Description Usage Arguments Value Author(s) See Also Examples

Reads in a (tab-delimited) file containing the true annotations for a set of sequences, a (tab-delimited) file containing the predicted annotations and corresponding scores for the same sequences. Calculates and outputs the average remaining uncertainty, misinformation, and semantic similarity at a series of user-specified thresholds.

1 2 3 |

`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. |

`increment` |
A numeric value between 0 and 1 indicating the distance between each threshold that should be calculated. Note that the iteration starts from a threshold of 1, so an increment value of 0.08 will result in the thresholds 0.92, 0.84, 0.76 ... being used. |

`truefile` |
A character vector indicating the file from which to read the true annotations for the given sequences. Should be tab-delimited, with the first column containing the sequence ids and the second containing GO accessions. |

`predfiles` |
A character vector containing which files to read in as the predicted annotations. Should be tab-delimited, with the first column containing sequences, the second column containing GO accessions, and the third column containing the predictors 0-1 score for that 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. |

`add.weighted` |
A boolean indicating whether or not to add calculation of information content weighted versions of RU, MI, and SS to the output. |

`add.prec.rec` |
A boolean indicating whether or not to calculate precision, recall and specificity values for the prediction at each threshold and add to the output. |

Returns a named list with the same number of elements as the input "predfiles". Each element is a data frame containing all of the user-requested values for the data at each threshold.

Ian Gonzalez and Wyatt Clark

1 2 3 4 5 6 | ```
# Using test data sets from SemDist, plot a RUMI curve:
truefile <- system.file("extdata", "MFO_LABELS_TEST.txt", package="SemDist")
predfile <- system.file("extdata", "MFO_PREDS_TEST.txt", package="SemDist")
avgRUMIvals <- RUMIcurve("MF", "human", 0.05, truefile, predfile)
firstset <- avgRUMIvals[[1]]
plot(firstset$RU, firstset$MI)
``` |

```
Loading required package: AnnotationDbi
Loading required package: stats4
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
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: IRanges
Loading required package: S4Vectors
Attaching package: ‘S4Vectors’
The following object is masked from ‘package:base’:
expand.grid
Loading required package: GO.db
Loading required package: annotate
Loading required package: XML
Working on data for file: /usr/lib/R/site-library/SemDist/extdata/MFO_PREDS_TEST.txt
Getting true terms
Getting true IAs
Now working on threshold: 0.95
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 10.0425842861668, MI: 1.22712368033041
Now working on threshold: 0.9
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 9.41517267591757, MI: 1.88394232952515
Now working on threshold: 0.85
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 8.88119145459546, MI: 2.40819024669426
Now working on threshold: 0.8
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 8.49203531817439, MI: 3.21601035997672
Now working on threshold: 0.75
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 8.24157466528671, MI: 3.94991555376817
Now working on threshold: 0.7
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 7.74350530058713, MI: 4.82597198319498
Now working on threshold: 0.65
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 7.25367694130847, MI: 5.98367946270065
Now working on threshold: 0.6
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 6.88641834354203, MI: 7.44642060437438
Now working on threshold: 0.55
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 6.33710934362437, MI: 10.1202877672894
Now working on threshold: 0.5
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 5.66909734568201, MI: 15.0188503448115
Now working on threshold: 0.45
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 5.05564397771161, MI: 22.2381391115589
Now working on threshold: 0.4
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 4.35446728729524, MI: 36.5943643395658
Now working on threshold: 0.35
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 3.68508827269144, MI: 56.6821625238097
Now working on threshold: 0.3
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 2.95026896346243, MI: 90.598480334505
Now working on threshold: 0.25
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 2.30162606298156, MI: 123.861596712332
Now working on threshold: 0.2
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 2.12285861213662, MI: 137.051546116007
Now working on threshold: 0.15
Getting sequence predicted terms.
Getting IA values for predicted terms.
Doing the same for the intersect.
RU: 2.11234725107343, MI: 137.547222728205
Now working on threshold: 0.0999999999999999
Getting sequence predicted terms.
Now working on threshold: 0.05
Getting sequence predicted terms.
```

SemDist documentation built on Nov. 8, 2020, 8:27 p.m.

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