Contents

1 Introduction

Comprehensive archiving of genome-scale sequencing experiments is valuable for substantive and methodological progress in multiple domains.

The HumanTranscriptomeCompendium package provides functions for interacting with quantifications and metadata for over 180000 sequenced human transcriptomes.

2 Access to gene-level quantifications

BiocFileCache is used to manage access to a modest collection of metadata about compendium contents. By default, htx_load will load the cache and establish a connection to remote HDF5 representation of quantifications. The numerical data is lodged in an instance of the HDF Scalable Data Service, at http://hsdshdflab.hdfgroup.org.

library(HumanTranscriptomeCompendium)
genelev = htx_load()
## Loading required namespace: BiocFileCache
## adding rname 'https://biocfound-bigrnatx.s3.us-west-2.amazonaws.com/rangedHtxGeneSE.rds'
genelev
## class: RangedSummarizedExperiment 
## dim: 58288 181134 
## metadata(1): rangeSource
## assays(1): counts_lstpm
## rownames(58288): ENSG00000000003.14 ENSG00000000005.5 ...
##   ENSG00000284747.1 ENSG00000284748.1
## rowData names(0):
## colnames(181134): DRX001125 DRX001126 ... SRX999990 SRX999991
## colData names(4): experiment_accession experiment_platform
##   study_accession study_title
if (rhdf5client::check_hsds()) assay(genelev)
## <58288 x 181134> matrix of class DelayedMatrix and type "double":
##                       DRX001125    DRX001126    DRX001127 ...  SRX999990
## ENSG00000000003.14    40.001250  1322.844547  1528.257578   .  1149.0341
##  ENSG00000000005.5     0.000000     9.999964     6.000006   .     0.0000
## ENSG00000000419.12    64.000031  1456.004418  2038.996875   .  1485.0003
## ENSG00000000457.13    31.814591  1583.504257  1715.041308   .   631.7751
## ENSG00000000460.16    12.430602   439.321234   529.280324   .   945.6903
##                ...            .            .            .   .          .
##  ENSG00000284744.1   1.05614505  24.81388079  32.29261298   .   7.316061
##  ENSG00000284745.1   0.99999879  15.99996994  16.99999743   .   0.000000
##  ENSG00000284746.1   0.00000000   0.00379458   0.00000000   .   0.000000
##  ENSG00000284747.1   7.77564984 270.83296409 239.88056843   . 108.011633
##  ENSG00000284748.1   1.00000768  22.23010514  37.73881938   .  11.278980
##                     SRX999991
## ENSG00000000003.14  1430.3955
##  ENSG00000000005.5     0.0000
## ENSG00000000419.12  1970.0004
## ENSG00000000457.13   802.0563
## ENSG00000000460.16  1259.7648
##                ...          .
##  ENSG00000284744.1   3.268453
##  ENSG00000284745.1   0.000000
##  ENSG00000284746.1   0.000000
##  ENSG00000284747.1  94.606851
##  ENSG00000284748.1   5.240970

2.1 Identifying single-cell RNA-seq studies

We use crude pattern-matching in the study titles to identify single cell RNA-seq experiments

sing = grep("single.cell", genelev$study_title, 
   ignore.case=TRUE)
length(sing)
## [1] 59886

Now we will determine which studies are involved. We will check out the titles of the single-cell studies to assess the specificity of this approach.

sa = genelev$study_accession[sing]
sing2 = sing[-which(duplicated(sa))]
length(sing2)
## [1] 142
datatable(as.data.frame(colData(genelev[,sing2])),
   options=list(lengthMenu=c(3,5,10,50,100)))

2.2 Collecting bulk RNA-seq samples on a disease of interest: glioblastoma

bulk = genelev[,-sing]
kpglio = grep("glioblastoma", bulk$study_title, 
  ignore.case=TRUE)
glioGene = bulk[,kpglio]
glioGene
## class: RangedSummarizedExperiment 
## dim: 58288 662 
## metadata(1): rangeSource
## assays(1): counts_lstpm
## rownames(58288): ENSG00000000003.14 ENSG00000000005.5 ...
##   ENSG00000284747.1 ENSG00000284748.1
## rowData names(0):
## colnames(662): ERX1097381 ERX1097382 ... SRX972028 SRX972029
## colData names(4): experiment_accession experiment_platform
##   study_accession study_title

To acquire numerical values, as.matrix(assay()) is needed.

if (rhdf5client::check_hsds()) {
 beeswarm(as.matrix(assay(
   glioGene["ENSG00000138413.13",1:100])), pwcol=as.numeric(factor(glioGene$study_title[1:100])), ylab="IDH1 expression")
 legend(.6, 15000, legend=unique(glioGene$study_accession[1:100]),
   col=1:2, pch=c(1,1))
}
## Warning in data.frame(x = x.new, y = y): row names were found from a short
## variable and have been discarded

3 Access to transcript-level quantifications

This feature is not available until further notice. By setting genesOnly to FALSE in htx_load, we can obtain a transcript-level version of the compendium. Note that the number of samples in this version exceeds that of the gene version by two. There are two unintended columns in the underlying HDF Cloud array, with names ‘X0’ and ‘X0.1’, that should be ignored.

txlev = htx_load(genesOnly=FALSE)
txlev

4 Resources of the HumanTranscriptomeCompendium package

The primary purposes of the HumanTranscriptomeCompendium package are

We will address these in turn.

4.1 Access to the quantifications

4.1.1 htx_load

htx_load has three arguments: remotePath, cache, and genesOnly.

genesOnly defaults to TRUE. If it is TRUE, the HDF array that will be used consists of gene-level quantifications; otherwise the array in use will consist of transcript-level quantifications based on the Gencode V27 models.

remotePath is the path to an RDS-formatted RangedSummarizedExperiment instance that has been prepared to include a DelayedArray reference to the HSDS representation of the quantifications. The specific reference used depends on the setting of genesOnly. The default value currently references an AWS S3 bucket to retrieve the RDS.

cache is an instance of BiocFileCache, where the RDS will be stored and retrieved as needed.

A typical use is htx = htx_load() which efficiently sets up htx to give access to gene-level quantifications. After such a command is issued, assay(htx[G, S]) is the DelayedMatrix for features G on samples S. If G or S are too long, the HSDS may return an error. Systematic chunking of large requests is a topic of future development.

4.1.2 htx_query_by_study_accession

htx_query_by_study_accession has one mandatory argument, study_accessions. This function uses htx_load to prepare a SummarizedExperiment with DelayedArray assay data, with samples limited to those in the studies listed in the character vector argument study_accessions. Optional arguments to this function are passed to htx_load.

4.1.3 htx_app

htx_app has no arguments. It fires up a shiny app that lists studies by size, study accession number, and study title. User can search titles using regular expressions, and can ask for retrieval of multiple studies. The studies are returned in a SummarizedExperiment. This is for use in R. A more advanced query/retrieval app is prototyped at vjcitn.shinyapps.io/cancer9k. The cancer9k app provides a ‘search engine’-like capability over a richer collection of sample-level attributes. See the package at vjcitn/htxapp for the sources related to cancer9k.

4.2 Fundamental metadata

A number of the functions described in this subsection make use of the SRAdbV2 package managed at github.com/seandavi/SRAdbV2. If this package is not installed, some of the functions described will fail.

4.2.1 bigrnaFiles

This is a vector of length 3829708. It provides relative paths for all relevant salmon output files developed in the BigRNA project.

4.2.2 experTable, studTable

This is a data.frame with 294174 rows and 6 columns. It is a record of all SRA experiments for which metadata was retrieved via SRAdbV2 as of 28 June 2018. studTable provides study title for each experiment.

4.2.3 sampleAtts()

This function uses SRAdbV2 in real time to acquire study-level metadata component ‘sample.attributes’ for a selected SRA study accession number.

4.2.4 tx2gene_gencode27()

This function reads tx2gene.gencode.v27.csv from tximportData.

4.2.5 uniqueAcc_120518

A character vector of 186011 unique experiment accession numbers.

4.2.6 HumanTranscriptomeCompendium.colnames

A vector of 181136 strings giving the column names for the transcript-level quantifications.

4.2.7 addRD()

This utility will add a rowData component to the result of htx_load(, ..., genesOnly=TRUE ) giving the gene type, gene id, gene name, and havana gene for each row as available.

4.3 Verification tools

The production of HumanTranscriptomeCompendium has considerable complexity. There is a persistent repository of salmon outputs at

http://bigrna.cancerdatasci.org/results/human/27/*
   /aux_info/meta_info.json

where experiment accession is substitute for *. procExpToGene takes an experiment accession number and materializes the salmon quantification for the user in the form

>      str(nn)
List of 4
 $ abundance          : num [1:58288, 1] 22.8668 0.0286 32.8925 2.9392 4.1314 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:58288] "ENSG00000000003.14" "ENSG00000000005.5" "ENSG00000000419.12" "ENSG00000000457.13" ...
  .. ..$ : NULL
 $ counts             : num [1:58288, 1] 2427 2 1744 634 662 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:58288] "ENSG00000000003.14" "ENSG00000000005.5" "ENSG00000000419.12" "ENSG00000000457.13" ...
  .. ..$ : NULL
 $ length             : num [1:58288, 1] 1962 1294 980 3984 2964 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:58288] "ENSG00000000003.14" "ENSG00000000005.5" "ENSG00000000419.12" "ENSG00000000457.13" ...
  .. ..$ : NULL
 $ countsFromAbundance: chr "lengthScaledTPM"

This can be used to check the accuracy of the image of the data in HSDS.

5 Comments on motivation

Our main concern is to provide a unified and reasonable efficient access to uniformly preprocessed SRA RNA-seq studies. The quantifications are provided at an instance of the HDF Scalable Data Service. The Biocpkg("restfulSE") and Biocpkg("rhdf5client") packages allow interrogation of the service via familiar SummarizedExperiment-based programming.

The quantifications are not all that useful in the absence of accurate metadata (although large-scale unsupervised learning processes are certainly feasible without such metadata). We have therefore put a lot of effort into streamlining the acquisition and binding of sample-level metadata.

The following is a sketch of the metadata model for SRA contributions.

metadata model

“sample.attributes” can have much more (or less) information than is depicted. In fact, for some studies, a sparse setup seems to be used.

sparse attributes

We want to make it very easy to use the swagger API set up by Sean Davis for SRA metadata.

swagger look

swagger SRAdbV2

However, real-time querying may be tedious and/or fragile. Therefore we have taken snapshots of the sample.attributes components for all studies, serialized them to csv, indexed them using the parseDoc utility of ssrch, producing hashed environments to support interactive search using the selectize.js functionality. These details are worked out in ssrch package.