Introduction

Data management is often regarded as a specialized and tedious dimension of scientific research. Because failures of data management are extremely costly in terms of resources and reputation, highly reliable and efficient methods are essential. Customary lab science practice of maintaining data in spreadsheets is regarded as risky. We want to add value to data by making it easier to follow reliable data management practices.

In Bioconductor, principles that guide software development are applied in data management strategy. High value accrues to data structures that are modular and extensible. Packaging and version control protocols apply to data class definitions. We will motivate and illustrate these ideas by giving examples of transforming spreadsheets to semantically rich objects, working with the NCBI GEO archive, dealing with families of BAM and BED files, and using external storage to foster coherent interfaces to large multiomic archives like TCGA.

Coordinating information from diverse tables, gains from integration

A demonstration package: tables from GSE5859Subset

GSE5859Subset is a package with expression data derived from a study of genetics of gene expression. Upon attachment and loading of package data, we have three data elements:

library(GSE5859Subset)
data(GSE5859Subset)
dim(geneExpression)

## [1] 8793   24

dim(geneAnnotation)

## [1] 8793    4

dim(sampleInfo)

## [1] 24  4

How are these entities (one matrix and two data frames) related?

all.equal(sampleInfo$filename, colnames(geneExpression))

## [1] TRUE

all.equal(rownames(geneExpression), geneAnnotation$PROBEID)

## [1] TRUE

Informally, we can think of sampleInfo$filename as a key for joining, row by row, the sample information table with a transposed image of the gene expression table. The colnames of the gene expression matrix link the columns of that matrix to samples enumerated in rows of sampleInfo.

Likewise, the rownames of geneExpression coincide exactly with the PROBEID field of geneAnnotation.

options(digits=2)
cbind(sampleInfo[1:3,], colnames(geneExpression)[1:3], 
    t(geneExpression)[1:3,1:4])

##     ethnicity       date         filename group
## 107       ASN 2005-06-23 GSM136508.CEL.gz     1
## 122       ASN 2005-06-27 GSM136530.CEL.gz     1
## 113       ASN 2005-06-27 GSM136517.CEL.gz     1
##     colnames(geneExpression)[1:3] 1007_s_at 1053_at 117_at 121_at
## 107              GSM136508.CEL.gz       6.5     7.5    5.4    7.9
## 122              GSM136530.CEL.gz       6.4     7.3    5.1    7.7
## 113              GSM136517.CEL.gz       6.3     7.2    5.0    7.5

Binding the tables together in an ExpressionSet

The ExpressionSet container manages all this information in one object. To improve the visibility of nomenclature for genes and samples, we improve the annotation for the individual components.

rownames(sampleInfo) = sampleInfo$filename
rownames(geneAnnotation) = geneAnnotation$PROBEID

Now we make the ExpressionSet.

library(Biobase)
es5859 = ExpressionSet(assayData=geneExpression)
pData(es5859) = sampleInfo
fData(es5859) = geneAnnotation
es5859

## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 8793 features, 24 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: GSM136508.CEL.gz GSM136530.CEL.gz ...
##     GSM136572.CEL.gz (24 total)
##   varLabels: ethnicity date filename group
##   varMetadata: labelDescription
## featureData
##   featureNames: 1007_s_at 1053_at ... AFFX-r2-P1-cre-5_at (8793
##     total)
##   fvarLabels: PROBEID CHR CHRLOC SYMBOL
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:

One of the nice things about this arrangement is that we can easily select features using higher level concepts annotated in the fData and pData components. For example to obtain expression data for genes on the Y chromosome only:

es5859[which(fData(es5859)$CHR=="chrY"),]

## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 21 features, 24 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: GSM136508.CEL.gz GSM136530.CEL.gz ...
##     GSM136572.CEL.gz (24 total)
##   varLabels: ethnicity date filename group
##   varMetadata: labelDescription
## featureData
##   featureNames: 201909_at 204409_s_at ... 211149_at (21 total)
##   fvarLabels: PROBEID CHR CHRLOC SYMBOL
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:

The full set of methods to which ExpressionSet instances respond can be seen using

methods(class="ExpressionSet")

##  [1] [                [[               [[<-             $$               
##  [5] $$<-              abstract         annotation       annotation<-    
##  [9] as.data.frame    as.matrix        assayData        assayData<-     
## [13] boxplot          classVersion     classVersion<-   coerce          
## [17] combine          description      description<-    dim             
## [21] dimnames         dimnames<-       dims             esApply         
## [25] experimentData   experimentData<- exprs            exprs<-         
## [29] fData            fData<-          featureData      featureData<-   
## [33] featureNames     featureNames<-   fvarLabels       fvarLabels<-    
## [37] fvarMetadata     fvarMetadata<-   initialize       isCurrent       
## [41] isVersioned      KEGG2heatmap     KEGGmnplot       makeDataPackage 
## [45] notes            notes<-          pData            pData<-         
## [49] phenoData        phenoData<-      preproc          preproc<-       
## [53] protocolData     protocolData<-   pubMedIds        pubMedIds<-     
## [57] rowMedians       rowQ             sampleNames      sampleNames<-   
## [61] show             storageMode      storageMode<-    updateObject    
## [65] updateObjectTo   varLabels        varLabels<-      varMetadata     
## [69] varMetadata<-    write.exprs     
## see '?methods' for accessing help and source code

The most important methods are

exprs(): get the numerical expression values
pData(): get the sample-level data
fData(): get feature-level data
annotation(): get a tag that identifies nomenclature for feature names
experimentData(): get a MIAME-compliant metadata structure

Note that many methods have setter versions, e.g., exprs<- can be used to assign expression values. Also, all components are optional. Thus our es5859 has no content for annotation or experimentData. We can improve the self-describing capacity of this object as follows. First, set the annotation field:

annotation(es5859) = "hgfocus.db" # need to look at GSE record in GEO, and know .db

Second, acquire a MIAME-compliant document of metadata about the experiment.

library(annotate)
mi = pmid2MIAME("17206142")
experimentData(es5859) = mi
es5859

## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 8793 features, 24 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: GSM136508.CEL.gz GSM136530.CEL.gz ...
##     GSM136572.CEL.gz (24 total)
##   varLabels: ethnicity date filename group
##   varMetadata: labelDescription
## featureData
##   featureNames: 1007_s_at 1053_at ... AFFX-r2-P1-cre-5_at (8793
##     total)
##   fvarLabels: PROBEID CHR CHRLOC SYMBOL
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
##   pubMedIds: 17206142 
## Annotation: hgfocus.db

Now, for example, the abstract method will function well:

nchar(abstract(es5859))

## [1] 1057

substr(abstract(es5859),1,50)

## [1] "Variation in DNA sequence contributes to individua"

A more up-to-date approach to combining these table types uses the SummarizedExperiment class, that we discuss below.

The endomorphism concept

A final remark about the ExpressionSet container design: Suppose X is an ExpressionSet. The bracket operator has been defined so that whenever G and S are suitable vectors identifying features and samples respectively, X[G, S] is an ExpressionSet with features and samples restricted to those identified in G and S. All operations that are valid for X are valid for X[G, S]. This property is called the endomorphism of ExpressionSet with respect to subsetting with bracket.

GEO, GEOquery, ArrayExpress for expression array archives

Data from all microarray experiments funded by USA National Institutes of Health should be deposited in the Gene Expression Omnibus (GEO). Bioconductor’s GEOquery package simplifies harvesting of this archive. The European Molecular Biology Laboratories sponsor ArrayExpress, which can be queried using the ArrayExpress package.

GEOmetadb

There are results of tens of thousands of experiments in GEO. The GEOmetadb includes tools to acquire and query a SQLite database with extensive annotation of GEO contents. The database retrieved in October 2017 was over 6 GB in size. Thus we do not require that you use this package. If you are interested, the vignette is very thorough. A view of the gse table is given here:

plot of chunk dogmd

getGEO: obtaining the ExpressionSet for a GEO series

We have an especial interest in the genomics of glioblastoma and have identified a paper (PMID 27746144) addressing a metabolic pathway whose manipulation may enhance treatment development strategies. Affymetrix Primeview arrays were used, with quantifications available in GEO. We use getGEO to acquire an image of these data.

library(GEOquery)
glioMA = getGEO("GSE78703")[[1]]

## Found 1 file(s)

## GSE78703_series_matrix.txt.gz

## Parsed with column specification:
## cols(
##   ID_REF = col_character(),
##   GSM2072905 = col_double(),
##   GSM2072906 = col_double(),
##   GSM2072907 = col_double(),
##   GSM2072908 = col_double(),
##   GSM2072909 = col_double(),
##   GSM2072910 = col_double(),
##   GSM2072911 = col_double(),
##   GSM2072912 = col_double(),
##   GSM2072913 = col_double(),
##   GSM2072914 = col_double(),
##   GSM2072915 = col_double(),
##   GSM2072916 = col_double()
## )

## File stored at:

## /var/folders/5_/14ld0y7s0vbg_z0g2c9l8v300000gr/T//RtmpKRbO0i/GPL15207.soft

## Warning: 102 parsing failures.
##   row     col           expected         actual         file
## 49294 SPOT_ID 1/0/T/F/TRUE/FALSE AFFX-BioB-3_at literal data
## 49295 SPOT_ID 1/0/T/F/TRUE/FALSE AFFX-BioB-5_at literal data
## 49296 SPOT_ID 1/0/T/F/TRUE/FALSE AFFX-BioB-M_at literal data
## 49297 SPOT_ID 1/0/T/F/TRUE/FALSE AFFX-BioC-3_at literal data
## 49298 SPOT_ID 1/0/T/F/TRUE/FALSE AFFX-BioC-5_at literal data
## ..... ....... .................. .............. ............
## See problems(...) for more details.

glioMA

## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 49395 features, 12 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: GSM2072905 GSM2072906 ... GSM2072916 (12 total)
##   varLabels: title geo_accession ... treated with:ch1 (35 total)
##   varMetadata: labelDescription
## featureData
##   featureNames: 11715100_at 11715101_s_at ... AFFX-TrpnX-M_at
##     (49395 total)
##   fvarLabels: ID GeneChip Array ... SPOT_ID (36 total)
##   fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
##   pubMedIds: 27746144 
## Annotation: GPL15207

In exercises we will see how to use this object to check on the assertion that treatment with LXR-623 affects expression of gene ABCA1. The associated PubMed ID is 27746144.

ArrayExpress: searching and harvesting from EMBL-EBI ArrayExpress

``The ArrayExpress Archive of Functional Genomics Data stores data from high-throughput functional genomics experiments, and provides these data for reuse to the research community.’’ Until recently ArrayExpress imported all expression data from NCBI GEO.

The ArrayExpress package supports direct interrogation of the EMBL-EBI archive, with queryAE. We’ll examine a small subset of the results.

library(ArrayExpress)
sets = queryAE(keywords = "glioblastoma", species = "homo+sapiens")
dim(sets)

## [1] 546   8

sets[5:7,-c(7,8)]

##                      ID Raw Processed ReleaseDate PubmedID      Species
## E-MTAB-6881 E-MTAB-6881  no        no  2019-04-30       NA Homo sapiens
## E-MTAB-6882 E-MTAB-6882  no        no  2019-04-29       NA Homo sapiens
## E-MTAB-7490 E-MTAB-7490 yes        no  2019-03-22       NA Homo sapiens

We see a PubMed ID for one of the experiments retrieved here, and acquire the raw data with the getAE function.

initdir = dir()
if (!file.exists("E-MTAB-5797.sdrf.txt")) nano = getAE("E-MTAB-5797")

This particular invocation will populate the working directory with files related to the experiment:

afterget = dir()
setdiff(afterget, initdir)
##  [1] "9406922003_R01C01_Grn.idat" "9406922003_R01C01_Red.idat"
##  [3] "9406922003_R02C01_Grn.idat" "9406922003_R02C01_Red.idat"
##  [5] "9406922003_R03C02_Grn.idat" "9406922003_R03C02_Red.idat"
##  [7] "9406922003_R04C02_Grn.idat" "9406922003_R04C02_Red.idat"
##  [9] "9406922003_R05C01_Grn.idat" "9406922003_R05C01_Red.idat"
## [11] "A-MEXP-2255.adf.txt"        "E-MTAB-5797.idf.txt"       
## [13] "E-MTAB-5797.raw.1.zip"      "E-MTAB-5797.sdrf.txt"

Below we will demonstrate import and inspection of this data.

SummarizedExperiment: accommodating more diverse feature concepts

In the microarray era, assay targets were determined by the content of the array in use.
Greater flexibility for targeted quantification is afforded by short read sequencing methods. Consequently, Bioconductor developers created a more flexible container for genome-scale assays. A key idea is that quantified features of interested may be identified only by genomic coordinates. It should be convenient to organize the assay values to permit interrogation using genomic coordinates only. The general method subsetByOverlaps can be used with SummarizedExperiment instances, and accomplishes this aim.

General considerations

The methods table for SummarizedExperiment is longer than that for ExpressionSet:

methods(class="SummarizedExperiment")

##   [1] !=                [                 [[               
##   [4] [[<-              [<-               %in%             
##   [7] <                 <=                ==               
##  [10] >                 >=                $$                
##  [13] $$<-               aggregate         anyDuplicated    
##  [16] anyNA             append            as.character     
##  [19] as.complex        as.data.frame     as.env           
##  [22] as.integer        as.list           as.logical       
##  [25] as.matrix         as.numeric        as.raw           
##  [28] assay             assay<-           assayNames       
##  [31] assayNames<-      assays            assays<-         
##  [34] bindROWS          by                c                
##  [37] cbind             coerce            coerce<-         
##  [40] colData           colData<-         countOverlaps    
##  [43] dim               dimnames          dimnames<-       
##  [46] duplicated        elementMetadata   elementMetadata<-
##  [49] eval              expand            expand.grid      
##  [52] extractROWS       FactorToClass     findOverlaps     
##  [55] head              intersect         is.na            
##  [58] length            lengths           match            
##  [61] mcols             mcols<-           merge            
##  [64] mergeROWS         metadata          metadata<-       
##  [67] mstack            names             names<-          
##  [70] NROW              overlapsAny       parallelSlotNames
##  [73] pcompare          rank              rbind            
##  [76] realize           relist            rename           
##  [79] rep               rep.int           replaceROWS      
##  [82] rev               rowData           rowData<-        
##  [85] ROWNAMES          rowRanges         rowRanges<-      
##  [88] selfmatch         seqlevelsInUse    setdiff          
##  [91] setequal          shiftApply        show             
##  [94] sort              split             split<-          
##  [97] subset            subsetByOverlaps  summary          
## [100] table             tail              tapply           
## [103] transform         union             unique           
## [106] updateObject      values            values<-         
## [109] window            window<-          with             
## [112] xtabs             xtfrm            
## see '?methods' for accessing help and source code

Analogs of the key ExpressionSet methods are:

assay(): get the primary numerical assay quantifications, but note that multiple assays are supported and a list of assays can be acquired using assays()
colData(): get the sample-level data
rowData(): get feature-level data, with rowRanges() applicable when features are identified primarily through genomic coordinates
metadata(): get a list that may hold any relevant metadata about the experiment

An RNA-seq experiment

We’ll use the airway package to illustrate the SummarizedExperiment concept.

library(airway)
data(airway)
airway

## class: RangedSummarizedExperiment 
## dim: 64102 8 
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample

Metadata are available in a list.

metadata(airway)

## [[1]]
## Experiment data
##   Experimenter name: Himes BE 
##   Laboratory: NA 
##   Contact information:  
##   Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. 
##   URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665 
##   PMIDs: 24926665 
## 
##   Abstract: A 226 word abstract is available. Use 'abstract' method.

The matrix of quantified features has dimensions 64102 by

The features that are quantified are exons, annotated using ENSEMBL nomenclature.

rowRanges(airway)

## GRangesList object of length 64102:
## $$ENSG00000000003
## GRanges object with 17 ranges and 2 metadata columns:
##        seqnames            ranges strand |   exon_id       exon_name
##           <Rle>         <IRanges>  <Rle> | <integer>     <character>
##    [1]        X 99883667-99884983      - |    667145 ENSE00001459322
##    [2]        X 99885756-99885863      - |    667146 ENSE00000868868
##    [3]        X 99887482-99887565      - |    667147 ENSE00000401072
##    [4]        X 99887538-99887565      - |    667148 ENSE00001849132
##    [5]        X 99888402-99888536      - |    667149 ENSE00003554016
##    ...      ...               ...    ... .       ...             ...
##   [13]        X 99890555-99890743      - |    667156 ENSE00003512331
##   [14]        X 99891188-99891686      - |    667158 ENSE00001886883
##   [15]        X 99891605-99891803      - |    667159 ENSE00001855382
##   [16]        X 99891790-99892101      - |    667160 ENSE00001863395
##   [17]        X 99894942-99894988      - |    667161 ENSE00001828996
##   -------
##   seqinfo: 722 sequences (1 circular) from an unspecified genome
## 
## ...
## <64101 more elements>

We may be accustomed to gene-level quantification in microarray studies. Here the use of exon-level quantifications necessitates special computations for gene-level summaries. For example, gene ORMDL3 has ENSEMBL identifier ENSG00000172057. The coordinates supplied in this SummarizedExperiment are

rowRanges(airway)$ENSG00000172057

## GRanges object with 20 ranges and 2 metadata columns:
##        seqnames            ranges strand |   exon_id       exon_name
##           <Rle>         <IRanges>  <Rle> | <integer>     <character>
##    [1]       17 38077294-38078938      - |    549057 ENSE00001316037
##    [2]       17 38077296-38077570      - |    549058 ENSE00002684279
##    [3]       17 38077929-38078002      - |    549059 ENSE00002697088
##    [4]       17 38078351-38078552      - |    549060 ENSE00002718599
##    [5]       17 38078530-38078938      - |    549061 ENSE00001517665
##    ...      ...               ...    ... .       ...             ...
##   [16]       17 38081876-38083094      - |    549072 ENSE00001517669
##   [17]       17 38082049-38082592      - |    549073 ENSE00002700064
##   [18]       17 38083070-38083099      - |    549074 ENSE00002688012
##   [19]       17 38083320-38083482      - |    549075 ENSE00002700595
##   [20]       17 38083737-38083854      - |    549076 ENSE00001283878
##   -------
##   seqinfo: 722 sequences (1 circular) from an unspecified genome

We will look closely at the GenomicRanges infrastructure for working with structures like this. To check for the existence of overlapping regions in this list of exon coordinates, we can use the reduce method:

reduce(rowRanges(airway)$ENSG00000172057)

## GRanges object with 8 ranges and 0 metadata columns:
##       seqnames            ranges strand
##          <Rle>         <IRanges>  <Rle>
##   [1]       17 38077294-38078938      -
##   [2]       17 38079365-38079516      -
##   [3]       17 38080283-38080478      -
##   [4]       17 38081008-38081058      -
##   [5]       17 38081422-38081624      -
##   [6]       17 38081876-38083099      -
##   [7]       17 38083320-38083482      -
##   [8]       17 38083737-38083854      -
##   -------
##   seqinfo: 722 sequences (1 circular) from an unspecified genome

This shows that projecting from the set of exons to the genome leads to 8 regions harboring subregions that may be transcribed. Details on how to summarize such counts to gene level are available elsewhere in the genomicsclass book.

In addition to detailed annotation of features, we need to manage information on samples. This occurs using the colData method. The $ operator can be used as a shortcut to get columns out of the sample data store.

names(colData(airway))

## [1] "SampleName" "cell"       "dex"        "albut"      "Run"       
## [6] "avgLength"  "Experiment" "Sample"     "BioSample"

table(airway$dex) # main treatment factor

## 
##   trt untrt 
##     4     4

Handling the ArrayExpress deposit of Illumina 450k Methylation arrays

The SummarizedExperiment class was designed for use with all kinds of array or short read sequencing data. The getAE call used above retrieved a number of files from ArrayExpress recording methylation quantification in glioblastoma tissues.

The sample level data are in the sdrf.txt file:

library(data.table)
sd5797 = fread("E-MTAB-5797.sdrf.txt")
head(sd5797[,c(3,16,18)])

##    Characteristics[age] Label                   Performer
## 1:                   58   Cy3 IntegraGen SA, Evry, France
## 2:                   58   Cy5 IntegraGen SA, Evry, France
## 3:                   72   Cy3 IntegraGen SA, Evry, France
## 4:                   72   Cy5 IntegraGen SA, Evry, France
## 5:                   70   Cy3 IntegraGen SA, Evry, France
## 6:                   70   Cy5 IntegraGen SA, Evry, France

The raw assay data are delivered in idat files. We import these using read.metharray() from the minfi package.

library(minfi)
pref = unique(substr(dir(patt="idat"),1,17)) # find the prefix strings
raw = read.metharray(pref)
raw

## class: RGChannelSet 
## dim: 622399 5 
## metadata(0):
## assays(2): Green Red
## rownames(622399): 10600313 10600322 ... 74810490 74810492
## rowData names(0):
## colnames(5): 9406922003_R01C01 9406922003_R02C01 9406922003_R03C02
##   9406922003_R04C02 9406922003_R05C01
## colData names(0):
## Annotation
##   array: IlluminaHumanMethylation450k
##   annotation: ilmn12.hg19

A number of algorithms have been proposed to transform the raw measures into biologically interpretable measures of relative methylation. Here we use a quantile normalization algorithm to transform the red and green signals to measures of relative methylation (M) and estimates of local copy number (CN) in a SummarizedExperiment instance.

glioMeth = preprocessQuantile(raw) # generate SummarizedExperiment

## [preprocessQuantile] Mapping to genome.

## [preprocessQuantile] Fixing outliers.

## [preprocessQuantile] Quantile normalizing.

glioMeth

## class: GenomicRatioSet 
## dim: 485512 5 
## metadata(0):
## assays(2): M CN
## rownames(485512): cg13869341 cg14008030 ... cg08265308 cg14273923
## rowData names(0):
## colnames(5): 9406922003_R01C01 9406922003_R02C01 9406922003_R03C02
##   9406922003_R04C02 9406922003_R05C01
## colData names(3): xMed yMed predictedSex
## Annotation
##   array: IlluminaHumanMethylation450k
##   annotation: ilmn12.hg19
## Preprocessing
##   Method: Raw (no normalization or bg correction)
##   minfi version: 1.31.1
##   Manifest version: 0.4.0

Later in the course we will work on the interpretation of the samples obtained in this study.

External storage of large assay data – HDF5Array, saveHDF5SummarizedExperiment

Measuring memory consumption

In typical interactive use, R data are fully resident in memory. We can use the gc function to get estimates of quantity of memory used in a session. Space devoted to Ncells is used to deal with language constructs such as parse trees and namespaces, while space devoted to Vcells is used to store numerical and character data loaded in the session. On my macbook air, a vanilla startup of R yields

> gc()
         used (Mb) gc trigger (Mb) max used (Mb)
Ncells 255849 13.7     460000 24.6   350000 18.7
Vcells 533064  4.1    1023718  7.9   908278  7.0

After loading the airway package:

> suppressMessages({library(airway)})
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 2772615 148.1    3886542 207.6  3205452 171.2
Vcells 2302579  17.6    3851194  29.4  3651610  27.9

After we load the airway SummarizedExperiment instance

> data(airway)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3594720 192.0    5684620 303.6  3594935 192.0
Vcells 6690732  51.1    9896076  75.6  6953991  53.1
> dim(airway)
[1] 64102     8

The memory to be managed will grow as the number of resources made available for interaction increases. The functions rm() and gc() can be used manually to reduce memory consumption but this is seldom necessary or efficient.

Demonstrating HDF5 for external storage

HDF5 is a widely used data model for numerical arrays, with interfaces defined for a wide variety of scientific programming languages. The HDF5Array package simplifies use of this system for managing large numerical arrays.

library(airway)
library(HDF5Array)  # setup for external serialization

## Loading required package: rhdf5

data(airway)
airass = assay(airway)  # obtain numerical data, then save as HDF5
href = writeHDF5Array(airass, "airass.h5", "airway")

Now when we acquire access to the same numerical data, there is no growth in memory consumption:

> gc()  # after attaching HDF5Array package
          used (Mb) gc trigger  (Mb) max used (Mb)
Ncells 1272290 68.0    2164898 115.7  1495687 79.9
Vcells 1539420 11.8    2552219  19.5  1938513 14.8
> myd = HDF5Array("airass.h5", "airway")  # get reference to data
> gc()
          used (Mb) gc trigger  (Mb) max used (Mb)
Ncells 1277344 68.3    2164898 115.7  1495687 79.9
Vcells 1543344 11.8    2552219  19.5  1938513 14.8
> dim(myd)
[1] 64102     8

This is all well and good, but it is more useful to interact with the airway data through a SummarizedExperiment container, as we will now show.

HDF5-backed SummarizedExperiment

Given a SummarizedExperiment, saveHDF5SummarizedExperiment arranges the data and metadata together, allowing control of memory consumption while preserving rich semantics of the container.

saveHDF5SummarizedExperiment(airway, "externalAirway", replace=TRUE)
newse = loadHDF5SummarizedExperiment("externalAirway")
newse

## class: RangedSummarizedExperiment 
## dim: 64102 8 
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample

assay(newse[c("ENSG00000000005", "LRG_99"), 
        which(newse$dex == "trt")]) # use familiar subsetting

## <2 x 4> DelayedMatrix object of type "integer":
##                 SRR1039509 SRR1039513 SRR1039517 SRR1039521
## ENSG00000000005          0          0          0          0
## LRG_99                   0          0          0          0

In this case the overhead of dealing with the SummarizedExperiment metadata wipes out the advantage of externalizing the assay data.

> gc() # after loading SummarizedExperiment
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 2802892 149.7    3886542 207.6  3205452 171.2
Vcells 2334204  17.9    3851194  29.4  3625311  27.7
> newse = loadHDF5SummarizedExperiment("externalAirway")
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3754945 200.6    5684620 303.6  3972131 212.2
Vcells 6669078  50.9   10201881  77.9  6962256  53.2

For very large assay arrays, the HDF5-backed SummarizedExperiment permits flexible computation with data that will not fit in available memory.

GenomicFiles: families of files of a given type

The GenomicFiles package helps to manage collections of files. This is important for data that we do not want to parse and model holistically, and do not need to import as a whole.

There are methods for rowRanges and colData for instances of the GenomicFiles class. We can coordinate metadata about the samples or experiments from which files are derived in the colData component of GenomicFiles instances, and can define genomic intervals of interest for targeted querying using the rowRanges component.

BAM collections

A small-scale illustration with RNA-seq data uses the 2013 of Zarnack and colleagues on a HNRNPC knockout experiment.
There are 8 HeLa cell samples, four of which are wild type, four of which have had RNA interference treatment to reduce expression of HNRNPC, a gene on chromosome 14.

The RNAseqData.HNRNPC.bam.chr14 package has a collection of aligned reads. The locations of the BAM files are in the vector RNAseqData.HNRNPC.bam.chr14_BAMFILES.

library(RNAseqData.HNRNPC.bam.chr14)
library(GenomicFiles)
gf = GenomicFiles(files=RNAseqData.HNRNPC.bam.chr14_BAMFILES)
gf

## GenomicFiles object with 0 ranges and 8 files: 
## files: ERR127306_chr14.bam, ERR127307_chr14.bam, ..., ERR127304_chr14.bam, ERR127305_chr14.bam 
## detail: use files(), rowRanges(), colData(), ...

This compact representation of a file set can be enhanced by binding a region of interest to the object. We’ll use the GRanges for HNRNPC:

hn = GRanges("chr14", IRanges(21677296, 21737638), strand="-")
rowRanges(gf) = hn

To extract all the alignments overlapping the region of interest, we can use the reduceByRange method of GenomicFiles. We need to define a MAP function to use this. This will be a function of two arguments, r referring to the range of interest, and f referring to the file being parsed for alignments overlapping the range. readGAlignmentPairs is used because we are dealing with paired end sequencing data.

library(GenomicAlignments)
MAP = function(r, f) 
    readGAlignmentPairs(f, param=ScanBamParam(which=r))
ali = reduceByRange(gf, MAP=MAP)

## Warning in .make_GAlignmentPairs_from_GAlignments(gal, strandMode = strandMode, :   4 alignments with ambiguous pairing were dumped.
##     Use 'getDumpedAlignments()' to retrieve them from the dump environment.

sapply(ali[[1]], length)

## ERR127306 ERR127307 ERR127308 ERR127309 ERR127302 ERR127303 ERR127304 
##      2711      3160      2946      2779        86        98       158 
## ERR127305 
##       141

This shows, informally, that there are more reads aligning to the HNRNPC region for the first four samples and thus tells us which of the samples are wild type, and which have had HNRNPC knocked down. Note that the knockdown is imperfect – there is still evidence of some transcription in cells that underwent the knockdown protocol.

BED collections

The erma is developed as a demonstration of the utility of packaging voluminous data on epigenomic assay outputs on diverse cell types for ‘out of memory’ analysis. The basic container is a simple extension of ‘GenomicFiles’ and is constructed using the makeErmaSet function:

library(erma)
erset = makeErmaSet()

## NOTE: input data had non-ASCII characters replaced by ' '.

erset

## ErmaSet object with 0 ranges and 31 files: 
## files: E002_25_imputed12marks_mnemonics.bed.gz, E003_25_imputed12marks_mnemonics.bed.gz, ..., E088_25_imputed12marks_mnemonics.bed.gz, E096_25_imputed12marks_mnemonics.bed.gz 
## detail: use files(), rowRanges(), colData(), ... 
## cellTypes() for type names; data(short_celltype) for abbr.

What are the samples managed here? The colData method gives a nice report:

colData(erset)

## (showing narrow slice of  31 x 95  DataFrame)   narrDF with 31 rows and 6 columns
##      Epigenome.ID..EID.          GROUP  Epigenome.Mnemonic     ANATOMY
##             <character>    <character>         <character> <character>
## E002               E002            ESC             ESC.WA7         ESC
## E003               E003            ESC              ESC.H1         ESC
## E021               E021           iPSC         IPSC.DF.6.9        IPSC
## E032               E032   HSC & B-cell        BLD.CD19.PPC       BLOOD
## E033               E033 Blood & T-cell         BLD.CD3.CPC       BLOOD
## ...                 ...            ...                 ...         ...
## E071               E071          Brain        BRN.HIPP.MID       BRAIN
## E072               E072          Brain         BRN.INF.TMP       BRAIN
## E073               E073          Brain BRN.DL.PRFRNTL.CRTX       BRAIN
## E088               E088          Other             LNG.FET        LUNG
## E096               E096          Other                 LNG        LUNG
##                TYPE            SEX
##         <character>    <character>
## E002 PrimaryCulture         Female
## E003 PrimaryCulture           Male
## E021 PrimaryCulture           Male
## E032    PrimaryCell           Male
## E033    PrimaryCell        Unknown
## ...             ...            ...
## E071  PrimaryTissue           Male
## E072  PrimaryTissue          Mixed
## E073  PrimaryTissue          Mixed
## E088  PrimaryTissue Female/Unknown
## E096  PrimaryTissue         Female
## use colnames() for full set of metadata attributes.

We can use familiar shortcuts to tabulate metadata about the samples.

table(erset$ANATOMY)

## 
##    BLOOD    BRAIN      ESC      FAT     IPSC    LIVER     LUNG     SKIN 
##       15        7        2        1        1        1        2        1 
## VASCULAR 
##        1

Thus we can have very lightweight interface in R (limited to metadata about the samples and file paths) to very large collections of BED files. If these are tabix-indexed we can have very fast targeted retrieval of range-specific data. This is illustrated by the stateProfile function.

stateProfile(erset[,26:31], shortCellType=FALSE)

## 'select()' returned 1:many mapping between keys and columns

## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

plot of chunk lkstpr

This depicts the variation between anatomic sites in the epigenetic state of the promoter region of gene IL33, on the plus strand of chr9. Of interest is the fact that the fetal lung sample seems to demonstrate enhancer activity in regions where the adult lung is found to have quiescent epigenetic state.

Managing information on large numbers of DNA variants

VCF background

The most common approach to handling large numbers of SNP genotypes (or small indels) is in files following the Variant Call Format (VCF files). Explanations of the format are available at Wikipedia, through the spec, and as a diagram. The basic design is that there is a header that provides metadata about the contents of the VCF file, and one record per genomic variant. The variant records describe the nature of the variant (what modifications to the reference have been identified) and include a field for each sample describing the variant configuration present. Some variants are “called”, others may be uncertain and in this case genotype likelihoods are recorded. Variants may also be phased, meaning that it is possible to locate different variant events on a given chromosome.

The VariantAnnotation package defines infrastructure for working with this format. Since there is a tabix indexing procedure for these, Rsamtools also provides relevant infrastructure.

1000 Genomes VCF in the cloud

A very salient example of DNA variant archiving is the 1000 genomes project. Bioconductor’s ldblock package includes a utility that will create references to compressed, tabix-indexed VCF files that are maintained by the 1000 genomes project in the Amazon cloud.

library(ldblock)
sta = stack1kg()
sta

## VcfStack object with 22 files and 2548 samples
## Seqinfo object with 22 sequences from an unspecified genome; no seqlengths 
## use 'readVcfStack()' to extract VariantAnnotation VCF.

Note that the genome build is tagged as ‘b37’. This is unusual but is a feature of the metadata bound to the variant data in the VCF. It cannot be changed.

We would like to add information about geographic origin of samples in this file. The ph525x package includes a table of sample information.

library(ph525x)
data(sampInfo_1kg)
rownames(sampInfo_1kg) = sampInfo_1kg[,1]
cd = sampInfo_1kg[ colnames(sta), ]
colData(sta) = DataFrame(cd)

Importing and using VCF data

We use the readVcfStack function from GenomicFiles to extract content from the VCF store. We will target the coding region of ORMDL3.

library(erma)
orm = range(genemodel("ORMDL3")) # quick lookup

## 'select()' returned 1:many mapping between keys and columns

genome(orm) = "b37"  # must agree with VCF
seqlevelsStyle(orm) = "NCBI"  # use integer chromosome codes
ormRead = readVcfStack(sta, orm)
ormRead

## class: CollapsedVCF 
## dim: 3 2548 
## rowRanges(vcf):
##   GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
## info(vcf):
##   DataFrame with 12 columns: AF, AC, NS, AN, EAS_AF, EUR_AF, AFR_AF, AM...
## info(header(vcf)):
##              Number Type    Description                                   
##    AF        A      Float   Estimated allele frequency in the range (0,1) 
##    AC        A      Integer Total number of alternate alleles in called...
##    NS        1      Integer Number of samples with data                   
##    AN        1      Integer Total number of alleles in called genotypes   
##    EAS_AF    A      Float   Allele frequency in the EAS populations cal...
##    EUR_AF    A      Float   Allele frequency in the EUR populations cal...
##    AFR_AF    A      Float   Allele frequency in the AFR populations cal...
##    AMR_AF    A      Float   Allele frequency in the AMR populations cal...
##    SAS_AF    A      Float   Allele frequency in the SAS populations cal...
##    VT        .      String  indicates what type of variant the line rep...
##    EX_TARGET 0      Flag    indicates whether a variant is within the e...
##    DP        1      Integer Approximate read depth; some reads may have...
## geno(vcf):
##   SimpleList of length 1: GT
## geno(header(vcf)):
##       Number Type   Description    
##    GT 1      String Phased Genotype

The CollapsedVCF class manages the imported genotype data. Information was retrieved on 3 variants. We can get a clearer sense of the contents by transforming a subset of the result to the VRanges representation of VariantTools.

library(VariantTools)
vr = as(ormRead[,1:5], "VRanges")
vr[1:3,]

## VRanges object with 3 ranges and 14 metadata columns:
##                     seqnames            ranges strand         ref
##                        <Rle>         <IRanges>  <Rle> <character>
##    17:38078372_T/TG       17          38078372      *           T
##   17:38078393_TGG/T       17 38078393-38078395      *         TGG
##   17:38078398_G/GGT       17          38078398      *           G
##                                  alt     totalDepth       refDepth
##                     <characterOrRle> <integerOrRle> <integerOrRle>
##    17:38078372_T/TG               TG           <NA>           <NA>
##   17:38078393_TGG/T                T           <NA>           <NA>
##   17:38078398_G/GGT              GGT           <NA>           <NA>
##                           altDepth   sampleNames softFilterMatrix |
##                     <integerOrRle> <factorOrRle>         <matrix> |
##    17:38078372_T/TG           <NA>       HG00096                  |
##   17:38078393_TGG/T           <NA>       HG00096                  |
##   17:38078398_G/GGT           <NA>       HG00096                  |
##                          QUAL            AF            AC        NS
##                     <numeric> <NumericList> <IntegerList> <integer>
##    17:38078372_T/TG      <NA>          0.01            61      2548
##   17:38078393_TGG/T      <NA>          0.04           191      2548
##   17:38078398_G/GGT      <NA>          0.05           272      2548
##                            AN        EAS_AF        EUR_AF        AFR_AF
##                     <integer> <NumericList> <NumericList> <NumericList>
##    17:38078372_T/TG      5096             0             0          0.04
##   17:38078393_TGG/T      5096             0          0.01          0.13
##   17:38078398_G/GGT      5096          0.01          0.04          0.13
##                            AMR_AF        SAS_AF              VT EX_TARGET
##                     <NumericList> <NumericList> <CharacterList> <logical>
##    17:38078372_T/TG             0             0           INDEL     FALSE
##   17:38078393_TGG/T          0.01          0.01           INDEL     FALSE
##   17:38078398_G/GGT          0.02          0.03           INDEL     FALSE
##                            DP          GT
##                     <integer> <character>
##    17:38078372_T/TG    240289         0|0
##   17:38078393_TGG/T    222931         0|0
##   17:38078398_G/GGT    218409         0|0
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
##   hardFilters: NULL

This gives a complete representation of the contents of the extraction from the VCF. To tabulate genotypes for a given individual:

table(vr[which(sampleNames(vr)=="HG00096"),]$GT)

## 
## 0|0 
##   3

In summary:

VCF files store variant information on cohorts and the VariantAnnotation package can import such files
Chromosome-specific VCF files can be stacked together in the VcfStack class
colData can be bound to VcfStack to coordinate information on sample members with their genotypes
The VRanges class from VariantTools can be used to generate metadata-rich variant-by-individual tables

Multiomics: MultiAssayExperiment, example of TCGA

The Cancer Genome Atlas is a collection of data from 14000 individuals, providing genomic information on 29 distinct tumor sites. We can download curated public data on Glioblastoma Multiforme through an effort of Levi Waldron’s lab at CUNY. R objects of the MultiAssayExperiment class have been stored in Amazon S3, and a Google Sheet provides details and links.

Retrieving data on Glioblastoma Multiforme

Here we’ll retrieve the archive on GBM. Some software technicalities necessitate use of updateObject.

library(curatedTCGAData)
gbm = curatedTCGAData("GBM", c("Mutation", "CNASNP", "RNASeq2GeneNorm",
   "Methylation_methyl27-20160128"), dry.run=FALSE)

## snapshotDate(): 2019-08-13

## see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation

## downloading 0 resources

## loading from cache

## see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation

## downloading 0 resources

## loading from cache

## see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation

## downloading 0 resources

## loading from cache

## see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation

## downloading 0 resources

## loading from cache

## see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation

## downloading 0 resources

## loading from cache

## see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation

## downloading 0 resources

## loading from cache

## harmonizing input:
##   removing 6320 sampleMap rows not in names(experiments)
##   removing 4 colData rownames not in sampleMap 'primary'

gbm

## A MultiAssayExperiment object of 3 listed
##  experiments with user-defined names and respective classes. 
##  Containing an ExperimentList class object of length 3: 
##  [1] GBM_CNASNP-20160128: RaggedExperiment with 602338 rows and 1104 columns 
##  [2] GBM_Mutation-20160128: RaggedExperiment with 22073 rows and 290 columns 
##  [3] GBM_RNASeq2GeneNorm-20160128: SummarizedExperiment with 20501 rows and 166 columns 
## Features: 
##  experiments() - obtain the ExperimentList instance 
##  colData() - the primary/phenotype DataFrame 
##  sampleMap() - the sample availability DataFrame 
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment 
##  *Format() - convert into a long or wide DataFrame 
##  assays() - convert ExperimentList to a SimpleList of matrices

Thus a single R variable can be used to work with 3 different assays measured on (subsets of) 595 individuals. Constituent assays have classes ExpressionSet, SummarizedExperiment, and RaggedExperiment. RaggedExperiment has its own package, and the vignette can be consulted for motivation.

To get a feel for the scope and completeness of the archive for GBM, we can use an UpSet diagram:

upsetSamples(gbm)

## Loading required namespace: UpSetR

plot of chunk doup

This shows that the vast majority of participants provide data on copy number variation and array-based expression, but much fewer provide RNA-seq or proteomic (RPPA, reverse-phase proteomic assay) measurements.

Working with TCGA mutation data

The mutation data illustrate a basic challenge of unified representation of heterogeneous data.

mut = experiments(gbm)[["GBM_Mutation-20160128"]]
mut

## class: RaggedExperiment 
## dim: 22073 290 
## assays(74): Hugo_Symbol Entrez_Gene_Id ... OREGANNO_ID
##   OREGANNO_Values
## rownames: NULL
## colnames(290): TCGA-02-0003-01A-01D-1490-08
##   TCGA-02-0033-01A-01D-1490-08 ... TCGA-81-5911-01A-12D-1845-08
##   TCGA-87-5896-01A-01D-1696-08
## colData names(0):

The names of the ‘assays’ of mutations are given in a vector of length 74. These are not assays in the usual sense, but characteristics or contexts of mutations that may vary between individuals.

head(assayNames(mut))

## [1] "Hugo_Symbol"            "Entrez_Gene_Id"        
## [3] "Center"                 "NCBI_Build"            
## [5] "Variant_Classification" "Variant_Type"

Mutation locations and gene associations

The mutation data includes a GRanges structure recording the genomic coordinates of mutations.

rowRanges(mut)

## GRanges object with 22073 ranges and 0 metadata columns:
##           seqnames    ranges strand
##              <Rle> <IRanges>  <Rle>
##       [1]        1   1430871      +
##       [2]        1 154148652      +
##       [3]        1 161206281      +
##       [4]        1 230846235      +
##       [5]       10 123810032      +
##       ...      ...       ...    ...
##   [22069]        X  77112989      +
##   [22070]        X 119243159      +
##   [22071]        X 131348336      +
##   [22072]        X 138643011      +
##   [22073]        X 150891145      +
##   -------
##   seqinfo: 24 sequences from 37 genome; no seqlengths

To see which genes are most frequently mutated, we can make a table:

sort(table(assay(mut, "Hugo_Symbol")), decreasing=TRUE)[1:6]

## 
## Unknown     TTN    EGFR    TP53    PTEN   MUC16 
##     126     121     102     101      93      68

Mutation types

We tabulate the kinds of variants recorded:

table(as.character(assay(mut, "Variant_Classification")))

## 
##        Frame_Shift_Del        Frame_Shift_Ins           In_Frame_Del 
##                    566                    217                    214 
##           In_Frame_Ins      Missense_Mutation      Nonsense_Mutation 
##                     28                  14213                    851 
##       Nonstop_Mutation                 Silent            Splice_Site 
##                     17                   5514                    382 
## Translation_Start_Site 
##                     71

Which genes have had deletions causing frame shifts?
We can use matrix subscripting and find the top ten genes exhibiting this event.

sort(table(assay(mut, "Hugo_Symbol")[assay(mut, "Variant_Classification")==
  "Frame_Shift_Del"]),decreasing=TRUE)[1:10]

## 
##    PTEN     NF1    ATRX     RB1   CDC27    TP53     TTN Unknown    CHD8 
##      16      10       7       7       6       6       4       4       3 
##    OGDH 
##       3

In summary

MultiAssayExperiment unifies diverse assays collected on a cohort of individuals
TCGA tumor-specific datasets have been serialized as MultiAssayExperiments for public use from Amazon S3
Idiosyncratic data with complex annotation can be managed in RaggedAssay structures; these have proven useful for mutation and copy number variants

Cloud-oriented management strategies: CGC and GDC concepts

In the previous section we indicated how the TCGA data on a tumor can be collected in a coherent object that unifies numerous genomic assays. In this section we want to illustrate how the complete data from the TCGA can be accessed for interactive analysis through a single R connection object. To execute the commands in this section, you will need to be able to authenticate to the Cancer Genomics Cloud as managed by the Institute for Systems Biology. Contact the [ISB project administrators] (https://www.systemsbiology.org/research/cancer-genomics-cloud/) to obtain an account.

In the following code, we create a DBI connection to Google BigQuery, using the bigrquery package maintained by the r-db-stats group. We then list the available tables.

library(bigrquery)
library(dplyr)
library(magrittr)
tcgaCon = DBI::dbConnect(dbi_driver(), project="isb-cgc", 
     dataset="TCGA_hg38_data_v0", billing = Sys.getenv("CGC_BILLING")) 
dbListTables(tcgaCon)

##  [1] "Copy_Number_Segment_Masked"  "DNA_Methylation"            
##  [3] "DNA_Methylation_chr1"        "DNA_Methylation_chr10"      
##  [5] "DNA_Methylation_chr11"       "DNA_Methylation_chr12"      
##  [7] "DNA_Methylation_chr13"       "DNA_Methylation_chr14"      
##  [9] "DNA_Methylation_chr15"       "DNA_Methylation_chr16"      
## [11] "DNA_Methylation_chr17"       "DNA_Methylation_chr18"      
## [13] "DNA_Methylation_chr19"       "DNA_Methylation_chr2"       
## [15] "DNA_Methylation_chr20"       "DNA_Methylation_chr21"      
## [17] "DNA_Methylation_chr22"       "DNA_Methylation_chr3"       
## [19] "DNA_Methylation_chr4"        "DNA_Methylation_chr5"       
## [21] "DNA_Methylation_chr6"        "DNA_Methylation_chr7"       
## [23] "DNA_Methylation_chr8"        "DNA_Methylation_chr9"       
## [25] "DNA_Methylation_chrX"        "DNA_Methylation_chrY"       
## [27] "Protein_Expression"          "RNAseq_Gene_Expression"     
## [29] "Somatic_Mutation"            "Somatic_Mutation_Jun2017"   
## [31] "miRNAseq_Expression"         "miRNAseq_Isoform_Expression"

Now we use the dplyr approach to requesting a summary of mutation types in GBM.

tcgaCon %>% tbl("Somatic_Mutation") %>% dplyr::filter(project_short_name=="TCGA-GBM") %>% 
      dplyr::select(Variant_Classification, Hugo_Symbol) %>% group_by(Variant_Classification) %>%
      summarise(n=n())

## # Source:   lazy query [?? x 2]
## # Database: BigQueryConnection
##    Variant_Classification     n
##                     <chr> <int>
##  1                  3'UTR  4166
##  2                 Intron  3451
##  3                3'Flank   423
##  4       Nonstop_Mutation    58
##  5                    IGR     3
##  6                    RNA  1338
##  7            Splice_Site   955
##  8        Frame_Shift_Del  1487
##  9      Missense_Mutation 53054
## 10 Translation_Start_Site    60
## # ... with more rows

This is essentially a proof of concept that the entire TCGA archive can be interrogated through operations on a single R variable, in this case tcgaCon. The underlying infrastructure is Google-specific, but the basic idea should replicate well in other environments.

Can we envision a SummarizedExperiment-like interface to this data store? The answer is yes; see the seByTumor function in the shwetagopaul92/restfulSE package on github. (This package is under evaluation for Bioconductor but may always be acquired through github.)

This concludes the discussion of the Cancer Genomics Cloud pilot instance at Institute for Systems Biology. Other Cloud pilot projects were created at Seven Bridges Genomics and Broad Institute. There is considerable effort underway to federate large collections of general genomics data in a cloud-oriented framework that would diminish the need for genomics data transfer. This RFA is very informative about what is at stake.

Overall summary

We have reviewed basic concepts of genome-scale data managment for several experimental paradigms:

array-based gene expression measures
array-based measurement of DNA methylation
RNA-seq alignments and gene expression measures derived from these
genome-wide genotyping
tumor mutation assessment

Data sources include:

local files
institutional archives (NCBI GEO, EMBL ArrayExpress)
high-performance external data stores (HDF5)
cloud-resident distributed data stores (Google BigQuery or Amazon S3)

Critical managerial principles include:

unification of assay, sample characteristics, and experiment metadata in formal object classes (ExpressionSet, SummarizedExperiment)
amalgamation of multiple coordinated assay sets when applied to overlapping subsets of a cohort (MultiAssayExperiment)
endomorphic character of objects under feature- or sample-level filtering
amenability to filtering by genomic coordinates, using GRanges as queries
support for consistency checking by labeling object components with key provenance information such as reference genome build

This is a time of great innovation in the domains of molecular assay technology and data storage and retrieval. Benchmarking and support for decisionmaking on choice of strategy are high-value processes that are hard to come by. This chapter has acquainted you with a spectrum of concepts and solutions, many of which will be applied in analytical examples to follow in the course.

Management of genome-scale data (2019, Bioc 3.9)