A container for storing information used in TCC

Description

This is a container class for TCC. This class initially contains count data matrix and some information for the analysis of count data. It also provides further fields that are populated during the analysis.

Details

This class is implemented as an R5 reference class. Functions calling such methods copies the object prior to calling the method to keep the semantics of functional programming. This class can be created by the generic new function with count data and associated information of experimental design.

The values (defaults to all 1) in the norm.factors field will be changed after performing the calcNormFactors function. The DEGES field stores information related to our DEGES-based normalization pipeline after performing the calcNormFactors function. The stat and estimatedDEG fields store results after performing the estimateDE function. The simulation field stores parameters used when performing the simulateReadCounts function.

Fields

This class contains the following fields:

count

numeric matrix containing count data.

gene_id

character vector indicating the identifier of the count unit, usually gene.

group

data frame indicating information about experimental design.

norm.factors

numeric vector containing normalization factors (default to 1).

stat

list for storing results after the execution of the calcNormFactors and estimateDE functions.

estimatedDEG

numeric vector as a placeholder for indicating either DEGs (flagged as "1") or non-DEGs (as "0") for individual genes. The values in this field will be populated after the execution of the estimateDE function.

simulation

list. This field is only used for analyzing simulation data.

DEGES

list for storing the information about normalization steps.

Examples

tcc <- simulateReadCounts(Ngene = 10000, PDEG = 0.2, DEG.assign = c(0.8, 0.2),
                          DEG.foldchange = c(4, 4), replicates = c(3, 3))

# Check the TCC class object.
tcc

# Check the fields of TCC class object.
names(tcc)
head(tcc$count)

# Check the normalization factors.
tcc <- calcNormFactors(tcc, norm.method = "tmm", test.method = "edger",
                       iteration = 1, FDR = 0.1, floorPDEG = 0.05)
tcc$norm.factors

# Check the p-values and q-values.
tcc <- estimateDE(tcc, test.method = "edger", FDR = 0.1)
tcc

# Compare the breakdowns of estimated DEGs with the truth.
head(tcc$estimatedDEG)
head(tcc$simulation$trueDEG)

# M-A plotting.
plot(tcc)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(TCC)
Loading required package: DESeq
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

Loading required package: Biobase
Welcome to Bioconductor

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

Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
Loading required package: lattice
    Welcome to 'DESeq'. For improved performance, usability and
    functionality, please consider migrating to 'DESeq2'.
Loading required package: DESeq2
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

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

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment

Attaching package: 'DESeq2'

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

    estimateSizeFactorsForMatrix, getVarianceStabilizedData,
    varianceStabilizingTransformation

Loading required package: edgeR
Loading required package: limma

Attaching package: 'limma'

The following object is masked from 'package:DESeq2':

    plotMA

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

    plotMA

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

    plotMA

Loading required package: baySeq
Loading required package: abind
Loading required package: perm
Loading required package: ROC

Attaching package: 'TCC'

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

    calcNormFactors

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/TCC/TCC-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: TCC-class
> ### Title: A container for storing information used in TCC
> ### Aliases: TCC-class show show.TCC show,TCC-method names names,TCC-method
> ###   length length,TCC-method [ [,TCC,ANY-method [,TCC-method
> ###   [,TCC,ANY,ANY-method [,TCC,ANY,ANY,ANY-method subset
> ###   subset,TCC-method
> ### Keywords: classes
> 
> ### ** Examples
> 
> tcc <- simulateReadCounts(Ngene = 10000, PDEG = 0.2, DEG.assign = c(0.8, 0.2),
+                           DEG.foldchange = c(4, 4), replicates = c(3, 3))
TCC::INFO: Generating simulation data under NB distribution ...
TCC::INFO: (genesizes   :  10000 )
TCC::INFO: (replicates  :  3, 3 )
TCC::INFO: (PDEG        :  0.16, 0.04 )
> 
> # Check the TCC class object.
> tcc
Count:
       G1_rep1 G1_rep2 G1_rep3 G2_rep1 G2_rep2 G2_rep3
gene_1     480     323     603     146     119     112
gene_2     126     114     125      41      32      59
gene_3      12       3       9       0       2       1
gene_4     575     593    1302     222      64     307
gene_5      52      54      41      11       5       6
gene_6     213     264     400      90     107      63

Sample:
        group norm.factors lib.sizes
G1_rep1     1            1   1286214
G1_rep2     1            1   1288977
G1_rep3     1            1   1358796
G2_rep1     2            1   1052820
G2_rep2     2            1   1100227
G2_rep3     2            1   1039522

> 
> # Check the fields of TCC class object.
> names(tcc)
[1] "count"        "gene_id"      "group"        "norm.factors" "DEGES"       
[6] "stat"         "estimatedDEG" "simulation"  
> head(tcc$count)
       G1_rep1 G1_rep2 G1_rep3 G2_rep1 G2_rep2 G2_rep3
gene_1     480     323     603     146     119     112
gene_2     126     114     125      41      32      59
gene_3      12       3       9       0       2       1
gene_4     575     593    1302     222      64     307
gene_5      52      54      41      11       5       6
gene_6     213     264     400      90     107      63
> 
> # Check the normalization factors.
> tcc <- calcNormFactors(tcc, norm.method = "tmm", test.method = "edger",
+                        iteration = 1, FDR = 0.1, floorPDEG = 0.05)
TCC::INFO: Calculating normalization factors using DEGES
TCC::INFO: (iDEGES pipeline : tmm - [ edger - tmm ] X 1 )
TCC::INFO: Done.
> tcc$norm.factors
  G1_rep1   G1_rep2   G1_rep3   G2_rep1   G2_rep2   G2_rep3 
0.9236479 0.9177329 0.8724595 1.1052712 1.0578330 1.1230555 
> 
> # Check the p-values and q-values.
> tcc <- estimateDE(tcc, test.method = "edger", FDR = 0.1)
TCC::INFO: Identifying DE genes using edger ...
TCC::INFO: Done.
> tcc
Count:
       G1_rep1 G1_rep2 G1_rep3 G2_rep1 G2_rep2 G2_rep3
gene_1     480     323     603     146     119     112
gene_2     126     114     125      41      32      59
gene_3      12       3       9       0       2       1
gene_4     575     593    1302     222      64     307
gene_5      52      54      41      11       5       6
gene_6     213     264     400      90     107      63

Sample:
        group norm.factors lib.sizes
G1_rep1     1    0.9236479   1188009
G1_rep2     1    0.9177329   1182937
G1_rep3     1    0.8724595   1185494
G2_rep1     2    1.1052712   1163652
G2_rep2     2    1.0578330   1163856
G2_rep3     2    1.1230555   1167441

DEGES:
   Pipeline       : tmm - [ edger - tmm ] X 1
   Execution time : 2.5 sec
   Threshold type : FDR < 0.10
   Potential PDEG : 0.13

Results:
  gene_id  a.value   m.value      p.value      q.value rank estimatedDEG
1  gene_1 7.922910 -1.873277 5.316356e-06 0.0001158247  459            1
2  gene_2 6.192864 -1.442605 1.133242e-03 0.0114584632  989            1
3  gene_3 1.499437 -2.973764 3.388453e-02 0.2163763378 1566            0
4  gene_4 8.655792 -2.034069 2.570919e-04 0.0030789451  835            1
5  gene_5 4.244826 -2.714812 5.345076e-05 0.0007825880  683            1
6  gene_6 7.314788 -1.728673 6.260202e-05 0.0008981638  697            1

> 
> # Compare the breakdowns of estimated DEGs with the truth.
> head(tcc$estimatedDEG)
[1] 1 1 0 1 1 1
> head(tcc$simulation$trueDEG)
gene_1 gene_2 gene_3 gene_4 gene_5 gene_6 
     1      1      1      1      1      1 
> 
> # M-A plotting.
> plot(tcc)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>