, Sven Laur [ctb]
| Title: | Methods for Robust Rank Aggregation |
|---|---|
| Description: | Methods for aggregating ranked lists, especially lists of genes. It implements the Robust Rank Aggregation Kolde et al (2012) <doi:10.1093/bioinformatics/btr709> and some other simple algorithms for the task. RRA method uses a probabilistic model for aggregation that is robust to noise and also facilitates the calculation of significance probabilities for all the elements in the final ranking. |
| Authors: | Raivo Kolde [aut, cre]
, Sven Laur [ctb]
|
| Maintainer: | Raivo Kolde <[email protected]> |
| License: | GPL-2 |
| Version: | 1.2.1 |
| Built: | 2024-12-01 06:56:00 UTC |
| Source: | https://github.com/cran/RobustRankAggreg |
Method implementing various gene list aggregation methods, most notably Robust Rank Aggregation.
aggregateRanks( glist, rmat = rankMatrix(glist, N, full = full), N = NA, method = "RRA", full = FALSE, exact = FALSE, topCutoff = NA )aggregateRanks( glist, rmat = rankMatrix(glist, N, full = full), N = NA, method = "RRA", full = FALSE, exact = FALSE, topCutoff = NA )
glist |
list of element vectors, the order of the vectors is used as the ranking. |
rmat |
the rankings in matrix format. The glist is by default converted to this format. |
N |
the number of ranked elements, important when using only top-k ranks, by default it is calculated as the number of unique elements in the input. |
method |
rank aggregation method, by default |
full |
indicates if the full rankings are given, used if the the sets of ranked elements do not match perfectly |
exact |
indicator showing if exact p-value will be calculated based on rho score (Default: if number of lists smaller than 10, exact is used) |
topCutoff |
a vector of cutoff values used to limit the number of elements in the input lists elements do not match perfectly |
All the methods implemented in this function make an assumtion that the number of ranked items is known. This assumption is satisfied for example in the case of gene lists (number of all genes known to certain extent), but not when aggregating results from google searches (there are too many web pages). This parameter N can be set manually and has strong influence on the end result. The p-values from RRA algorithm can be trusted only if N is close to the real value.
The rankings can be either full or partial. Tests with the RRA algorithm show that one does not lose too much information if only top-k rankings are used. The missing values are assumed to be equal to maximal value and that way taken into account appropriately.
The function can handle also the case when elements of the different rankings do not
overlap perfectly. For example if we combine results from different microarray
platforms with varying coverage. In this case these structurally missing values are
substituted with NA-s and handled differently than omitted parts of the rankings.
The function accepts as an input either list of rankings or rank matrix based on them.
It converts the list to rank matrix automatically using the function
rankMatrix. For most cases the ranking list is more convenient. Only
in complicated cases, for example with top-k lists and structural missing values one
would like to construct the rank matrix manually.
When the number of top elements included into input is specified in advance, for example some lists are limited to 100 elements, and the lengths of these lists differ significantly, we can use more sensitive and accurate algorithm for the score calculation. Then one has to specify in the input also the parameter topCutoff, which is a vector defining an cutoff value for each input list. For example if we have three lists of 1000 elements but first is limited to 100, second 200 and third to 900 elements, then the topCutoff parameter should be c(0.1, 0.2, 0.9).
Returns a two column dataframe with the element names and associated scores or p-values.
Raivo Kolde <[email protected]>
Raivo Kolde, Sven Laur, Priit Adler, Jaak Vilo, Robust rank aggregation for gene list integration and meta-analysis, Bioinformatics, 2012,, https://doi.org/10.1093/bioinformatics/btr709
# Make sample input data glist <- list(sample(letters, 4), sample(letters, 10), sample(letters, 12)) # Aggregate the inputs aggregateRanks(glist = glist, N = length(letters)) aggregateRanks(glist = glist, N = length(letters), method = "stuart") # Since we know the cutoffs for the lists in advance (4, 10, 12) we can use # the more accurate algorithm with parameter topCutoff # Use the rank matrix instead of the gene lists as the input r = rankMatrix(glist) aggregateRanks(rmat = r) # Example, when the input lists represent full rankings but the domains do not match glist <- list(sample(letters[4:24]), sample(letters[2:22]), sample(letters[1:20])) r = rankMatrix(glist, full = TRUE) head(r) aggregateRanks(rmat = r, method = "RRA") # Dataset representing significantly changed genes after knockouts # of cell cycle specific trancription factors data(cellCycleKO) r = rankMatrix(cellCycleKO$gl, N = cellCycleKO$N) ar = aggregateRanks(rmat = r) head(ar)# Make sample input data glist <- list(sample(letters, 4), sample(letters, 10), sample(letters, 12)) # Aggregate the inputs aggregateRanks(glist = glist, N = length(letters)) aggregateRanks(glist = glist, N = length(letters), method = "stuart") # Since we know the cutoffs for the lists in advance (4, 10, 12) we can use # the more accurate algorithm with parameter topCutoff # Use the rank matrix instead of the gene lists as the input r = rankMatrix(glist) aggregateRanks(rmat = r) # Example, when the input lists represent full rankings but the domains do not match glist <- list(sample(letters[4:24]), sample(letters[2:22]), sample(letters[1:20])) r = rankMatrix(glist, full = TRUE) head(r) aggregateRanks(rmat = r, method = "RRA") # Dataset representing significantly changed genes after knockouts # of cell cycle specific trancription factors data(cellCycleKO) r = rankMatrix(cellCycleKO$gl, N = cellCycleKO$N) ar = aggregateRanks(rmat = r) head(ar)
Calculate the beta scores for normalized rank vector.
betaScores(r)betaScores(r)
r |
vector of values in [0, 1] |
Takes in a vector with values in [0, 1]. It sorts the values to get the order statistics and calculates p-values for each of the order statistics. These are based on their expected distribution under the null hypothesis of uniform distribution.
In RRA algorithm context the inputs are supposed to be normalized ranks. However, p-values in general follow the uniform distribution, therefore it can be used with any kind of p-value vectors, to see if there are more small values than expected.
The NA values are removed before calculation and all results take into account only existing values.
The functions returns a vector of p-values, that correspond to the sorted input vector. The NA-s are pushed to the end.
Raivo Kolde <[email protected]>
Raivo Kolde, Sven Laur, Priit Adler, Jaak Vilo, Robust rank aggregation for gene list integration and meta-analysis, Bioinformatics, 2012,, https://doi.org/10.1093/bioinformatics/btr709
betaScores(c(runif(15))) betaScores(c(runif(10), rbeta(5, 1, 50)))betaScores(c(runif(15))) betaScores(c(runif(10), rbeta(5, 1, 50)))
The dataset contains lists yeast genes that were most influenced by 12 cell cycle related transcription factor knockouts. The dataset is a list with 3 slots
gl - set of gene lists in a format suitable for
aggregateRanks;
N - number of yeast genes;
ref - reference list of cell cycle related genes taken from de
Lichtenberg et al.
Raivo Kolde <[email protected]>
Reimand, J., Vaquerizas, J. M., Todd, A. E., Vilo, J., and Luscombe, N. M. (2010). "Comprehensive reanalysis of transcription factor knockout expression data in saccharomyces cerevisiae reveals many new targets. Nucleic Acids Res."
Hu, Z., Killion, P. J., and Iyer, V. R. (2007). "Genetic reconstruction of a functional transcriptional regulatory network." Nat. Genet., 39(5), 683-7
de Lichtenberg, U., Jensen, L. J., Fausboll, A., Jensen, T. S., Bork, P., and Brunak, S. (2005). "Comparison of computational methods for the identification of cell cycle- regulated genes. Bioinformatics, 21(7), 1164-71."
Convert a set of ranked lists into a rank matrix
rankMatrix(glist, N = NA, full = FALSE)rankMatrix(glist, N = NA, full = FALSE)
glist |
list of preference lists |
N |
number of all rankable elements |
full |
logical showing if the given rankings are complete |
The lists are converted to a format that is used by aggregateRanks. If partial rankings are given to the function, all the missing values are substituted by the maximum rank N, which can be specified manually. This parameter has a very strong influence on the performance of RRA algorithm, therefore it should be reasonably accurate. If the N is different for the gene lists, it can be also given as a vector.
Parameter full is used, when full rankings are given, but the sets of ranked elements do not match perfectly. Then the structurally missing values are substituted with NA-s.
A matrix, with as many columns as input rankings and rows as unique elements in all the rankings combined.
Raivo Kolde [email protected]
# Make sample input data glist <- list(sample(letters, 4), sample(letters, 10), sample(letters, 12)) r = rankMatrix(glist) r = rankMatrix(glist, full = TRUE) # Use real data data(cellCycleKO) r = rankMatrix(cellCycleKO$gl, N = cellCycleKO$N)# Make sample input data glist <- list(sample(letters, 4), sample(letters, 10), sample(letters, 12)) r = rankMatrix(glist) r = rankMatrix(glist, full = TRUE) # Use real data data(cellCycleKO) r = rankMatrix(cellCycleKO$gl, N = cellCycleKO$N)
Calculate Rho score for normalized rank vector
rhoScores(r, topCutoff = NA, exact = FALSE)rhoScores(r, topCutoff = NA, exact = FALSE)
r |
vector of values in [0, 1] |
topCutoff |
a vector of cutoff values used to limit the number of elements in the input lists |
exact |
indicator if exact p-values should be calculated (Warning: it is computationally unstable and does to give considerable gain) |
Takes in a vector with values in [0, 1]. Applies betaScores to the vector, takes the minimum of the beta scores and converts it to a valid p-value.
A rho score for the normalized rank vector.
Raivo Kolde <[email protected]>
Raivo Kolde, Sven Laur, Priit Adler, Jaak Vilo, Robust rank aggregation for gene list integration and meta-analysis, Bioinformatics, 2012,, https://doi.org/10.1093/bioinformatics/btr709
rhoScores(c(runif(15))) rhoScores(c(runif(10), rbeta(5, 1, 50)))rhoScores(c(runif(15))) rhoScores(c(runif(10), rbeta(5, 1, 50)))