SemDist (version 1.2.0)

findRUMI: Information accretion based predictor assessment

Description

Reads in a file containing the true terms annotating a set of sequences and a file containing the predicted terms and scores for a set of sequences and outputs a data frame containing the remaining uncertainty and misinformation values for the predictions made for each sequence.

Usage

findRUMI(ont, organism, threshold = 0.05, truefile="", predfile = "", IAccr = NULL)

Arguments

ont

Character representation of ontology version to use. One of "CC", "MF", or "BP" , corresponding to Cellular Component, Molecular Function, and Biological Process.

organism

A character vector indicating which organism(s) annotation data to use.

threshold

Score above which a predicted annotation should be included in the calculation. Must be a numeric value between 0 and 1, or else findRUMI throws an error.

truefile

Text file from which to read true annotations of sequences. Should be a tab-delineated file with 2 columns: Sequences and GO terms (accessions).

predfile

Text file from which to read predicted annotations of sequences. Should be a tab-delineated file with 3 columns: Sequences, GO terms (accessions), and probability score from 0 to 1 for each prediction.

IAccr

A variable containing a named numeric vector of IA values for all the GO terms being used that will be used for calculations instead of R packages. This argument is optional.

Value

A data frame containing the RU and MI values for each sequence in the file.

See Also

computeIA RUMIcurve

Examples

# Using test data sets from SemDist, calculate RU and MI:
truefile <- system.file("extdata", "MFO_LABELS_TEST.txt", package="SemDist")
predfile <- system.file("extdata", "MFO_PREDS_TEST.txt", package="SemDist")
rumiTable <- findRUMI("MF", "human", 0.75, truefile, predfile)
avgRU <- mean(rumiTable$RU)
avgMI <- mean(rumiTable$MI)