OVESEGtest: OVESEG-test
In OVESEG: OVESEG-test to detect tissue/cell-specific markers

Description Usage Arguments Details Value Examples

This function performs OVESEG-test to assess significance of molecular markers.

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OVESEGtest(y, group, weights = NULL, alpha = "moderated",
  NumPerm = 999, seed = 111, detail.return = TRUE,
  BPPARAM = bpparam())

`y`	a numeric matrix containing log-expression or logCPM (log2-counts per million) values. Data frame or SummarizedExperiment object will be internally coerced into a matrix. Rows correspond to probes and columns to samples. Missing values are not permitted.
`group`	categorical vector or factor giving group membership of columns of y. At least two categories need to be presented.
`weights`	optional numeric matrix containing prior weights for each spot.
`alpha`	parameter specifying within-group variance estimator to be used. 'moderated': empirical Bayes moderated variance estimator as used in `eBayes`. Numeric value: a constant value added to pooled variance estimator (α + σ). NULL: no estimator; all variances are set to be 1.
`NumPerm`	an integer specifying the number of permutation resamplings (default 999).
`seed`	an integer seed for the random number generator.
`detail.return`	a logical indicating whether more details about posterior probability estimation will be returned.
`BPPARAM`	a BiocParallelParam object indicating whether parallelization should be used for permutation resamplings. The default is bpparam().

OVESEG-test is a statistically-principled method that can detect tissue/cell-specific marker genes among many subtypes. OVESEG-test statistics are designed to mathematically match the definition of molecular markers, and a novel permutation scheme are employed to estimate the corresponding distribution under null hypotheses where the expression patterns of non-markers can be highly complex.

a list containing the following components:

`pv.overall`	a vector of p-values calculated by all permutations regardless of upregulated subtypes.
`pv.oneside`	a vector of subtype-specific p-values calculated specifically for the upregulated subtype of each probe.
`pv.oneside.max`	subtype-specific p-values when observed test statistic equal to zero.
`pv.multiside`	pv.oneside*K (K-time comparison correction) and truncated at 1.
`W`	a matrix of posterior probabilities for each component null hypothesis given an observed probe. Rows correspond to probes and columns to one hypothesis.
`label`	a vector of group labels.
`groupOrder`	a matrix with each row being group indexes ordered based on decreasing expression levels. Group indexes are positions in `label`.
`F.p.value`	a matrix with each column giving p-values corresponding to F-statistics on certain groups.
`lfdr`	a matrix with each column being local false discovery rates estimated based on one column of weighted F.p.value matrix.
`fit`	a `MArrayLM` fitted model object produced by `lmFit`.

F.p.value, lfdr and fit are returned only when detail.return is TRUE.

data(RocheBT)
rtest <- OVESEGtest(RocheBT$y, RocheBT$group, NumPerm=99,
                    BPPARAM=BiocParallel::SerialParam())
## Not run: 
#parallel computing
rtest <- OVESEGtest(RocheBT$y, RocheBT$group, NumPerm=99,
                    BPPARAM=BiocParallel::SnowParam())

## End(Not run)