0            
##  [3] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
##  [5] Biobase_2.64.0              GenomicRanges_1.56.0       
##  [7] GenomeInfoDb_1.40.0         IRanges_2.38.0             
##  [9] S4Vectors_0.42.0            MatrixGenerics_1.16.0      
## [11] matrixStats_1.3.0           ExperimentHub_2.12.0       
## [13] AnnotationHub_3.12.0        BiocFileCache_2.12.0       
## [15] dbplyr_2.5.0                BiocGenerics_0.50.0        
## [17] BiocStyle_2.32.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1        dplyr_1.1.4             blob_1.2.4             
##  [4] filelock_1.0.3          Biostrings_2.72.0       fastmap_1.1.1          
##  [7] digest_0.6.35           mime_0.12               lifecycle_1.0.4        
## [10] statmod_1.5.0           KEGGREST_1.44.0         RSQLite_2.3.6          
## [13] magrittr_2.0.3          compiler_4.4.0          rlang_1.1.3            
## [16] sass_0.4.9              tools_4.4.0             utf8_1.2.4             
## [19] yaml_2.3.8              data.table_1.15.4       knitr_1.46             
## [22] S4Arrays_1.4.0          bit_4.0.5               curl_5.2.1             
## [25] DelayedArray_0.30.0     xml2_1.3.6              abind_1.4-5            
## [28] withr_3.0.0             purrr_1.0.2             grid_4.4.0             
## [31] fansi_1.0.6             cli_3.6.2               rmarkdown_2.26         
## [34] crayon_1.5.2            generics_0.1.3          httr_1.4.7             
## [37] tzdb_0.4.0              DBI_1.2.2               cachem_1.0.8           
## [40] zlibbioc_1.50.0         AnnotationDbi_1.66.0    BiocManager_1.30.22    
## [43] XVector_0.44.0          vctrs_0.6.5             Matrix_1.7-0           
## [46] jsonlite_1.8.8          bookdown_0.39           hms_1.1.3              
## [49] bit64_4.0.5             limma_3.60.0            jquerylib_0.1.4        
## [52] tidyr_1.3.1             glue_1.7.0              BiocVersion_3.19.1     
## [55] UCSC.utils_1.0.0        tibble_3.2.1            pillar_1.9.0           
## [58] rappdirs_0.3.3          htmltools_0.5.8.1       GenomeInfoDbData_1.2.12
## [61] R6_2.5.1                evaluate_0.23           lattice_0.22-6         
## [64] readr_2.1.5             png_0.1-8               memoise_2.0.1          
## [67] bslib_0.7.0             SparseArray_1.4.0       xfun_0.43              
## [70] pkgconfig_2.0.3