Description Usage Arguments Details Value Author(s) References See Also Examples
The two orderings received as parameters are compared using the weighted overlap score and compared with a random distribution of that score (yielding an empirical pvalue).
1 2 3 4  compareLists(ID.List1, ID.List2, mapping = NULL,
two.sided=TRUE, B = 1000, alphas = NULL,
invar.q = 0.5, min.weight = 1e5,
no.reverse=FALSE)

ID.List1 
first ordered list of identifiers to be compared. 
ID.List2 
second ordered list to be compared, must have the same
length as 
mapping 
maps identifiers between the two lists. This is a matrix with
two columns. All items in 
two.sided 
whether the score is to be computed considering both ends of the list, or just the top members. 
B 
the number of permutations used to estimate empirical pvalues. 
alphas 
a set of alpha candidates to be evaluated. If set to

invar.q 
quantile of genes expected to be invariant. These are not used during shuffling, since they are expected to stay away from the ends of the lists, even when the data is perturbed to generate the NULL distribution. The default of 0.5 is reasonable for wholegenome gene expression analysis, but must be reconsidered when the compared lists are deduced from other sources. 
min.weight 
the minimal weight to be considered. 
no.reverse 
skip computing scores for reversed second list. 
The two lists received as arguments are matched against each other
according to the given mapping. The comparison is performed from both
ends by default. Permutations of lists are used to generate random scores
and compute empirical pvalues. The evaluation is also performed for the
case the lists should be reversed. From the resulting output, the set of
overlapping list identifiers can be extracted using function getOverlap
.
An object of class listComparison
is returned. It contains the following
list elements:
n 
the length of the lists 
call 
the input parameters 
nn 
the maximal number of genes corresponding to the alphas and the minimal weight 
scores 
scores for the straight list comparisons 
revScores 
scores for the reversed list comparison 
pvalues 
pvalues for the straight list comparison 
revPvalues 
pvalues for the reversed list comparison 
overlap 
number of overlapping identifiers per rank in straight comparison 
revOverlap 
number of overlapping identifiers per rank in reversed comparison 
randomScores 
random scores per weighting parameter 
ID.List1 
same as input ID.List1 
ID.List2 
same as input ID.List2 
There are print and plot methods for listComparison
objects. The plot
method takes a parameter which
to specify whether "overlap" or
"density" is to be drawn.
Claudio Lottaz, Stefanie Scheid
Yang X, Bentink S, Scheid S, and Spang R (2006): Similarities of ordered gene lists, to appear in Journal of Bioinformatics and Computational Biology.
1 2 3 4 5 6 7  ### Compare two artificial lists with some overlap
data(OL.data)
list1 < as.character(OL.data$map$prostate)
list2 < c(sample(list1[1:500]),sample(list1[501:1000]))
x < compareLists(list1,list2)
x
getOverlap(x)

Loading required package: Biobase
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':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
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Loading required package: twilight
Loading required package: splines
Simulating random scores...
0%.......:.........:.........:.........:......100%

List comparison
Assessing similarity of : top and bottom ranks
Length of lists : 1000
Quantile of invariant genes : 0.5
Number of random samples : 1000

Genes Scores p.values Rev.Scores Rev.p.values
0.115 100 1.073385 0.961 0.000000 1
0.077 150 7.490459 0.949 0.000000 1
0.058 200 24.187855 0.939 0.000000 1
0.038 300 104.685686 0.933 0.000000 1
0.029 400 272.780774 0.921 0.000000 1
0.023 500 559.029968 0.920 0.000000 1
0.015 750 1998.820224 0.923 7.062765 1
List comparison
Assessing similarity of : top and bottom ranks
Length of lists : 1000
Number of random samples : 1000

Lists are more alike in direct order
Chosen regularization parameter : alpha = 0.023 ( 500 genes)
Weighted overlap score : 559.03
Significance of similarity : pvalue = 0.92
Score percentage for common entries : 95
Entries contributing score percentage : 313
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