Description Usage Arguments Details Value Author(s) References Examples
Method implementing various gene list aggregation methods, most notably Robust Rank Aggregation.
1 2 3 4  aggregateRanks(glist,
rmat = rankMatrix(glist, N, full = full), N = NA,
method = "RRA", full = FALSE, exact = F,
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 topk ranks, by default it is calculated as the number of unique elements in the input. 
method 
rank aggregation method, by defaylt

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 pvalue 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 pvalues 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 topk 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 resutls from different microarray
platforms with varying coverage. In this case these
structurally missing values are substituted with NAs 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 topk 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 pvalues.
Raivo Kolde <rkolde@gmail.com>
Kolde et al "Robust Rank Aggregation for gene list integration and metaanalysis" (in preparation)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  # 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)

Name Score
i i 0.01092399
w w 0.05052344
h h 0.33301092
g g 0.33301092
c c 0.40555303
s s 0.64041875
u u 0.67728721
d d 0.92324761
y y 0.92324761
b b 1.00000000
a a 1.00000000
t t 1.00000000
r r 1.00000000
n n 1.00000000
z z 1.00000000
m m 1.00000000
p p 1.00000000
x x 1.00000000
l l 1.00000000
Name Score
i i 0.001934456
w w 0.016841147
h h 0.049385526
g g 0.111003641
s s 0.126081020
c c 0.135184342
u u 0.196574875
d d 0.307749203
y y 0.307749203
b b 0.394173873
a a 0.394173873
n n 0.473088302
z z 0.609751934
t t 0.668183887
r r 0.720471097
m m 0.720471097
p p 0.766954939
x x 0.807976787
l l 0.843878015
Name Score
i i 0.02799242
w w 0.09272489
h h 0.44919084
g g 0.44919084
c c 0.70855810
s s 0.85114448
b b 1.00000000
d d 1.00000000
a a 1.00000000
u u 1.00000000
t t 1.00000000
r r 1.00000000
y y 1.00000000
n n 1.00000000
z z 1.00000000
m m 1.00000000
p p 1.00000000
x x 1.00000000
l l 1.00000000
[,1] [,2] [,3]
v 0.04761905 0.2380952 NA
o 0.09523810 0.6190476 0.45
u 0.14285714 0.0952381 NA
j 0.19047619 1.0000000 0.25
r 0.23809524 0.2857143 0.90
i 0.28571429 0.9523810 0.95
Name Score
u u 0.04081633
v v 0.11337868
b b 0.18000000
f f 0.23615160
m m 0.32393910
x x 0.33333333
h h 0.40848720
n n 0.42787500
j j 0.46875000
r r 0.59475219
o o 0.71169420
a a 0.80000000
w w 0.80952381
p p 0.81300000
s s 0.88888889
i i 1.00000000
e e 1.00000000
q q 1.00000000
t t 1.00000000
l l 1.00000000
d d 1.00000000
g g 1.00000000
k k 1.00000000
c c 1.00000000
Name Score
YJR148W YJR148W 2.326157e12
YMR034C YMR034C 5.704013e10
YPL016W YPL016W 1.626147e09
YKR093W YKR093W 8.819815e09
YOR043W YOR043W 2.520095e07
YFL026W YFL026W 2.972454e07
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.