noiseqbio: Differential expression method for biological replicates
In NOISeq: Exploratory analysis and differential expression for RNA-seq data

Description Usage Arguments Value Author(s) References See Also Examples

noiseqbio computes differential expression between two experimental conditions from read count data (e.g. RNA-seq).

noiseqbio(input, k = 0.5, norm = c("rpkm","uqua","tmm","n"), nclust = 15, plot = FALSE,
                      factor=NULL, conditions = NULL, lc = 0, r = 50, adj = 1.5,
                      a0per = 0.9, random.seed = 12345, filter = 1, depth = NULL,
                      cv.cutoff = 500, cpm = 1)

`input`	Object of eSet class coming from `readData` function or other R packages such as DESeq.
`k`	Counts equal to 0 are replaced by k. By default, k = 0.5.
`norm`	Normalization method. It can be one of "rpkm" (default), "uqua" (upper quartile), "tmm" (trimmed mean of M) or "n" (no normalization).
`factor`	A string indicating the name of factor whose levels are the conditions to be compared.
`conditions`	A vector containing the two conditions to be compared by the differential expression algorithm (needed when the `factor` contains more than 2 different conditions).
`lc`	Length correction is done by dividing expression by length^lc. By default, lc = 0.
`r`	Number of permutations to generate noise distribution by resampling.
`adj`	Smoothing parameter for the Kernel Density Estimation of noise distribution. Higher values produce smoother curves.
`nclust`	Number of clusters for the K-means algorithm. Used when the number of replicates per condition is less than 5.
`plot`	If TRUE, a plot is generated showing the mixture distribution (f) and the noise distribution (f0) of theta values.
`a0per`	M and D values are corrected for the biological variability by being divided by S + a0, where S is the standard error of the corresponding statistic and a0 is determined by the value of a0per parameter. If a0per is NULL, a0 = 0. If a0per is a value between 0 and 1, a0 is the a0per percentile of S values for all features. If a0per = "B", a0 takes the highest value given by 100*max(S).
`random.seed`	Random seed. In order to get the same results in different runs of the method (otherwise the resampling procedure would produce different resulst), the random seed is set to this parameter value.
`filter`	Method to filter out low count features before computing differential expression analysis. If filter=0, no filtering is performed. If 1, CPM method is applied. If 2, Wilcoxon test method (not recommended when the number of replicates per condition is less than 5), If 3, proportion test method. Type `?filtered.data` for more details.
`depth`	Sequencing depth of each sample to be used by filtering method. It must be data provided when the data is already normalized and filtering method 3 is to be applied.
`cv.cutoff`	Cutoff for the coefficient of variation per condition to be used in filtering method 1.
`cpm`	Cutoff for the counts per million value to be used in filtering methods 1 and 3.

The function returns an object of class Output

Sonia Tarazona

Bullard J.H., Purdom E., Hansen K.D. and Dudoit S. (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments. BMC Bioinformatics 11(1):94+.

Mortazavi A., Williams B.A., McCue K., Schaeer L. and Wold B. (2008) Mapping and quantifying mammalian transcriptomes by RNA-seq. Nature Methods 5(7):621-628.

Robinson M.D. and Oshlack A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 11(3):R25+.

Marioni, J.C. and Mason, C.E. and Mane, S.M. and Stephens, M. and Gilad, Y. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Research, 18: 1509–1517.

readData.

## Load the input object from Marioni's data as returned by readData()
data(myCounts)

## Computing differential expression probability by NOISeqBIO using factor "Tissue" (data will be RPKM-normalized)
mynoiseqbio = noiseqbio(mydata, k = 0.5, norm = "rpkm", factor="Tissue", lc = 1, r = 50, adj = 1.5, plot = FALSE,
                        a0per = 0.9, random.seed = 12345, filter = 1, cv.cutoff = 500, cpm = 1)

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, sd, var, xtabs

The following objects are masked from ‘package:base’:

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

Welcome to Bioconductor

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

Loading required package: splines
Loading required package: Matrix
Computing Z values...
Filtering out low count features...
3842 features are to be kept for differential expression analysis with filtering method 1
[1] "r = 1"
[1] "r = 2"
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Computing probability of differential expression...
p0 = 0.0953387829844561
Probability
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.9636  0.9972  0.9063  1.0000  1.0000    1246