Combining values

Combine several similarities into one using several methods.

Usage

combineScores(
  scores,
  method = c("max", "avg", "rcmax", "rcmax.avg", "BMA", "reciprocal"),
  round = FALSE,
  t = 0
)

combineScoresPar(scores, method, subSets = NULL, BPPARAM = NULL, ...)

Arguments

scores: Matrix of scores to be combined
method: one of c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal"), see Details.
round: Should the resulting value be rounded to the third digit?
t: Numeric value to filter scores below this value. Only used in the reciprocal method.
subSets: List of combinations as info in other functions.
BPPARAM: BiocParallel back-end parameters. By default (NULL) a for loop is used.
...: Other arguments passed to combineScores

Value

A numeric value as described in details.

Details

The input matrix can be a base matrix or a matrix from package Matrix. The methods return:

avg: The average or mean value.
max: The max value.
rcmax: The max of the column means or row means.
rcmax.avg: The sum of the max values by rows and columns divided by the number of columns and rows.
BMA: The same as rcmax.avg.
reciprocal: The double of the sum of the reciprocal maximal similarities (above a threshold) divided by the number of elements. See equation 3 of the Tao et al 2007 article.

Note

combineScores is a version of the function of the same name in package GOSemSim (GOSemSim::combineScores()) with optional rounding and some internal differences.

References

Ying Tao, Lee Sam, Jianrong Li, Carol Friedman, Yves A. Lussier; Information theory applied to the sparse gene ontology annotation network to predict novel gene function. Bioinformatics 2007; 23 (13): i529-i538. doi: 10.1093/bioinformatics/btm195

Author

Lluís Revilla based on Guangchuang Yu.

Examples

(d <- structure(c(
    0.4, 0.6, 0.222222222222222, 0.4, 0.4, 0, 0.25, 0.5,
    0.285714285714286
),
.Dim = c(3L, 3L),
.Dimnames = list(c("a", "b", "c"), c("d", "e", "f"))
))
#>           d   e         f
#> a 0.4000000 0.4 0.2500000
#> b 0.6000000 0.4 0.5000000
#> c 0.2222222 0.0 0.2857143
e <- d
sapply(c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal"),
    combineScores,
    scores = d
)
#>        avg        max      rcmax  rcmax.avg        BMA reciprocal 
#>  0.3397707  0.6000000  0.5000000  0.4642857  0.4642857  0.3333333 
d[1, 2] <- NA
sapply(c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal"),
    combineScores,
    scores = d
)
#>        avg        max      rcmax  rcmax.avg        BMA reciprocal 
#>  0.3322421  0.6000000  0.5000000  0.4642857  0.4642857  0.2000000 
colnames(e) <- rownames(e)
combineScoresPar(e, list(a = c("a", "b"), b = c("b", "c")),
    method = "max"
)
#>     a   b
#> a 0.6 0.5
#> b 0.5 0.5