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Last data update: 2014.03.03 |
Obtain normalized count dataDescriptionThis function generates normalized count data from both original count data and calculated normalization factors. UsagegetNormalizedData(tcc) Arguments
DetailsThis function is generally used after the ValueA numeric matrix containing normalized count data. Examples
# Note that the hypoData has non-DEGs at 201-1000th rows.
nonDEG <- 201:1000
data(hypoData)
summary(hypoData[nonDEG, ])
group <- c(1, 1, 1, 2, 2, 2)
# Obtaining normalized count data after performing the
# DEGES/edgeR normalization method, i.e., DEGES/edgeR-normalized data.
tcc <- new("TCC", hypoData, group)
tcc <- calcNormFactors(tcc, norm.method = "tmm", test.method = "edger",
iteration = 1, FDR = 0.1, floorPDEG = 0.05)
normalized.count <- getNormalizedData(tcc)
summary(normalized.count[nonDEG, ])
# Obtaining normalized count data after performing the TMM normalization
# method (Robinson and Oshlack, 2010), i.e., TMM-normalized data.
tcc <- new("TCC", hypoData, group)
tcc <- calcNormFactors(tcc, norm.method = "tmm", iteration = 0)
normalized.count <- getNormalizedData(tcc)
summary(normalized.count[nonDEG, ])
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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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/getNormalizedData.Rd_%03d_medium.png", width=480, height=480)
> ### Name: getNormalizedData
> ### Title: Obtain normalized count data
> ### Aliases: getNormalizedData
> ### Keywords: methods
>
> ### ** Examples
>
> # Note that the hypoData has non-DEGs at 201-1000th rows.
> nonDEG <- 201:1000
> data(hypoData)
> summary(hypoData[nonDEG, ])
G1_rep1 G1_rep2 G1_rep3 G2_rep1
Min. : 0.00 Min. : 0 Min. : 0.00 Min. : 0.0
1st Qu.: 3.00 1st Qu.: 4 1st Qu.: 3.00 1st Qu.: 3.0
Median : 20.50 Median : 20 Median : 20.00 Median : 21.0
Mean : 103.36 Mean : 105 Mean : 104.45 Mean : 113.8
3rd Qu.: 74.25 3rd Qu.: 68 3rd Qu.: 73.25 3rd Qu.: 68.0
Max. :8815.00 Max. :9548 Max. :8810.00 Max. :9304.0
G2_rep2 G2_rep3
Min. : 0 Min. : 0.0
1st Qu.: 3 1st Qu.: 3.0
Median : 21 Median : 20.0
Mean : 105 Mean : 104.6
3rd Qu.: 70 3rd Qu.: 70.0
Max. :9466 Max. :9320.0
> group <- c(1, 1, 1, 2, 2, 2)
>
> # Obtaining normalized count data after performing the
> # DEGES/edgeR normalization method, i.e., DEGES/edgeR-normalized data.
> tcc <- new("TCC", hypoData, group)
> 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.
> normalized.count <- getNormalizedData(tcc)
> summary(normalized.count[nonDEG, ])
G1_rep1 G1_rep2 G1_rep3 G2_rep1
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 2.961 1st Qu.: 4.008 1st Qu.: 2.924 1st Qu.: 3.042
Median : 20.235 Median : 20.042 Median : 19.491 Median : 21.292
Mean : 102.027 Mean : 105.178 Mean : 101.796 Mean : 115.390
3rd Qu.: 73.290 3rd Qu.: 68.141 3rd Qu.: 71.387 3rd Qu.: 68.946
Max. :8701.005 Max. :9567.851 Max. :8585.900 Max. :9433.395
G2_rep2 G2_rep3
Min. : 0.000 Min. : 0.000
1st Qu.: 3.074 1st Qu.: 2.998
Median : 21.517 Median : 19.987
Mean : 107.564 Mean : 104.539
3rd Qu.: 71.724 3rd Qu.: 69.956
Max. :9699.190 Max. :9314.119
>
> # Obtaining normalized count data after performing the TMM normalization
> # method (Robinson and Oshlack, 2010), i.e., TMM-normalized data.
> tcc <- new("TCC", hypoData, group)
> tcc <- calcNormFactors(tcc, norm.method = "tmm", iteration = 0)
TCC::INFO: Calculating normalization factors using tmm ...
TCC::INFO: Done.
> normalized.count <- getNormalizedData(tcc)
> summary(normalized.count[nonDEG, ])
G1_rep1 G1_rep2 G1_rep3 G2_rep1
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 2.833 1st Qu.: 3.802 1st Qu.: 2.789 1st Qu.: 3.284
Median : 19.359 Median : 19.011 Median : 18.591 Median : 22.986
Mean : 97.610 Mean : 99.769 Mean : 97.092 Mean : 124.570
3rd Qu.: 70.117 3rd Qu.: 64.637 3rd Qu.: 68.088 3rd Qu.: 74.431
Max. :8324.342 Max. :9075.747 Max. :8189.160 Max. :10183.853
G2_rep2 G2_rep3
Min. : 0.000 Min. : 0.000
1st Qu.: 3.166 1st Qu.: 3.151
Median : 22.163 Median : 21.007
Mean : 110.790 Mean : 109.871
3rd Qu.: 73.876 3rd Qu.: 73.524
Max. :9990.114 Max. :9789.191
>
>
>
>
>
> dev.off()
null device
1
>
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