AlvMac_results.pVal {GOexpress} | R Documentation

## Sample output from the `pValue_GO()` function on an RNAseq experiment.

### Description

This variable may be used to test the filtering and visualisation methods implemented in the package. It contains the output of successively applying the commands `AlvMac_results = GO_analyse(eSet=AlvMac, f="Treatment")` and `AlvMac_results.pVal = pValue_GO(AlvMac_results, N=100, ranked.by=result$rank.by, rank.by='P')` to the toy input data `AlvMac`.

### Usage

    data("AlvMac_results.pVal")

### Value

A list of 9 slots summarising the input and results of the analysis:

- `GO` contains a table ranking all GO terms related to genes in the expression dataset based on the average ability of their related genes to cluster the samples according to the predefined grouping factor.

- `mapping` contains the table mapping genes present in the dataset to GO terms.

- `genes` contains a table ranking all genes present in the expression dataset based on their ability to cluster the samples according to the predefined grouping factor (see 'factor' below).

- `factor` contains the grouping factor analysed.

- `method` contains the statistical framework used.

- `subset` contains the filters used to select a subset of samples from the original `ExpressionSet` for analysis.

- `rank.by` contains the metric used to rank the scoring tables.

- `ntree` contains number of trees built during the randomForest analysis.

- `mtry` contains the number of features randomly sampled as candidates at each split in each tree built during the randomForest analysis.

- `p.iterations` contains the number of permutations performed to compute the P-value in the `GO` slot.

### Warning

Running the above command again, you might obtain slightly different scores and ranks due to the stochastic process of sampling used by the random forest algorithm. However, the ranking metric was found to be robust and stable across run, given adequate number of trees and predictor variables sampled.

To produce reproducible results, use the `set.seed()` function prior to running any randomising or sampling function.

### Source

Source data are part of a publication in review.

### Examples

    data(AlvMac_results.pVal)
    str(AlvMac_results.pVal)
    head(AlvMac_results.pVal, n=20)

[Package GOexpress version 1.28.0 Index]