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Last data update: 2014.03.03

R: Convert to other classes

convertTo

R Documentation

Convert to other classes

Description

Convert a SCESet object into other classes for entry into other analysis pipelines.

Usage

## S4 method for signature 'SCESet'
convertTo(x, type=c("edgeR", "DESeq2", "monocle"),
    fData.col=NULL, pData.col=NULL, ..., 
    assay, normalize=TRUE, get.spikes=FALSE)

Arguments

`x`	A SCESet object.
`type`	A string specifying the analysis for which the object should be prepared.
`fData.col`	Any set of indices specifying which columns of `fData(x)` should be retained in the returned object.
`pData.col`	Any set of indices specifying which columns of `pData(x)` should be retained.
`...`	Other arguments to be passed to pipeline-specific constructors.
`assay`	A string specifying which assay of `x` should be put in the returned object.
`normalize`	A logical scalar specifying whether the assay values should be normalized for `type="monocle"`.
`get.spikes`	A logical scalar specifying whether rows corresponding to spike-in transcripts should be returned.

Details

This function converts a SCESet into various other classes in preparation for entry into other analysis pipelines, as specified by type. Gene- and cell-specific data fields can be retained in the output object by setting fData.col and pData.col, respectively. Other arguments can be passed to the relevant constructors through the ellipsis.

By default, for edgeR and DESeq2, assay is set to "counts" such that count data is stored in the output object. This is consistent with the required inputs to these analysis pipeline (normalization information is stored through size factors). For monocle, counts are divided by the size factors to yield (roughly) log-normally distributed expression values. This can be turned off (i.e., to use the raw values in assay) by setting normalize=FALSE.

In all cases, rows corresponding to spike-in transcripts are removed from the output object by default. As such, rows in the returned object may not correspond directly to rows in x. Users should consider this when retrieving analysis results from these pipelines, e.g., match on row names. This behaviour can be turned off by setting get.spikes=TRUE, such that all rows are retrieved in the output object.

Value

For type="edgeR", a DGEList object is returned containing the count matrix. Size factors are converted to normalization factors. Gene-specific fData is stored in the genes element, and cell-specific pData is stored in the samples element.

For type="DESeq2", a DESeqDataSet object is returned containing the count matrix and size factors. Additional gene- and cell-specific data is stored in the mcols and colData respectively.

For type="monocle", a CellDataSet object is returned containing the unlogged expression values. Additional gene- and cell-specific data is stored in the fData and pData respectively.

Author(s)

Aaron Lun

Examples

ncells <- 200
ngenes <- 100
count.sizes <- rnbinom(ncells, mu=100, size=5)
dummy <- matrix(count.sizes, ncol=ncells, nrow=ngenes, byrow=TRUE)
rownames(dummy) <- paste0("X", seq_len(ngenes))

X <- newSCESet(countData=data.frame(dummy))
is.spike <- rbinom(ngenes, 1, 0.5)==0L
isSpike(X) <- is.spike
sizeFactors(X) <- 2^rnorm(ncells)
X <- normalize(X)

fData(X)$SYMBOL <- paste0("X", seq_len(ngenes))
X$other <- sample(LETTERS, ncells, replace=TRUE)

convertTo(X, type="edgeR")
convertTo(X, type="DESeq2")
convertTo(X, type="monocle")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(scran)
Loading required package: BiocParallel
Loading required package: scater
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: ggplot2

Attaching package: 'scater'

The following object is masked from 'package:stats':

    filter

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/scran/convertTo.Rd_%03d_medium.png", width=480, height=480)
> ### Name: convertTo
> ### Title: Convert to other classes
> ### Aliases: convertTo convertTo,SCESet-method
> 
> ### ** Examples
> 
> ncells <- 200
> ngenes <- 100
> count.sizes <- rnbinom(ncells, mu=100, size=5)
> dummy <- matrix(count.sizes, ncol=ncells, nrow=ngenes, byrow=TRUE)
> rownames(dummy) <- paste0("X", seq_len(ngenes))
> 
> X <- newSCESet(countData=data.frame(dummy))
> is.spike <- rbinom(ngenes, 1, 0.5)==0L
> isSpike(X) <- is.spike
> sizeFactors(X) <- 2^rnorm(ncells)
> X <- normalize(X)
> 
> fData(X)$SYMBOL <- paste0("X", seq_len(ngenes))
> X$other <- sample(LETTERS, ncells, replace=TRUE)
> 
> convertTo(X, type="edgeR")
An object of class "DGEList"
$counts
    X1 X2 X3  X4  X5 X6  X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20
X2  40 76 65 129 106 74 166 37 58 123  42 136 115 290  89  35 116  52 114  87
X3  40 76 65 129 106 74 166 37 58 123  42 136 115 290  89  35 116  52 114  87
X4  40 76 65 129 106 74 166 37 58 123  42 136 115 290  89  35 116  52 114  87
X11 40 76 65 129 106 74 166 37 58 123  42 136 115 290  89  35 116  52 114  87
X12 40 76 65 129 106 74 166 37 58 123  42 136 115 290  89  35 116  52 114  87
    X21 X22 X23 X24 X25 X26 X27 X28 X29 X30 X31 X32 X33 X34 X35 X36 X37 X38 X39
X2   90 130 130 224 108 159 119  55 126  36 123  56 202  96 100  43  88 110  80
X3   90 130 130 224 108 159 119  55 126  36 123  56 202  96 100  43  88 110  80
X4   90 130 130 224 108 159 119  55 126  36 123  56 202  96 100  43  88 110  80
X11  90 130 130 224 108 159 119  55 126  36 123  56 202  96 100  43  88 110  80
X12  90 130 130 224 108 159 119  55 126  36 123  56 202  96 100  43  88 110  80
    X40 X41 X42 X43 X44 X45 X46 X47 X48 X49 X50 X51 X52 X53 X54 X55 X56 X57 X58
X2   73 267  59 112 128 102  47  86  98 156 119 118  62  68 179  58  36  82  30
X3   73 267  59 112 128 102  47  86  98 156 119 118  62  68 179  58  36  82  30
X4   73 267  59 112 128 102  47  86  98 156 119 118  62  68 179  58  36  82  30
X11  73 267  59 112 128 102  47  86  98 156 119 118  62  68 179  58  36  82  30
X12  73 267  59 112 128 102  47  86  98 156 119 118  62  68 179  58  36  82  30
    X59 X60 X61 X62 X63 X64 X65 X66 X67 X68 X69 X70 X71 X72 X73 X74 X75 X76 X77
X2  177 144  52  99 129 121 101  81 149 129  78 132  81 123  70  93  99 105  67
X3  177 144  52  99 129 121 101  81 149 129  78 132  81 123  70  93  99 105  67
X4  177 144  52  99 129 121 101  81 149 129  78 132  81 123  70  93  99 105  67
X11 177 144  52  99 129 121 101  81 149 129  78 132  81 123  70  93  99 105  67
X12 177 144  52  99 129 121 101  81 149 129  78 132  81 123  70  93  99 105  67
    X78 X79 X80 X81 X82 X83 X84 X85 X86 X87 X88 X89 X90 X91 X92 X93 X94 X95 X96
X2   97  95  58  34 212  52 155  69  79  80 273  44  70  52 187 115 110  40 115
X3   97  95  58  34 212  52 155  69  79  80 273  44  70  52 187 115 110  40 115
X4   97  95  58  34 212  52 155  69  79  80 273  44  70  52 187 115 110  40 115
X11  97  95  58  34 212  52 155  69  79  80 273  44  70  52 187 115 110  40 115
X12  97  95  58  34 212  52 155  69  79  80 273  44  70  52 187 115 110  40 115
    X97 X98 X99 X100 X101 X102 X103 X104 X105 X106 X107 X108 X109 X110 X111
X2   81  70 107  157  103   93   20  122  106   96   40   76   82  148   81
X3   81  70 107  157  103   93   20  122  106   96   40   76   82  148   81
X4   81  70 107  157  103   93   20  122  106   96   40   76   82  148   81
X11  81  70 107  157  103   93   20  122  106   96   40   76   82  148   81
X12  81  70 107  157  103   93   20  122  106   96   40   76   82  148   81
    X112 X113 X114 X115 X116 X117 X118 X119 X120 X121 X122 X123 X124 X125 X126
X2   136  102   78   47   50   75   96   24  126  188   94  119   81  138  105
X3   136  102   78   47   50   75   96   24  126  188   94  119   81  138  105
X4   136  102   78   47   50   75   96   24  126  188   94  119   81  138  105
X11  136  102   78   47   50   75   96   24  126  188   94  119   81  138  105
X12  136  102   78   47   50   75   96   24  126  188   94  119   81  138  105
    X127 X128 X129 X130 X131 X132 X133 X134 X135 X136 X137 X138 X139 X140 X141
X2    42  120   89  143   90   91  162   41  106   80  175   87   76   76   46
X3    42  120   89  143   90   91  162   41  106   80  175   87   76   76   46
X4    42  120   89  143   90   91  162   41  106   80  175   87   76   76   46
X11   42  120   89  143   90   91  162   41  106   80  175   87   76   76   46
X12   42  120   89  143   90   91  162   41  106   80  175   87   76   76   46
    X142 X143 X144 X145 X146 X147 X148 X149 X150 X151 X152 X153 X154 X155 X156
X2    89   80   41   88   82   86   86   78  205  112  150   89  157   62  111
X3    89   80   41   88   82   86   86   78  205  112  150   89  157   62  111
X4    89   80   41   88   82   86   86   78  205  112  150   89  157   62  111
X11   89   80   41   88   82   86   86   78  205  112  150   89  157   62  111
X12   89   80   41   88   82   86   86   78  205  112  150   89  157   62  111
    X157 X158 X159 X160 X161 X162 X163 X164 X165 X166 X167 X168 X169 X170 X171
X2   151   24  117   81  122   50   36  154   46  103   48   43   93   74   46
X3   151   24  117   81  122   50   36  154   46  103   48   43   93   74   46
X4   151   24  117   81  122   50   36  154   46  103   48   43   93   74   46
X11  151   24  117   81  122   50   36  154   46  103   48   43   93   74   46
X12  151   24  117   81  122   50   36  154   46  103   48   43   93   74   46
    X172 X173 X174 X175 X176 X177 X178 X179 X180 X181 X182 X183 X184 X185 X186
X2    53   84   81  124   88  125   57  167  106  124  125   50  126   97   60
X3    53   84   81  124   88  125   57  167  106  124  125   50  126   97   60
X4    53   84   81  124   88  125   57  167  106  124  125   50  126   97   60
X11   53   84   81  124   88  125   57  167  106  124  125   50  126   97   60
X12   53   84   81  124   88  125   57  167  106  124  125   50  126   97   60
    X187 X188 X189 X190 X191 X192 X193 X194 X195 X196 X197 X198 X199 X200
X2    24   47  115  117  118   56  111  120   82  164  124   78  141   74
X3    24   47  115  117  118   56  111  120   82  164  124   78  141   74
X4    24   47  115  117  118   56  111  120   82  164  124   78  141   74
X11   24   47  115  117  118   56  111  120   82  164  124   78  141   74
X12   24   47  115  117  118   56  111  120   82  164  124   78  141   74
45 more rows ...

$samples
   group lib.size norm.factors
X1     1     4000    1.3667917
X2     1     7600    1.9821343
X3     1     6500    0.2451696
X4     1    12900    0.5690732
X5     1    10600    0.3700974
195 more rows ...

> convertTo(X, type="DESeq2")
converting counts to integer mode
class: DESeqDataSet 
dim: 50 200 
metadata(1): version
assays(1): counts
rownames(50): X2 X3 ... X97 X98
rowData names(0):
colnames(200): X1 X2 ... X199 X200
colData names(1): sizeFactor
> convertTo(X, type="monocle")
No methods found in "BiocGenerics" for requests: as.vector, unlist
CellDataSet (storageMode: environment)
assayData: 50 features, 200 samples 
  element names: exprs 
protocolData: none
phenoData: none
featureData
  featureNames: X2 X3 ... X98 (50 total)
  fvarLabels: use_for_ordering
  fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
Annotation:  
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>