Pablo Flores
| Snapshot Date: 2023-10-15 14:00:07 -0400 (Sun, 15 Oct 2023) |
| git_url: https://git.bioconductor.org/packages/goSorensen |
| git_branch: RELEASE_3_17 |
| git_last_commit: 93b7162 |
| git_last_commit_date: 2023-04-25 11:39:26 -0400 (Tue, 25 Apr 2023) |
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This page was generated on 2023-10-20 09:38:03 -0400 (Fri, 20 Oct 2023).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| kjohnson2 | macOS 12.6.1 Monterey | arm64 | 4.3.1 (2023-06-16) -- "Beagle Scouts" | 4347 |
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| Package 864/2230 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| goSorensen 1.2.0 (landing page) Pablo Flores
| kjohnson2 | macOS 12.6.1 Monterey / arm64 | OK | OK | OK | OK | | |||||||
|
To the developers/maintainers of the goSorensen package: - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
| Package: goSorensen |
| Version: 1.2.0 |
| Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:goSorensen.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings goSorensen_1.2.0.tar.gz |
| StartedAt: 2023-10-17 23:02:40 -0400 (Tue, 17 Oct 2023) |
| EndedAt: 2023-10-17 23:20:50 -0400 (Tue, 17 Oct 2023) |
| EllapsedTime: 1089.7 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: goSorensen.Rcheck |
| Warnings: 0 |
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### Running command:
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### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:goSorensen.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings goSorensen_1.2.0.tar.gz
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* using log directory ‘/Users/biocbuild/bbs-3.17-bioc-mac-arm64/meat/goSorensen.Rcheck’
* using R version 4.3.1 (2023-06-16)
* using platform: aarch64-apple-darwin20 (64-bit)
* R was compiled by
Apple clang version 14.0.0 (clang-1400.0.29.202)
GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.6.7
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘goSorensen/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘goSorensen’ version ‘1.2.0’
* package encoding: UTF-8
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* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
buildEnrichTable 20.63 1.793 34.399
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
Running ‘test_gosorensen_funcs.R’
Running ‘test_nonsense_genes.R’
OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: OK
goSorensen.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL goSorensen ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library’ * installing *source* package ‘goSorensen’ ... ** using staged installation ** R ** data ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (goSorensen)
goSorensen.Rcheck/tests/test_gosorensen_funcs.Rout
R version 4.3.1 (2023-06-16) -- "Beagle Scouts"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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> library(goSorensen)
Attaching package: 'goSorensen'
The following object is masked from 'package:utils':
upgrade
>
> # A contingency table of GO terms mutual enrichment
> # between gene lists "atlas" and "sanger":
> data(tab_atlas.sanger_BP3)
> tab_atlas.sanger_BP3
Enriched in sanger
Enriched in atlas TRUE FALSE
TRUE 56 30
FALSE 1 471
> ?tab_atlas.sanger_BP3
tab_atlas.sanger_BP3 package:goSorensen R Documentation
_C_r_o_s_s-_t_a_b_u_l_a_t_i_o_n _o_f _e_n_r_i_c_h_e_d _G_O _i_t_e_m_s _a_t _l_e_v_e_l _3 _o_f _o_n_t_o_l_o_g_y _B_P _i_n _t_w_o
_g_e_n_e _l_i_s_t_s
_D_e_s_c_r_i_p_t_i_o_n:
From the "Cancer gene list" of Bushman Lab, a collection of gene
lists related with cancer, for gene lists "Atlas" and "Sanger",
this dataset is the cross-tabulation of all GO items of ontology
BP at level 3 which are: Enriched in both lists, enriched in
sanger but not in atlas, non-enriched in sanger but enriched in
atlas and non-enriched in both lists. Take it just as an
illustrative example, non up-to-date for changes in the gene lists
or changes in the GO.
_U_s_a_g_e:
data(tab_atlas.sanger_BP3)
_F_o_r_m_a_t:
An object of class "table" representing a 2x2 contingency table.
_S_o_u_r_c_e:
<http://www.bushmanlab.org/links/genelists>
> class(tab_atlas.sanger_BP3)
[1] "table"
>
> # Sorensen-Dice dissimilarity on this contingency table:
> ?dSorensen
dSorensen package:goSorensen R Documentation
_C_o_m_p_u_t_a_t_i_o_n _o_f _t_h_e _S_o_r_e_n_s_e_n-_D_i_c_e _d_i_s_s_i_m_i_l_a_r_i_t_y
_D_e_s_c_r_i_p_t_i_o_n:
Computation of the Sorensen-Dice dissimilarity
_U_s_a_g_e:
dSorensen(x, ...)
## S3 method for class 'table'
dSorensen(x, check.table = TRUE, ...)
## S3 method for class 'matrix'
dSorensen(x, check.table = TRUE, ...)
## S3 method for class 'numeric'
dSorensen(x, check.table = TRUE, ...)
## S3 method for class 'character'
dSorensen(x, y, check.table = TRUE, ...)
## S3 method for class 'list'
dSorensen(x, check.table = TRUE, ...)
## S3 method for class 'tableList'
dSorensen(x, check.table = TRUE, ...)
_A_r_g_u_m_e_n_t_s:
x: either an object of class "table", "matrix" or "numeric"
representing a 2x2 contingency table, or a "character" vector
(a set of gene identifiers) or "list" or "tableList" object.
See the details section for more information.
...: extra parameters for function 'buildEnrichTable'.
check.table: Boolean. If TRUE (default), argument 'x' is checked to
adequately represent a 2x2 contingency table, by means of
function 'nice2x2Table'.
y: an object of class "character" representing a vector of valid
gene identifiers.
_D_e_t_a_i_l_s:
Given a 2x2 arrangement of frequencies (either implemented as a
"table", a "matrix" or a "numeric" object):
n11 n01
n10 n00,
this function computes the Sorensen-Dice dissimilarity
{n_10 + n_01}/{2 n_11 + n_10 + n_01}.
The subindex '11' corresponds to those GO items enriched in both
lists, '01' to items enriched in the second list but not in the
first one, '10' to items enriched in the first list but not
enriched in the second one and '00' corresponds to those GO items
non enriched in both gene lists, i.e., to the double negatives, a
value which is ignored in the computations.
In the "numeric" interface, if 'length(x) >= 3', the values are
interpreted as
(n_11, n_01, n_10, n_00), always in this order and discarding
extra values if necessary. The result is correct, regardless the
frequencies being absolute or relative.
If 'x' is an object of class "character", then 'x' (and 'y') must
represent two "character" vectors of valid gene identifiers. Then
the dissimilarity between lists 'x' and 'y' is computed, after
internally summarizing them as a 2x2 contingency table of joint
enrichment. This last operation is performed by function
'buildEnrichTable' and "valid gene identifiers" stands for the
coherency of these gene identifiers with the arguments
'geneUniverse' and 'orgPackg' of 'buildEnrichTable', passed by the
ellipsis argument '...' in 'dSorensen'.
If 'x' is an object of class "list", the argument must be a list
of "character" vectors, each one representing a gene list
(character identifiers). Then, all pairwise dissimilarities
between these gene lists are computed.
If 'x' is an object of class "tableList", the Sorensen-Dice
dissimilarity is computed over each one of these tables. Given k
gene lists (i.e. "character" vectors of gene identifiers) l1, l2,
..., lk, an object of class "tableList" (typically constructed by
a call to function 'buildEnrichTable') is a list of lists of
contingency tables t(i,j) generated from each pair of gene lists i
and j, with the following structure:
$l2
$l2$l1$t(2,1)
$l3
$l3$l1$t(3,1), $l3$l2$t(3,2)
...
$lk
$lk$l1$t(k,1), $lk$l2$t(k,2), ..., $lk$l(k-1)t(k,k-1)
_V_a_l_u_e:
In the "table", "matrix", "numeric" and "character" interfaces,
the value of the Sorensen-Dice dissimilarity. In the "list" and
"tableList" interfaces, the symmetric matrix of all pairwise
Sorensen-Dice dissimilarities.
_M_e_t_h_o_d_s (_b_y _c_l_a_s_s):
• 'table': S3 method for class "table"
• 'matrix': S3 method for class "matrix"
• 'numeric': S3 method for class "numeric"
• 'character': S3 method for class "character"
• 'list': S3 method for class "list"
• 'tableList': S3 method for class "tableList"
_S_e_e _A_l_s_o:
'buildEnrichTable' for constructing contingency tables of mutual
enrichment, 'nice2x2Table' for checking contingency tables
validity, 'seSorensen' for computing the standard error of the
dissimilarity, 'duppSorensen' for the upper limit of a one-sided
confidence interval of the dissimilarity, 'equivTestSorensen' for
an equivalence test.
_E_x_a_m_p_l_e_s:
# Gene lists 'atlas' and 'sanger' in 'allOncoGeneLists' dataset. Table of joint enrichment
# of GO items in ontology BP at level 3.
data(tab_atlas.sanger_BP3)
tab_atlas.sanger_BP3
?tab_atlas.sanger_BP3
dSorensen(tab_atlas.sanger_BP3)
# Table represented as a vector:
conti4 <- c(56, 1, 30, 471)
dSorensen(conti4)
# or as a plain matrix:
dSorensen(matrix(conti4, nrow = 2))
# This function is also appropriate for proportions:
dSorensen(conti4 / sum(conti4))
conti3 <- c(56, 1, 30)
dSorensen(conti3)
# Sorensen-Dice dissimilarity from scratch, directly from two gene lists:
# (These examples may be considerably time consuming due to many enrichment
# tests to build the contingency tables of mutual enrichment)
# data(pbtGeneLists)
# ?pbtGeneLists
# data(humanEntrezIDs)
# (Time consuming, building the table requires many enrichment tests:)
# dSorensen(pbtGeneLists[[2]], pbtGeneLists[[4]],
# onto = "CC", GOLevel = 3,
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
# Essentially, the above code makes the same as:
# tab.IRITD3vsKT1 <- buildEnrichTable(pbtGeneLists[[2]], pbtGeneLists[[4]],
# onto = "CC", GOLevel = 3,
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
# dSorensen(tab.IRITD3vsKT1)
# (Quite time consuming, all pairwise dissimilarities:)
# dSorensen(pbtGeneLists,
# onto = "CC", GOLevel = 3,
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> dSorensen(tab_atlas.sanger_BP3)
[1] 0.2167832
>
> # Standard error of this Sorensen-Dice dissimilarity estimate:
> ?seSorensen
seSorensen package:goSorensen R Documentation
_S_t_a_n_d_a_r_d _e_r_r_o_r _o_f _t_h_e _s_a_m_p_l_e _S_o_r_e_n_s_e_n-_D_i_c_e _d_i_s_s_i_m_i_l_a_r_i_t_y, _a_s_y_m_p_t_o_t_i_c
_a_p_p_r_o_a_c_h
_D_e_s_c_r_i_p_t_i_o_n:
Standard error of the sample Sorensen-Dice dissimilarity,
asymptotic approach
_U_s_a_g_e:
seSorensen(x, ...)
## S3 method for class 'table'
seSorensen(x, check.table = TRUE, ...)
## S3 method for class 'matrix'
seSorensen(x, check.table = TRUE, ...)
## S3 method for class 'numeric'
seSorensen(x, check.table = TRUE, ...)
## S3 method for class 'character'
seSorensen(x, y, check.table = TRUE, ...)
## S3 method for class 'list'
seSorensen(x, check.table = TRUE, ...)
## S3 method for class 'tableList'
seSorensen(x, check.table = TRUE, ...)
_A_r_g_u_m_e_n_t_s:
x: either an object of class "table", "matrix" or "numeric"
representing a 2x2 contingency table, or a "character" (a set
of gene identifiers) or "list" or "tableList" object. See the
details section for more information.
...: extra parameters for function 'buildEnrichTable'.
check.table: Boolean. If TRUE (default), argument 'x' is checked to
adequately represent a 2x2 contingency table. This checking
is performed by means of function 'nice2x2Table'.
y: an object of class "character" representing a vector of gene
identifiers.
_D_e_t_a_i_l_s:
This function computes the standard error estimate of the sample
Sorensen-Dice dissimilarity, given a 2x2 arrangement of
frequencies (either implemented as a "table", a "matrix" or a
"numeric" object):
n11 n10
n01 n00,
The subindex '11' corresponds to those GO items enriched in both
lists, '01' to items enriched in the second list but not in the
first one, '10' to items enriched in the first list but not
enriched in the second one and '00' corresponds to those GO items
non enriched in both gene lists, i.e., to the double negatives, a
value which is ignored in the computations.
In the "numeric" interface, if 'length(x) >= 3', the values are
interpreted as
(n_11, n_01, n_10), always in this order.
If 'x' is an object of class "character", then 'x' (and 'y') must
represent two "character" vectors of valid gene identifiers. Then
the standard error for the dissimilarity between lists 'x' and 'y'
is computed, after internally summarizing them as a 2x2
contingency table of joint enrichment. This last operation is
performed by function 'buildEnrichTable' and "valid gene
identifiers" stands for the coherency of these gene identifiers
with the arguments 'geneUniverse' and 'orgPackg' of
'buildEnrichTable', passed by the ellipsis argument '...' in
'seSorensen'.
In the "list" interface, the argument must be a list of
"character" vectors, each one representing a gene list (character
identifiers). Then, all pairwise standard errors of the
dissimilarity between these gene lists are computed.
If 'x' is an object of class "tableList", the standard error of
the Sorensen-Dice dissimilarity estimate is computed over each one
of these tables. Given k gene lists (i.e. "character" vectors of
gene identifiers) l1, l2, ..., lk, an object of class "tableList"
(typically constructed by a call to function 'buildEnrichTable')
is a list of lists of contingency tables t(i,j) generated from
each pair of gene lists i and j, with the following structure:
$l2
$l2$l1$t(2,1)
$l3
$l3$l1$t(3,1), $l3$l2$t(3,2)
...
$lk
$lk$l1$t(k,1), $lk$l2$t(k,2), ..., $lk$l(k-1)t(k,k-1)
_V_a_l_u_e:
In the "table", "matrix", "numeric" and "character" interfaces,
the value of the standard error of the Sorensen-Dice dissimilarity
estimate. In the "list" and "tableList" interfaces, the symmetric
matrix of all standard error dissimilarity estimates.
_M_e_t_h_o_d_s (_b_y _c_l_a_s_s):
• 'table': S3 method for class "table"
• 'matrix': S3 method for class "matrix"
• 'numeric': S3 method for class "numeric"
• 'character': S3 method for class "character"
• 'list': S3 method for class "list"
• 'tableList': S3 method for class "tableList"
_S_e_e _A_l_s_o:
'buildEnrichTable' for constructing contingency tables of mutual
enrichment, 'nice2x2Table' for checking the validity of enrichment
contingency tables, 'dSorensen' for computing the Sorensen-Dice
dissimilarity, 'duppSorensen' for the upper limit of a one-sided
confidence interval of the dissimilarity, 'equivTestSorensen' for
an equivalence test.
_E_x_a_m_p_l_e_s:
# Gene lists 'atlas' and 'sanger' in 'Cangenes' dataset. Table of joint enrichment
# of GO items in ontology BP at level 3.
data(tab_atlas.sanger_BP3)
tab_atlas.sanger_BP3
dSorensen(tab_atlas.sanger_BP3)
seSorensen(tab_atlas.sanger_BP3)
# Contingency table as a numeric vector:
seSorensen(c(56, 1, 30, 47))
seSorensen(c(56, 1, 30))
# (These examples may be considerably time consuming due to many enrichment
# tests to build the contingency tables of mutual enrichment)
# ?pbtGeneLists
# Standard error of the sample Sorensen-Dice dissimilarity, directly from
# two gene lists, from scratch:
# seSorensen(pbtGeneLists[[2]], pbtGeneLists[[4]],
# onto = "CC", GOLevel = 5,
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
# Essentially, the above code makes the same as:
# tab.IRITD3vsKT1 <- buildEnrichTable(pbtGeneLists[[2]], pbtGeneLists[[4]],
# onto = "CC", GOLevel = 5,
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
# tab.IRITD3vsKT1
# seSorensen(tab.IRITD3vsKT1)
# All pairwise standard errors (quite time consuming):
# seSorensen(pbtGeneLists,
# onto = "CC", GOLevel = 5,
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> seSorensen(tab_atlas.sanger_BP3)
[1] 0.03822987
>
> # Upper 95% confidence limit for the Sorensen-Dice dissimilarity:
> ?duppSorensen
duppSorensen package:goSorensen R Documentation
_U_p_p_e_r _l_i_m_i_t _o_f _a _o_n_e-_s_i_d_e_d _c_o_n_f_i_d_e_n_c_e _i_n_t_e_r_v_a_l (_0, _d_U_p_p] _f_o_r _t_h_e
_S_o_r_e_n_s_e_n-_D_i_c_e _d_i_s_s_i_m_i_l_a_r_i_t_y
_D_e_s_c_r_i_p_t_i_o_n:
Upper limit of a one-sided confidence interval (0, dUpp] for the
Sorensen-Dice dissimilarity
_U_s_a_g_e:
duppSorensen(x, ...)
## S3 method for class 'table'
duppSorensen(
x,
dis = dSorensen.table(x, check.table = FALSE),
se = seSorensen.table(x, check.table = FALSE),
conf.level = 0.95,
z.conf.level = qnorm(1 - conf.level),
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'matrix'
duppSorensen(
x,
dis = dSorensen.matrix(x, check.table = FALSE),
se = seSorensen.matrix(x, check.table = FALSE),
conf.level = 0.95,
z.conf.level = qnorm(1 - conf.level),
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'numeric'
duppSorensen(
x,
dis = dSorensen.numeric(x, check.table = FALSE),
se = seSorensen.numeric(x, check.table = FALSE),
conf.level = 0.95,
z.conf.level = qnorm(1 - conf.level),
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'character'
duppSorensen(
x,
y,
conf.level = 0.95,
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'list'
duppSorensen(
x,
conf.level = 0.95,
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'tableList'
duppSorensen(
x,
conf.level = 0.95,
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
_A_r_g_u_m_e_n_t_s:
x: either an object of class "table", "matrix" or "numeric"
representing a 2x2 contingency table, or a "character" (a set
of gene identifiers) or "list" or "tableList" object. See the
details section for more information.
...: additional arguments for function 'buildEnrichTable'.
dis: Sorensen-Dice dissimilarity value. Only required to speed
computations if this value is known in advance.
se: standard error estimate of the sample dissimilarity. Only
required to speed computations if this value is known in
advance.
conf.level: confidence level of the one-sided confidence interval, a
numeric value between 0 and 1.
z.conf.level: standard normal (or bootstrap, see arguments below)
distribution quantile at the '1 - conf.level' value. Only
required to speed computations if this value is known in
advance. Then, the argument 'conf.level' is ignored.
boot: boolean. If TRUE, 'z.conf.level' is computed by means of a
bootstrap approach instead of the asymptotic normal approach.
Defaults to FALSE.
nboot: numeric, number of initially planned bootstrap replicates.
Ignored if 'boot == FALSE'. Defaults to 10000.
check.table: Boolean. If TRUE (default), argument 'x' is checked to
adequately represent a 2x2 contingency table. This checking
is performed by means of function 'nice2x2Table'.
y: an object of class "character" representing a vector of gene
identifiers.
_D_e_t_a_i_l_s:
This function computes the upper limit of a one-sided confidence
interval for the Sorensen-Dice dissimilarity, given a 2x2
arrangement of frequencies (either implemented as a "table", a
"matrix" or a "numeric" object):
n11 n01
n10 n00,
The subindex '11' corresponds to those GO items enriched in both
lists, '01' to items enriched in the second list but not in the
first one, '10' to items enriched in the first list but not
enriched in the second one and '00' corresponds to those GO items
non enriched in both gene lists, i.e., to the double negatives, a
value which is ignored in the computations, except if 'boot ==
TRUE'.
In the "numeric" interface, if 'length(x) >= 4', the values are
interpreted as
(n_11, n_01, n_10, n_00), always in this order and discarding
extra values if necessary.
Arguments 'dis', 'se' and 'z.conf.level' are not required. If
known in advance (e.g., as a consequence of previous computations
with the same data), providing its value may speed the
computations.
By default, 'z.conf.level' corresponds to the 1 - conf.level
quantile of a standard normal N(0,1) distribution, as the
studentized statistic (^d - d) / ^se) is asymptotically N(0,1). In
the studentized statistic, d stands for the "true" Sorensen-Dice
dissimilarity, ^d to its sample estimate and ^se for the estimate
of its standard error. In fact, the normal is its limiting
distribution but, for finite samples, the true sampling
distribution may present departures from normality (mainly with
some inflation in the left tail). The bootstrap method provides a
better approximation to the true sampling distribution. In the
bootstrap approach, 'nboot' new bootstrap contingency tables are
generated from a multinomial distribution with parameters 'size ='
n11 + n01 + n10 + n00 and probabilities %. Sometimes, some of
these generated tables may present so low frequencies of
enrichment that make them unable for Sorensen-Dice computations.
As a consequence, the number of effective bootstrap samples may be
lower than the number of initially planned bootstrap samples
'nboot'. Computing in advance the value of argument 'z.conf.level'
may be a way to cope with these departures from normality, by
means of a more adequate quantile function. Alternatively, if
'boot == TRUE', a bootstrap quantile is internally computed.
If 'x' is an object of class "character", then 'x' (and 'y') must
represent two "character" vectors of valid gene identifiers. Then
the confidence interval for the dissimilarity between lists 'x'
and 'y' is computed, after internally summarizing them as a 2x2
contingency table of joint enrichment. This last operation is
performed by function 'buildEnrichTable' and "valid gene
identifiers" stands for the coherency of these gene identifiers
with the arguments 'geneUniverse' and 'orgPackg' of
'buildEnrichTable', passed by the ellipsis argument '...' in
'dUppSorensen'.
In the "list" interface, the argument must be a list of
"character" vectors, each one representing a gene list (character
identifiers). Then, all pairwise upper limits of the dissimilarity
between these gene lists are computed.
In the "tableList" interface, the upper limits are computed over
each one of these tables. Given gene lists (i.e. "character"
vectors of gene identifiers) l1, l2, ..., lk, an object of class
"tableList" (typically constructed by a call to function
'buildEnrichTable') is a list of lists of contingency tables
t(i,j) generated from each pair of gene lists i and j, with the
following structure:
$l2
$l2$l1$t(2,1)
$l3
$l3$l1$t(3,1), $l3$l2$t(3,2)
...
$lk
$lk$l1$t(k,1), $lk$l2$t(k,2), ..., $lk$l(k-1)t(k,k-1)
_V_a_l_u_e:
In the "table", "matrix", "numeric" and "character" interfaces,
the value of the Upper limit of the confidence interval for the
Sorensen-Dice dissimilarity. When 'boot == TRUE', this result also
haves a an extra attribute: "eff.nboot" which corresponds to the
number of effective bootstrap replicats, see the details section.
In the "list" and "tableList" interfaces, the result is the
symmetric matrix of all pairwise upper limits.
_M_e_t_h_o_d_s (_b_y _c_l_a_s_s):
• 'table': S3 method for class "table"
• 'matrix': S3 method for class "matrix"
• 'numeric': S3 method for class "numeric"
• 'character': S3 method for class "character"
• 'list': S3 method for class "list"
• 'tableList': S3 method for class "tableList"
_S_e_e _A_l_s_o:
'buildEnrichTable' for constructing contingency tables of mutual
enrichment, 'nice2x2Table' for checking contingency tables
validity, 'dSorensen' for computing the Sorensen-Dice
dissimilarity, 'seSorensen' for computing the standard error of
the dissimilarity, 'equivTestSorensen' for an equivalence test.
_E_x_a_m_p_l_e_s:
# Gene lists 'atlas' and 'sanger' in 'Cangenes' dataset. Table of joint enrichment
# of GO items in ontology BP at level 3.
data(tab_atlas.sanger_BP3)
?tab_atlas.sanger_BP3
duppSorensen(tab_atlas.sanger_BP3)
dSorensen(tab_atlas.sanger_BP3) + qnorm(0.95) * seSorensen(tab_atlas.sanger_BP3)
# Using the bootstrap approximation instead of the normal approximation to
# the sampling distribution of (^d - d) / se(^d):
duppSorensen(tab_atlas.sanger_BP3, boot = TRUE)
# Contingency table as a numeric vector:
duppSorensen(c(56, 1, 30, 47))
duppSorensen(c(56, 1, 30))
# Upper confidence limit for the Sorensen-Dice dissimilarity, from scratch,
# directly from two gene lists:
# (These examples may be considerably time consuming due to many enrichment
# tests to build the contingency tables of mutual enrichment)
# data(pbtGeneLists)
# ?pbtGeneLists
# data(humanEntrezIDs)
# duppSorensen(pbtGeneLists[[2]], pbtGeneLists[[4]],
# onto = "CC", GOLevel = 5,
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
# Even more time consuming (all pairwise values):
# duppSorensen(pbtGeneLists,
# onto = "CC", GOLevel = 5,
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> duppSorensen(tab_atlas.sanger_BP3)
[1] 0.2796658
> # This confidence limit is based on an assimptotic normal N(0,1)
> # approximation to the distribution of (dSampl - d) / se, where
> # dSampl stands for the sample dissimilarity, d for the true dissimilarity
> # and se for the sample dissimilarity standard error estimate.
>
> # Upper confidence limit but using a Student's t instead of a N(0,1)
> # (just as an example, not recommended -no theoretical justification)
> df <- sum(tab_atlas.sanger_BP3[1:3]) - 2
> duppSorensen(tab_atlas.sanger_BP3, z.conf.level = qt(1 - 0.95, df))
[1] 0.2803587
>
> # Upper confidence limit but using a bootstrap approximation
> # to the sampling distribution, instead of a N(0,1)
> set.seed(123)
> duppSorensen(tab_atlas.sanger_BP3, boot = TRUE)
[1] 0.2871182
attr(,"eff.nboot")
[1] 10000
>
> # Some computations on diverse data structures:
> badConti <- as.table(matrix(c(501, 27, 36, 12, 43, 15, 0, 0, 0),
+ nrow = 3, ncol = 3,
+ dimnames = list(c("a1","a2","a3"),
+ c("b1", "b2","b3"))))
> tryCatch(nice2x2Table(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(badConti): Not a 2x2 table>
>
> incompleteConti <- badConti[1,1:min(2,ncol(badConti)), drop = FALSE]
> incompleteConti
b1 b2
a1 501 12
> tryCatch(nice2x2Table(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(incompleteConti): Not a 2x2 table>
>
> contiAsVector <- c(32, 21, 81, 1439)
> nice2x2Table(contiAsVector)
[1] TRUE
> contiAsVector.mat <- matrix(contiAsVector, nrow = 2)
> contiAsVector.mat
[,1] [,2]
[1,] 32 81
[2,] 21 1439
> contiAsVectorLen3 <- c(32, 21, 81)
> nice2x2Table(contiAsVectorLen3)
[1] TRUE
>
> tryCatch(dSorensen(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
>
> # Apparently, the next order works fine, but returns a wrong value!
> dSorensen(badConti, check.table = FALSE)
[1] 0.05915493
>
> tryCatch(dSorensen(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> dSorensen(contiAsVector)
[1] 0.6144578
> dSorensen(contiAsVector.mat)
[1] 0.6144578
> dSorensen(contiAsVectorLen3)
[1] 0.6144578
> dSorensen(contiAsVectorLen3, check.table = FALSE)
[1] 0.6144578
> dSorensen(c(0,0,0,45))
[1] NaN
>
> tryCatch(seSorensen(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> tryCatch(seSorensen(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> seSorensen(contiAsVector)
[1] 0.04818012
> seSorensen(contiAsVector.mat)
[1] 0.04818012
> seSorensen(contiAsVectorLen3)
[1] 0.04818012
> seSorensen(contiAsVectorLen3, check.table = FALSE)
[1] 0.04818012
> tryCatch(seSorensen(contiAsVectorLen3, check.table = "not"), error = function(e) {return(e)})
<simpleError in seSorensen.numeric(contiAsVectorLen3, check.table = "not"): Argument 'check.table' must be logical>
> seSorensen(c(0,0,0,45))
[1] NaN
>
> tryCatch(duppSorensen(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> tryCatch(duppSorensen(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> duppSorensen(contiAsVector)
[1] 0.6937071
> duppSorensen(contiAsVector.mat)
[1] 0.6937071
> set.seed(123)
> duppSorensen(contiAsVector, boot = TRUE)
[1] 0.6922658
attr(,"eff.nboot")
[1] 10000
> set.seed(123)
> duppSorensen(contiAsVector.mat, boot = TRUE)
[1] 0.6922658
attr(,"eff.nboot")
[1] 10000
> duppSorensen(contiAsVectorLen3)
[1] 0.6937071
> # Bootstrapping requires full contingency tables (4 values)
> set.seed(123)
> tryCatch(duppSorensen(contiAsVectorLen3, boot = TRUE), error = function(e) {return(e)})
<simpleError in duppSorensen.numeric(contiAsVectorLen3, boot = TRUE): Bootstraping requires a numeric vector of 4 frequencies>
> duppSorensen(c(0,0,0,45))
[1] NaN
>
> # Equivalence test, H0: d >= d0 vs H1: d < d0 (d0 = 0.4444)
> ?equivTestSorensen
equivTestSorensen package:goSorensen R Documentation
_E_q_u_i_v_a_l_e_n_c_e _t_e_s_t _b_a_s_e_d _o_n _t_h_e _S_o_r_e_n_s_e_n-_D_i_c_e _d_i_s_s_i_m_i_l_a_r_i_t_y
_D_e_s_c_r_i_p_t_i_o_n:
Equivalence test based on the Sorensen-Dice dissimilarity,
computed either by an asymptotic normal approach or by a bootstrap
approach.
_U_s_a_g_e:
equivTestSorensen(x, ...)
## S3 method for class 'table'
equivTestSorensen(
x,
d0 = 1/(1 + 1.25),
conf.level = 0.95,
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'matrix'
equivTestSorensen(
x,
d0 = 1/(1 + 1.25),
conf.level = 0.95,
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'numeric'
equivTestSorensen(
x,
d0 = 1/(1 + 1.25),
conf.level = 0.95,
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'character'
equivTestSorensen(
x,
y,
d0 = 1/(1 + 1.25),
conf.level = 0.95,
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'list'
equivTestSorensen(
x,
d0 = 1/(1 + 1.25),
conf.level = 0.95,
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
## S3 method for class 'tableList'
equivTestSorensen(
x,
d0 = 1/(1 + 1.25),
conf.level = 0.95,
boot = FALSE,
nboot = 10000,
check.table = TRUE,
...
)
_A_r_g_u_m_e_n_t_s:
x: either an object of class "table", "matrix", "numeric",
"character", "list" or "tableList". See the details section
for more information.
...: extra parameters for function 'buildEnrichTable'.
d0: equivalence threshold for the Sorensen-Dice dissimilarity, d.
The null hypothesis states that d >= d0, i.e., inequivalence
between the compared gene lists and the alternative that d <
d0, i.e., equivalence or dissimilarity irrelevance (up to a
level d0).
conf.level: confidence level of the one-sided confidence interval, a
value between 0 and 1.
boot: boolean. If TRUE, the confidence interval and the test
p-value are computed by means of a bootstrap approach instead
of the asymptotic normal approach. Defaults to FALSE.
nboot: numeric, number of initially planned bootstrap replicates.
Ignored if 'boot == FALSE'. Defaults to 10000.
check.table: Boolean. If TRUE (default), argument 'x' is checked to
adequately represent a 2x2 contingency table. This checking
is performed by means of function 'nice2x2Table'.
y: an object of class "character" representing a list of gene
identifiers.
_D_e_t_a_i_l_s:
This function computes either the normal asymptotic or the
bootstrap equivalence test based on the Sorensen-Dice
dissimilarity, given a 2x2 arrangement of frequencies (either
implemented as a "table", a "matrix" or a "numeric" object):
n11 n10
n01 n00,
The subindex '11' corresponds to those GO items enriched in both
lists, '01' to items enriched in the second list but not in the
first one, '10' to items enriched in the first list but not
enriched in the second one and '00' corresponds to those GO items
non enriched in both gene lists, i.e., to the double negatives, a
value which is ignored in the computations.
In the "numeric" interface, if 'length(x) >= 4', the values are
interpreted as
(n_11, n_01, n_10, n_00), always in this order and discarding
extra values if necessary.
If 'x' is an object of class "character", then 'x' (and 'y') must
represent two "character" vectors of valid gene identifiers. Then
the equivalence test is performed between 'x' and 'y', after
internally summarizing them as a 2x2 contingency table of joint
enrichment. This last operation is performed by function
'buildEnrichTable' and "valid gene identifiers" stands for the
coherency of these gene identifiers with the arguments
'geneUniverse' and 'orgPackg' of 'buildEnrichTable', passed by the
ellipsis argument '...' in 'equivTestSorensen'.
If 'x' is an object of class "list", each of its elements must be
a "character" vector of gene identifiers. Then all pairwise
equivalence tests are performed between these gene lists.
Class "tableList" corresponds to objects representing all mutual
enrichment contingency tables generated in a pairwise fashion:
Given gene lists l1, l2, ..., lk, an object of class "tableList"
(typically constructed by a call to function 'buildEnrichTable')
is a list of lists of contingency tables tij generated from each
pair of gene lists i and j, with the following structure:
$l2
$l2$l1$t21
$l3
$l3$l1$t31, $l3$l2$t32
...
$lk$l1$tk1, $lk$l2$tk2, ..., $lk$l(k-1)tk(k-1)
If 'x' is an object of class "tableList", the test is performed
over each one of these tables.
The test is based on the fact that the studentized statistic (^d -
d) / ^se is approximately distributed as a standard normal. ^d
stands for the sample Sorensen-Dice dissimilarity, d for its true
(unknown) value and ^se for the estimate of its standard error.
This result is asymptotically correct, but the true distribution
of the studentized statistic is not exactly normal for finite
samples, with a heavier left tail than expected under the Gaussian
model, which may produce some type I error inflation. The
bootstrap method provides a better approximation to this
distribution. In the bootstrap approach, 'nboot' new bootstrap
contingency tables are generated from a multinomial distribution
with parameters 'size =' (n11 + n01 + n10 + n00) and probabilities
%. Sometimes, some of these generated tables may present so low
frequencies of enrichment that make them unable for Sorensen-Dice
computations. As a consequence, the number of effective bootstrap
samples may be lower than the number of initially planned ones,
'nboot', but our simulation studies concluded that this makes the
test more conservative, less prone to reject a truly false null
hypothesis of inequivalence, but in any case protects from
inflating the type I error.
In a bootstrap test result, use 'getNboot' to access the number of
initially planned bootstrap replicates and 'getEffNboot' to access
the number of finally effective bootstrap replicates.
_V_a_l_u_e:
For all interfaces (except for the "list" and "tableList"
interfaces) the result is a list of class "equivSDhtest" which
inherits from "htest", with the following components:
statistic the value of the studentized statistic (dSorensen(x) -
d0) / seSorensen(x)
p.value the p-value of the test
conf.int the one-sided confidence interval (0, dUpp]
estimate the Sorensen dissimilarity estimate, dSorensen(x)
null.value the value of d0
stderr the standard error of the Sorensen dissimilarity estimate,
seSorensen(x), used as denominator in the studentized
statistic
alternative a character string describing the alternative
hypothesis
method a character string describing the test
data.name a character string giving the names of the data
enrichTab the 2x2 contingency table of joint enrichment whereby
the test was based
For the "list" and "tableList" interfaces, the result is an
"equivSDhtestList", a list of objects with all pairwise
comparisons, each one being an object of "equivSDhtest" class.
_M_e_t_h_o_d_s (_b_y _c_l_a_s_s):
• 'table': S3 method for class "table"
• 'matrix': S3 method for class "matrix"
• 'numeric': S3 method for class "numeric"
• 'character': S3 method for class "character"
• 'list': S3 method for class "list"
• 'tableList': S3 method for class "tableList"
_S_e_e _A_l_s_o:
'nice2x2Table' for checking and reformatting data, 'dSorensen' for
computing the Sorensen-Dice dissimilarity, 'seSorensen' for
computing the standard error of the dissimilarity, 'duppSorensen'
for the upper limit of a one-sided confidence interval of the
dissimilarity. 'getTable', 'getPvalue', 'getUpper', 'getSE',
'getNboot' and 'getEffNboot' for accessing specific fields in the
result of these testing functions. 'update' for updating the
result of these testing functions with alternative equivalence
limits, confidence levels or to convert a normal result in a
bootstrap result or the reverse.
_E_x_a_m_p_l_e_s:
# Gene lists 'atlas' and 'sanger' in 'allOncoGeneLists' dataset. Table of joint enrichment
# of GO items in ontology BP at level 3.
data(tab_atlas.sanger_BP3)
tab_atlas.sanger_BP3
equivTestSorensen(tab_atlas.sanger_BP3)
# Bootstrap test:
equivTestSorensen(tab_atlas.sanger_BP3, boot = TRUE)
# Equivalence tests from scratch, directly from gene lists:
# (These examples may be considerably time consuming due to many enrichment
# tests to build the contingency tables of mutual enrichment)
# ?pbtGeneLists
# Gene universe:
# data(humanEntrezIDs)
# equivTestSorensen(pbtGeneLists[["IRITD3"]], pbtGeneLists[["IRITD5"]],
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
# onto = "CC", GOLevel = 5)
# Bootstrap instead of normal approximation test:
# equivTestSorensen(pbtGeneLists[["IRITD3"]], pbtGeneLists[["IRITD5"]],
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
# onto = "CC", GOLevel = 5,
# boot = TRUE)
# Essentially, the above code makes:
# IRITD3vs5.CC5 <- buildEnrichTable(pbtGeneLists[["IRITD3"]], pbtGeneLists[["IRITD5"]],
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
# onto = "CC", GOLevel = 5)
# IRITD3vs5.CC5
# equivTestSorensen(IRITD3vs5.CC5)
# equivTestSorensen(IRITD3vs5.CC5, boot = TRUE)
# (Note that building first the contingency table may be advantageous to save time!)
# All pairwise equivalence tests:
# equivTestSorensen(pbtGeneLists,
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
# onto = "CC", GOLevel = 5)
# Equivalence test on a contingency table represented as a numeric vector:
equivTestSorensen(c(56, 1, 30, 47))
equivTestSorensen(c(56, 1, 30, 47), boot = TRUE)
equivTestSorensen(c(56, 1, 30))
# Error: all frequencies are needed for bootstrap:
try(equivTestSorensen(c(56, 1, 30), boot = TRUE), TRUE)
> equiv.atlas.sanger <- equivTestSorensen(tab_atlas.sanger_BP3)
> equiv.atlas.sanger
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab_atlas.sanger_BP3
(d - d0) / se = -5.9551, p-value = 1.3e-09
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.2796658
sample estimates:
Sorensen dissimilarity
0.2167832
attr(,"se")
standard error
0.03822987
> getTable(equiv.atlas.sanger)
Enriched in sanger
Enriched in atlas TRUE FALSE
TRUE 56 30
FALSE 1 471
> getPvalue(equiv.atlas.sanger)
p-value
1.299869e-09
>
> tryCatch(equivTestSorensen(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> tryCatch(equivTestSorensen(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> equivTestSorensen(contiAsVector)
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: contiAsVector
(d - d0) / se = 3.5287, p-value = 0.9998
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6937071
sample estimates:
Sorensen dissimilarity
0.6144578
attr(,"se")
standard error
0.04818012
> equivTestSorensen(contiAsVector.mat)
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: contiAsVector.mat
(d - d0) / se = 3.5287, p-value = 0.9998
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6937071
sample estimates:
Sorensen dissimilarity
0.6144578
attr(,"se")
standard error
0.04818012
> set.seed(123)
> equivTestSorensen(contiAsVector.mat, boot = TRUE)
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: contiAsVector.mat
(d - d0) / se = 3.5287, p-value = 0.9996
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6922658
sample estimates:
Sorensen dissimilarity
0.6144578
attr(,"se")
standard error
0.04818012
> equivTestSorensen(contiAsVectorLen3)
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: contiAsVectorLen3
(d - d0) / se = 3.5287, p-value = 0.9998
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6937071
sample estimates:
Sorensen dissimilarity
0.6144578
attr(,"se")
standard error
0.04818012
>
> tryCatch(equivTestSorensen(contiAsVectorLen3, boot = TRUE), error = function(e) {return(e)})
<simpleError in equivTestSorensen.numeric(contiAsVectorLen3, boot = TRUE): Bootstraping requires a numeric vector of 4 frequencies>
>
> equivTestSorensen(c(0,0,0,45))
No test performed due non finite (d - d0) / se statistic
data: c(0, 0, 0, 45)
(d - d0) / se = NaN, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
NaN
attr(,"se")
standard error
NaN
>
> # Sorensen-Dice computations from scratch, directly from gene lists
> data(allOncoGeneLists)
> ?allOncoGeneLists
allOncoGeneLists package:goSorensen R Documentation
_7 _g_e_n_e _l_i_s_t_s _p_o_s_s_i_b_l_y _r_e_l_a_t_e_d _w_i_t_h _c_a_n_c_e_r
_D_e_s_c_r_i_p_t_i_o_n:
An object of class "list" of length 7. Each one of its elements is
a "character" vector of gene identifiers. Only gene lists of
length almost 100 were taken from their source web. Take these
lists just as an illustrative example, they are not automatically
updated.
_U_s_a_g_e:
data(allOncoGeneLists)
_F_o_r_m_a_t:
An object of class "list" of length 7. Each one of its elements is
a "character" vector of gene identifiers.
_S_o_u_r_c_e:
<http://www.bushmanlab.org/links/genelists>
> data(humanEntrezIDs)
> # First, the mutual GO node enrichment tables are built, then computations
> # proceed from these contingency tables.
> # Building the contingency tables is a slow process (many enrichment tests)
> normTest <- equivTestSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
+ listNames = c("atlas", "sanger"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> normTest
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -7.1312, p-value = 4.975e-13
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3678388
sample estimates:
Sorensen dissimilarity
0.3448718
attr(,"se")
standard error
0.01396295
>
> # To perform a bootstrap test from scratch would be even slower:
> # set.seed(123)
> # bootTest <- equivTestSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # listNames = c("atlas", "sanger"),
> # boot = TRUE,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # bootTest
>
> # It is much faster to upgrade 'normTest' to be a bootstrap test:
> set.seed(123)
> bootTest <- upgrade(normTest, boot = TRUE)
> bootTest
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -7.1312, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3677033
sample estimates:
Sorensen dissimilarity
0.3448718
attr(,"se")
standard error
0.01396295
> # To know the number of planned bootstrap replicates:
> getNboot(bootTest)
[1] 10000
> # To know the number of valid bootstrap replicates:
> getEffNboot(bootTest)
[1] 10000
>
> # There are similar methods for dSorensen, seSorensen, duppSorensen, etc. to
> # compute directly from a pair of gene lists.
> # They are quite slow for the same reason as before (many enrichment tests).
> # dSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # listNames = c("atlas", "sanger"),
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # seSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # listNames = c("atlas", "sanger"),
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> #
> # duppSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # listNames = c("atlas", "sanger"),
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> #
> # set.seed(123)
> # duppSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # boot = TRUE,
> # listNames = c("atlas", "sanger"),
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # etc.
>
> # To build the contingency table first and then compute from it, may be a more flexible
> # and saving time strategy, in general:
> ?buildEnrichTable
buildEnrichTable package:goSorensen R Documentation
_C_r_e_a_t_e_s _a _2_x_2 _e_n_r_i_c_h_m_e_n_t _c_o_n_t_i_n_g_e_n_c_y _t_a_b_l_e _f_r_o_m _t_w_o _g_e_n_e _l_i_s_t_s, _o_r _a_l_l
_p_a_i_r_w_i_s_e _c_o_n_t_i_n_g_e_n_c_y _t_a_b_l_e_s _f_o_r _a "_l_i_s_t" _o_f _g_e_n_e _l_i_s_t_s.
_D_e_s_c_r_i_p_t_i_o_n:
Creates a 2x2 enrichment contingency table from two gene lists, or
all pairwise contingency tables for a "list" of gene lists.
_U_s_a_g_e:
buildEnrichTable(x, ...)
## Default S3 method:
buildEnrichTable(
x,
y,
listNames = c("gene.list1", "gene.list2"),
check.table = TRUE,
geneUniverse,
orgPackg,
onto,
GOLevel,
restricted = FALSE,
pAdjustMeth = "BH",
pvalCutoff = 0.01,
qvalCutoff = 0.05,
...
)
## S3 method for class 'character'
buildEnrichTable(
x,
y,
listNames = c("gene.list1", "gene.list2"),
check.table = TRUE,
geneUniverse,
orgPackg,
onto,
GOLevel,
restricted = FALSE,
pAdjustMeth = "BH",
pvalCutoff = 0.01,
qvalCutoff = 0.05,
...
)
## S3 method for class 'list'
buildEnrichTable(
x,
check.table = TRUE,
geneUniverse,
orgPackg,
onto,
GOLevel,
restricted = FALSE,
pAdjustMeth = "BH",
pvalCutoff = 0.01,
qvalCutoff = 0.05,
parallel = FALSE,
nOfCores = min(detectCores() - 1, length(x) - 1),
...
)
_A_r_g_u_m_e_n_t_s:
x: either an object of class "character" (or coerzable to
"character") representing a vector of gene identifiers or an
object of class "list". In this second case, each element of
the list must be a "character" vector of gene identifiers.
Then, all pairwise contingency tables between these gene
lists are built.
...: Additional parameters for internal use (not used for the
moment)
y: an object of class "character" (or coerzable to "character")
representing a vector of gene identifiers.
listNames: a character(2) with the gene lists names originating the
cross-tabulated enrichment frequencies.
check.table: Logical The resulting table must be checked. Defaults to
TRUE.
geneUniverse: character vector containing all genes from where
geneLists have been extracted.
orgPackg: A string with the name of the annotation package.
onto: string describing the ontology. Either "BP", "MF" or "CC".
GOLevel: An integer, the GO ontology level.
restricted: Logical variable to decide how tabulation of GOIDs is
performed. Defaults to FALSE. See the details section.
pAdjustMeth: string describing the adjust method, either "BH", "BY" or
"Bonf", defaults to 'BH'.
pvalCutoff: A numeric value. Defaults to 0.01.
qvalCutoff: A numeric value. Defaults to 0.05.
parallel: Logical. Defaults to FALSE but put it at TRUE for parallel
computation.
nOfCores: Number of cores for parallel computations. Only in "list"
interface.
_D_e_t_a_i_l_s:
Unrestricted tabulation crosses _all_ GO Terms located at the
level indicated by `GOLev` with the two GOIDs lists. Restricted
tabulation crosses only terms from the selected GO level that are
_common to ancestor terms of either list_. That is, if one term in
the selected GO level is not an ancestor of at least one of the
gene list most specific GO terms it is excluded from the GO
Level's terms because it is impossible that it appears as being
enriched.
_V_a_l_u_e:
in the "character" interface, an object of class "table" is
returned. It represents a 2x2 contingency table interpretable as
the cross-tabulation of the enriched GO items in two gene lists:
"Number of enriched items in list 1 (TRUE, FALSE)" x "Number of
enriched items in list 2 (TRUE, FALSE)". In the "list" interface,
the result is an object of class "tableList" with all pairwise
tables. Class "tableList" corresponds to objects representing all
mutual enrichment contingency tables generated in a pairwise
fashion: Given gene lists (i.e. "character" vectors of gene
identifiers) l1, l2, ..., lk, an object of class "tableList" is a
list of lists of contingency tables t(i,j) generated from each
pair of gene lists i and j, with the following structure:
$l2
$l2$l1$t(2,1)
$l3
$l3$l1$t(3,1), $l3$l2$t(3,2)
...
$lk
$lk$l1$t(k,1), $lk$l2$t(k,2), ..., $lk$l(k-1)t(K,k-1)
_M_e_t_h_o_d_s (_b_y _c_l_a_s_s):
• 'default': S3 default method
• 'character': S3 method for class "character"
• 'list': S3 method for class "list"
_E_x_a_m_p_l_e_s:
# Gene universe:
data(humanEntrezIDs)
# Gene lists to be explored for enrichment:
data(allOncoGeneLists)
?allOncoGeneLists
# Table of mutual GO node enrichment between gene lists Vogelstein and sanger,
# for ontology MF at GO level 6 (only first 50 genes, to improve speed).
vog.VS.sang <- buildEnrichTable(allOncoGeneLists[["Vogelstein"]][seq_len(50)],
allOncoGeneLists[["sanger"]][seq_len(50)],
geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
onto = "MF", GOLevel = 6, listNames = c("Vogelstein", "sanger"))
vog.VS.sang
# This is an inadequate table for Sorensen-Dice computations:
equivTestSorensen(vog.VS.sang)
# This sometimes happens, due too small gene lists or due to poor incidence
# of enrichment.
#
# In fact, the complete gene lists generate a much interesting contingency table:
# vog.VS.sang <- buildEnrichTable(allOncoGeneLists[["Vogelstein"]],
# allOncoGeneLists[["sanger"]],
# geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
# onto = "MF", GOLevel = 6, listNames = c("Vogelstein", "sanger"))
# vog.VS.sang
# equivTestSorensen(vog.VS.sang)
> tab <- buildEnrichTable(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
+ listNames = c("atlas", "sanger"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
>
> tab
Enriched in sanger
Enriched in atlas TRUE FALSE
TRUE 511 492
FALSE 46 9151
>
> # (Here, an obvious faster possibility would be to recover the enrichment contingency
> # table from the previous normal test result:)
> tab <- getTable(normTest)
> tab
Enriched in sanger
Enriched in atlas TRUE FALSE
TRUE 511 492
FALSE 46 9151
>
> tst <- equivTestSorensen(tab)
> tst
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -7.1312, p-value = 4.975e-13
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3678388
sample estimates:
Sorensen dissimilarity
0.3448718
attr(,"se")
standard error
0.01396295
> set.seed(123)
> bootTst <- equivTestSorensen(tab, boot = TRUE)
> bootTst
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -7.1312, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3677033
sample estimates:
Sorensen dissimilarity
0.3448718
attr(,"se")
standard error
0.01396295
>
> dSorensen(tab)
[1] 0.3448718
> seSorensen(tab)
[1] 0.01396295
> # or:
> getDissimilarity(tst)
Sorensen dissimilarity
0.3448718
attr(,"se")
standard error
0.01396295
>
> duppSorensen(tab)
[1] 0.3678388
> getUpper(tst)
dUpper
0.3678388
>
> set.seed(123)
> duppSorensen(tab, boot = TRUE)
[1] 0.3677033
attr(,"eff.nboot")
[1] 10000
> getUpper(bootTst)
dUpper
0.3677033
>
> # To perform from scratch all pairwise tests (or other Sorensen-Dice computations)
> # is even much slower. For example, all pairwise...
> # Dissimilarities:
> # # allPairDiss <- dSorensen(allOncoGeneLists,
> # # onto = "BP", GOLevel = 5,
> # # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # # allPairDiss
> #
> # # Still time consuming but faster: build all tables computing in parallel:
> # allPairDiss <- dSorensen(allOncoGeneLists,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
> # parallel = TRUE)
> # allPairDiss
>
> # Standard errors:
> # seSorensen(allOncoGeneLists,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> #
> # Upper confidence interval limits:
> # duppSorensen(allOncoGeneLists,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # All pairwise asymptotic normal tests:
> # allTests <- equivTestSorensen(allOncoGeneLists,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # getPvalue(allTests, simplify = FALSE)
> # getPvalue(allTests)
> # p.adjust(getPvalue(allTests), method = "holm")
> # To perform all pairwise bootstrap tests from scratch is (slightly)
> # even more time consuming:
> # set.seed(123)
> # allBootTests <- equivTestSorensen(allOncoGeneLists,
> # boot = TRUE,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # Not all bootstrap replicates may conduct to finite statistics:
> # getNboot(allBootTests)
>
> # Given the normal tests (object 'allTests'), it is much faster to upgrade
> # it to have the bootstrap tests:
> # set.seed(123)
> # allBootTests <- upgrade(allTests, boot = TRUE)
> # getPvalue(allBootTests, simplify = FALSE)
>
> # Again, the faster and more flexible possibility may be:
> # 1) First, build all pairwise enrichment contingency tables (slow first step):
> # allTabsBP.4 <- buildEnrichTable(allOncoGeneLists,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # allTabsBP.4
>
> # Better, directly use the dataset available at this package, goSorensen:
> data(allTabsBP.4)
> allTabsBP.4
$cangenes
$cangenes$atlas
Enriched in atlas
Enriched in cangenes TRUE FALSE
TRUE 1 0
FALSE 959 9397
$cis
$cis$atlas
Enriched in atlas
Enriched in cis TRUE FALSE
TRUE 181 11
FALSE 779 9386
$cis$cangenes
Enriched in cangenes
Enriched in cis TRUE FALSE
TRUE 1 191
FALSE 0 10165
$miscellaneous
$miscellaneous$atlas
Enriched in atlas
Enriched in miscellaneous TRUE FALSE
TRUE 450 57
FALSE 510 9340
$miscellaneous$cangenes
Enriched in cangenes
Enriched in miscellaneous TRUE FALSE
TRUE 1 506
FALSE 0 9850
$miscellaneous$cis
Enriched in cis
Enriched in miscellaneous TRUE FALSE
TRUE 145 362
FALSE 47 9803
$sanger
$sanger$atlas
Enriched in atlas
Enriched in sanger TRUE FALSE
TRUE 500 45
FALSE 460 9352
$sanger$cangenes
Enriched in cangenes
Enriched in sanger TRUE FALSE
TRUE 1 544
FALSE 0 9812
$sanger$cis
Enriched in cis
Enriched in sanger TRUE FALSE
TRUE 153 392
FALSE 39 9773
$sanger$miscellaneous
Enriched in miscellaneous
Enriched in sanger TRUE FALSE
TRUE 359 186
FALSE 148 9664
$Vogelstein
$Vogelstein$atlas
Enriched in atlas
Enriched in Vogelstein TRUE FALSE
TRUE 542 76
FALSE 418 9321
$Vogelstein$cangenes
Enriched in cangenes
Enriched in Vogelstein TRUE FALSE
TRUE 1 617
FALSE 0 9739
$Vogelstein$cis
Enriched in cis
Enriched in Vogelstein TRUE FALSE
TRUE 163 455
FALSE 29 9710
$Vogelstein$miscellaneous
Enriched in miscellaneous
Enriched in Vogelstein TRUE FALSE
TRUE 374 244
FALSE 133 9606
$Vogelstein$sanger
Enriched in sanger
Enriched in Vogelstein TRUE FALSE
TRUE 512 106
FALSE 33 9706
$waldman
$waldman$atlas
Enriched in atlas
Enriched in waldman TRUE FALSE
TRUE 641 138
FALSE 319 9259
$waldman$cangenes
Enriched in cangenes
Enriched in waldman TRUE FALSE
TRUE 1 778
FALSE 0 9578
$waldman$cis
Enriched in cis
Enriched in waldman TRUE FALSE
TRUE 171 608
FALSE 21 9557
$waldman$miscellaneous
Enriched in miscellaneous
Enriched in waldman TRUE FALSE
TRUE 467 312
FALSE 40 9538
$waldman$sanger
Enriched in sanger
Enriched in waldman TRUE FALSE
TRUE 446 333
FALSE 99 9479
$waldman$Vogelstein
Enriched in Vogelstein
Enriched in waldman TRUE FALSE
TRUE 488 291
FALSE 130 9448
attr(,"class")
[1] "tableList" "list"
> class(allTabsBP.4)
[1] "tableList" "list"
> # 2) Then perform all required computatios from these enrichment contingency tables...
> # All pairwise tests:
> allTests <- equivTestSorensen(allTabsBP.4)
> allTests
$cangenes
$cangenes$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 266.22, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.9979188
attr(,"se")
standard error
0.002079
$cis
$cis$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 13.583, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.7149878
sample estimates:
Sorensen dissimilarity
0.6857639
attr(,"se")
standard error
0.01776688
$cis$cangenes
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 52.885, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.9896373
attr(,"se")
standard error
0.010309
$miscellaneous
$miscellaneous$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -3.8685, p-value = 5.474e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.000000 0.411139
sample estimates:
Sorensen dissimilarity
0.3865031
attr(,"se")
standard error
0.01497758
$miscellaneous$cangenes
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 140.39, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.996063
attr(,"se")
standard error
0.003929258
$miscellaneous$cis
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 5.9904, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.000000 0.623749
sample estimates:
Sorensen dissimilarity
0.5851216
attr(,"se")
standard error
0.02348381
$sanger
$sanger$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -7.738, p-value = 5.051e-15
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3586962
sample estimates:
Sorensen dissimilarity
0.3355482
attr(,"se")
standard error
0.01407301
$sanger$cangenes
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 150.94, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.996337
attr(,"se")
standard error
0.003656295
$sanger$cis
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 6.1374, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6224203
sample estimates:
Sorensen dissimilarity
0.5848033
attr(,"se")
standard error
0.02286957
$sanger$miscellaneous
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -7.701, p-value = 6.75e-15
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3446065
sample estimates:
Sorensen dissimilarity
0.3174905
attr(,"se")
standard error
0.01648539
$Vogelstein
$Vogelstein$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -9.8173, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3350683
sample estimates:
Sorensen dissimilarity
0.3130545
attr(,"se")
standard error
0.01338347
$Vogelstein$cangenes
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 171.22, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.996769
attr(,"se")
standard error
0.003225798
$Vogelstein$cis
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 7.0238, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6333811
sample estimates:
Sorensen dissimilarity
0.5975309
attr(,"se")
standard error
0.02179538
$Vogelstein$miscellaneous
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -6.7191, p-value = 9.142e-12
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3618762
sample estimates:
Sorensen dissimilarity
0.3351111
attr(,"se")
standard error
0.01627199
$Vogelstein$sanger
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -32.259, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.1360863
sample estimates:
Sorensen dissimilarity
0.1195185
attr(,"se")
standard error
0.0100725
$waldman
$waldman$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -15.308, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.2823131
sample estimates:
Sorensen dissimilarity
0.2627947
attr(,"se")
standard error
0.01186635
$waldman$cangenes
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 215.94, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.9974359
attr(,"se")
standard error
0.002560815
$waldman$cis
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 10.327, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6801719
sample estimates:
Sorensen dissimilarity
0.6477858
attr(,"se")
standard error
0.01968936
$waldman$miscellaneous
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -12.16, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.2968117
sample estimates:
Sorensen dissimilarity
0.273717
attr(,"se")
standard error
0.01404058
$waldman$sanger
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -7.9582, p-value = 8.729e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3507062
sample estimates:
Sorensen dissimilarity
0.326284
attr(,"se")
standard error
0.01484766
$waldman$Vogelstein
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -10.211, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3244082
sample estimates:
Sorensen dissimilarity
0.3013601
attr(,"se")
standard error
0.01401229
attr(,"class")
[1] "equivSDhtestList" "list"
> class(allTests)
[1] "equivSDhtestList" "list"
> set.seed(123)
> allBootTests <- equivTestSorensen(allTabsBP.4, boot = TRUE)
> allBootTests
$cangenes
$cangenes$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (6298 effective bootstrap replicates of 10000)
data: tab
(d - d0) / se = 266.22, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.9979188
attr(,"se")
standard error
0.002079
$cis
$cis$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 13.583, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.7143131
sample estimates:
Sorensen dissimilarity
0.6857639
attr(,"se")
standard error
0.01776688
$cis$cangenes
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (6342 effective bootstrap replicates of 10000)
data: tab
(d - d0) / se = 52.885, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.9896373
attr(,"se")
standard error
0.010309
$miscellaneous
$miscellaneous$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -3.8685, p-value = 2e-04
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.4111369
sample estimates:
Sorensen dissimilarity
0.3865031
attr(,"se")
standard error
0.01497758
$miscellaneous$cangenes
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (6382 effective bootstrap replicates of 10000)
data: tab
(d - d0) / se = 140.39, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.996063
attr(,"se")
standard error
0.003929258
$miscellaneous$cis
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 5.9904, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6245189
sample estimates:
Sorensen dissimilarity
0.5851216
attr(,"se")
standard error
0.02348381
$sanger
$sanger$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -7.738, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3588894
sample estimates:
Sorensen dissimilarity
0.3355482
attr(,"se")
standard error
0.01407301
$sanger$cangenes
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (6403 effective bootstrap replicates of 10000)
data: tab
(d - d0) / se = 150.94, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.996337
attr(,"se")
standard error
0.003656295
$sanger$cis
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 6.1374, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6222143
sample estimates:
Sorensen dissimilarity
0.5848033
attr(,"se")
standard error
0.02286957
$sanger$miscellaneous
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -7.701, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3449603
sample estimates:
Sorensen dissimilarity
0.3174905
attr(,"se")
standard error
0.01648539
$Vogelstein
$Vogelstein$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -9.8173, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3357311
sample estimates:
Sorensen dissimilarity
0.3130545
attr(,"se")
standard error
0.01338347
$Vogelstein$cangenes
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (6273 effective bootstrap replicates of 10000)
data: tab
(d - d0) / se = 171.22, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.996769
attr(,"se")
standard error
0.003225798
$Vogelstein$cis
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 7.0238, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6328101
sample estimates:
Sorensen dissimilarity
0.5975309
attr(,"se")
standard error
0.02179538
$Vogelstein$miscellaneous
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -6.7191, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3629782
sample estimates:
Sorensen dissimilarity
0.3351111
attr(,"se")
standard error
0.01627199
$Vogelstein$sanger
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -32.259, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.1369488
sample estimates:
Sorensen dissimilarity
0.1195185
attr(,"se")
standard error
0.0100725
$waldman
$waldman$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -15.308, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.000000 0.282671
sample estimates:
Sorensen dissimilarity
0.2627947
attr(,"se")
standard error
0.01186635
$waldman$cangenes
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (6279 effective bootstrap replicates of 10000)
data: tab
(d - d0) / se = 215.94, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 1
sample estimates:
Sorensen dissimilarity
0.9974359
attr(,"se")
standard error
0.002560815
$waldman$cis
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 10.327, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6796177
sample estimates:
Sorensen dissimilarity
0.6477858
attr(,"se")
standard error
0.01968936
$waldman$miscellaneous
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -12.16, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.2973298
sample estimates:
Sorensen dissimilarity
0.273717
attr(,"se")
standard error
0.01404058
$waldman$sanger
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -7.9582, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3515402
sample estimates:
Sorensen dissimilarity
0.326284
attr(,"se")
standard error
0.01484766
$waldman$Vogelstein
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -10.211, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3243615
sample estimates:
Sorensen dissimilarity
0.3013601
attr(,"se")
standard error
0.01401229
attr(,"class")
[1] "equivSDhtestList" "list"
> class(allBootTests)
[1] "equivSDhtestList" "list"
> getPvalue(allBootTests, simplify = FALSE)
atlas cangenes cis miscellaneous sanger Vogelstein
atlas 0.00000000 1 1 0.00019998 9.999e-05 9.999e-05
cangenes 1.00000000 0 1 1.00000000 1.000e+00 1.000e+00
cis 1.00000000 1 0 1.00000000 1.000e+00 1.000e+00
miscellaneous 0.00019998 1 1 0.00000000 9.999e-05 9.999e-05
sanger 0.00009999 1 1 0.00009999 0.000e+00 9.999e-05
Vogelstein 0.00009999 1 1 0.00009999 9.999e-05 0.000e+00
waldman 0.00009999 1 1 0.00009999 9.999e-05 9.999e-05
waldman
atlas 9.999e-05
cangenes 1.000e+00
cis 1.000e+00
miscellaneous 9.999e-05
sanger 9.999e-05
Vogelstein 9.999e-05
waldman 0.000e+00
> getEffNboot(allBootTests)
cangenes.atlas cis.atlas cis.cangenes
6298 10000 6342
miscellaneous.atlas miscellaneous.cangenes miscellaneous.cis
10000 6382 10000
sanger.atlas sanger.cangenes sanger.cis
10000 6403 10000
sanger.miscellaneous Vogelstein.atlas Vogelstein.cangenes
10000 10000 6273
Vogelstein.cis Vogelstein.miscellaneous Vogelstein.sanger
10000 10000 10000
waldman.atlas waldman.cangenes waldman.cis
10000 6279 10000
waldman.miscellaneous waldman.sanger waldman.Vogelstein
10000 10000 10000
>
> # To adjust for testing multiplicity:
> p.adjust(getPvalue(allBootTests), method = "holm")
cangenes.atlas.p-value cis.atlas.p-value
1.00000000 1.00000000
cis.cangenes.p-value miscellaneous.atlas.p-value
1.00000000 0.00239976
miscellaneous.cangenes.p-value miscellaneous.cis.p-value
1.00000000 1.00000000
sanger.atlas.p-value sanger.cangenes.p-value
0.00209979 1.00000000
sanger.cis.p-value sanger.miscellaneous.p-value
1.00000000 0.00209979
Vogelstein.atlas.p-value Vogelstein.cangenes.p-value
0.00209979 1.00000000
Vogelstein.cis.p-value Vogelstein.miscellaneous.p-value
1.00000000 0.00209979
Vogelstein.sanger.p-value waldman.atlas.p-value
0.00209979 0.00209979
waldman.cangenes.p-value waldman.cis.p-value
1.00000000 1.00000000
waldman.miscellaneous.p-value waldman.sanger.p-value
0.00209979 0.00209979
waldman.Vogelstein.p-value
0.00209979
>
> # If only partial statistics are desired:
> dSorensen(allTabsBP.4)
atlas cangenes cis miscellaneous sanger Vogelstein
atlas 0.0000000 0.9979188 0.6857639 0.3865031 0.3355482 0.3130545
cangenes 0.9979188 0.0000000 0.9896373 0.9960630 0.9963370 0.9967690
cis 0.6857639 0.9896373 0.0000000 0.5851216 0.5848033 0.5975309
miscellaneous 0.3865031 0.9960630 0.5851216 0.0000000 0.3174905 0.3351111
sanger 0.3355482 0.9963370 0.5848033 0.3174905 0.0000000 0.1195185
Vogelstein 0.3130545 0.9967690 0.5975309 0.3351111 0.1195185 0.0000000
waldman 0.2627947 0.9974359 0.6477858 0.2737170 0.3262840 0.3013601
waldman
atlas 0.2627947
cangenes 0.9974359
cis 0.6477858
miscellaneous 0.2737170
sanger 0.3262840
Vogelstein 0.3013601
waldman 0.0000000
> duppSorensen(allTabsBP.4)
atlas cangenes cis miscellaneous sanger Vogelstein
atlas 0.0000000 1 0.7149878 0.4111390 0.3586962 0.3350683
cangenes 1.0000000 0 1.0000000 1.0000000 1.0000000 1.0000000
cis 0.7149878 1 0.0000000 0.6237490 0.6224203 0.6333811
miscellaneous 0.4111390 1 0.6237490 0.0000000 0.3446065 0.3618762
sanger 0.3586962 1 0.6224203 0.3446065 0.0000000 0.1360863
Vogelstein 0.3350683 1 0.6333811 0.3618762 0.1360863 0.0000000
waldman 0.2823131 1 0.6801719 0.2968117 0.3507062 0.3244082
waldman
atlas 0.2823131
cangenes 1.0000000
cis 0.6801719
miscellaneous 0.2968117
sanger 0.3507062
Vogelstein 0.3244082
waldman 0.0000000
> seSorensen(allTabsBP.4)
atlas cangenes cis miscellaneous sanger
atlas 0.00000000 0.002079000 0.01776688 0.014977580 0.014073007
cangenes 0.00207900 0.000000000 0.01030900 0.003929258 0.003656295
cis 0.01776688 0.010309002 0.00000000 0.023483807 0.022869567
miscellaneous 0.01497758 0.003929258 0.02348381 0.000000000 0.016485388
sanger 0.01407301 0.003656295 0.02286957 0.016485388 0.000000000
Vogelstein 0.01338347 0.003225798 0.02179538 0.016271992 0.010072500
waldman 0.01186635 0.002560815 0.01968936 0.014040581 0.014847661
Vogelstein waldman
atlas 0.013383469 0.011866345
cangenes 0.003225798 0.002560815
cis 0.021795381 0.019689356
miscellaneous 0.016271992 0.014040581
sanger 0.010072500 0.014847661
Vogelstein 0.000000000 0.014012289
waldman 0.014012289 0.000000000
>
>
> # Tipically, in a real study it would be interesting to scan tests
> # along some ontologies and levels inside these ontologies:
> # (which obviously will be a quite slow process)
> # gc()
> # set.seed(123)
> # allBootTests_BP_MF_lev4to8 <- allEquivTestSorensen(allOncoGeneLists,
> # boot = TRUE,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
> # ontos = c("BP", "MF"), GOLevels = 4:8)
> # getPvalue(allBootTests_BP_MF_lev4to8)
> # getEffNboot(allBootTests_BP_MF_lev4to8)
>
> proc.time()
user system elapsed
182.712 8.124 293.669
goSorensen.Rcheck/tests/test_nonsense_genes.Rout
R version 4.3.1 (2023-06-16) -- "Beagle Scouts"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(goSorensen)
Attaching package: 'goSorensen'
The following object is masked from 'package:utils':
upgrade
>
> testError <- function(e) {return(e)}
>
> tryCatch(dSorensen("Sec1", onto = "BP"), error = testError)
<simpleError in buildEnrichTable.character(x, y, check.table = check.table, ...): Argument 'y' is missing, 'x' and 'y' must be 'character' vectors of valid gene identifiers>
>
> data(allOncoGeneLists)
> ?allOncoGeneLists
allOncoGeneLists package:goSorensen R Documentation
_7 _g_e_n_e _l_i_s_t_s _p_o_s_s_i_b_l_y _r_e_l_a_t_e_d _w_i_t_h _c_a_n_c_e_r
_D_e_s_c_r_i_p_t_i_o_n:
An object of class "list" of length 7. Each one of its elements is
a "character" vector of gene identifiers. Only gene lists of
length almost 100 were taken from their source web. Take these
lists just as an illustrative example, they are not automatically
updated.
_U_s_a_g_e:
data(allOncoGeneLists)
_F_o_r_m_a_t:
An object of class "list" of length 7. Each one of its elements is
a "character" vector of gene identifiers.
_S_o_u_r_c_e:
<http://www.bushmanlab.org/links/genelists>
> data(humanEntrezIDs)
>
> # Non-sense random gene lists. Generating Entrez-like gene identifiers, but random:
> set.seed(1234567)
> genList1 <- unique(as.character(sample.int(99999, size = 100)))
> genList2 <- unique(as.character(sample.int(99999, size = 100)))
> # Gene identifiers are numbers like Entrez identifiers at 'humanEntrezIDs', but random.
> dSorensen(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
[1] NaN
> duppSorensen(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
[1] NaN
> seSorensen(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
[1] NaN
> nonSenseTst <- equivTestSorensen(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> nonSenseTst
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = NaN, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
NaN
attr(,"se")
standard error
NaN
> tab <- getTable(nonSenseTst)
> tab
Enriched in genList2
Enriched in genList1 TRUE FALSE
TRUE 0 0
FALSE 0 10200
> # Or, alternatively:
> tab <- buildEnrichTable(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> tab
Enriched in genList2
Enriched in genList1 TRUE FALSE
TRUE 0 0
FALSE 0 10200
> dSorensen(tab)
[1] NaN
> duppSorensen(tab)
[1] NaN
> seSorensen(tab)
[1] NaN
> equivTestSorensen(tab)
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = NaN, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
NaN
attr(,"se")
standard error
NaN
>
> # Even more non-sense, letters non numeric-style like those at 'humanEntrezIDs':
> set.seed(1234567)
> genList1 <- unique(vapply(seq_len(100), function(i) {
+ paste0(sample(c(letters, LETTERS), 6, replace = TRUE), collapse = "")
+ }, FUN.VALUE = character(1)))
> genList2 <- unique(vapply(seq_len(100), function(i) {
+ paste0(sample(c(letters, LETTERS), 6, replace = TRUE), collapse = "")
+ }, FUN.VALUE = character(1)))
>
> # Gene identifiers incompatible with those at 'humanEntrezIDs':
> dSorensen(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
--> No gene can be mapped....
--> Expected input gene ID: 3400,8614,222698,2683,284359,56979
--> return NULL...
--> No gene can be mapped....
--> Expected input gene ID: 55231,132612,3479,342977,84688,9289
--> return NULL...
[1] NaN
> duppSorensen(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
--> No gene can be mapped....
--> Expected input gene ID: 2068,22803,221400,734,51433,373863
--> return NULL...
--> No gene can be mapped....
--> Expected input gene ID: 84464,10007,54456,5872,91603,84072
--> return NULL...
[1] NaN
> seSorensen(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
--> No gene can be mapped....
--> Expected input gene ID: 100125288,151254,23641,5892,4986,128637
--> return NULL...
--> No gene can be mapped....
--> Expected input gene ID: 1082,431705,51298,57446,117144,6781
--> return NULL...
[1] NaN
> nonSenseTst <- equivTestSorensen(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
--> No gene can be mapped....
--> Expected input gene ID: 2192,4342,6118,2069,8747,207
--> return NULL...
--> No gene can be mapped....
--> Expected input gene ID: 51361,5810,23626,79915,147912,7314
--> return NULL...
> nonSenseTst
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = NaN, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
NaN
attr(,"se")
standard error
NaN
> tab <- getTable(nonSenseTst)
> tab
Enriched in genList2
Enriched in genList1 TRUE FALSE
TRUE 0 0
FALSE 0 10200
> # Or, alternatively:
> tab <- buildEnrichTable(genList1, genList2,
+ listNames = c("genList1", "genList2"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
--> No gene can be mapped....
--> Expected input gene ID: 581,84519,23318,23705,91,409
--> return NULL...
--> No gene can be mapped....
--> Expected input gene ID: 84231,49,133308,406949,92667,55063
--> return NULL...
> tab
Enriched in genList2
Enriched in genList1 TRUE FALSE
TRUE 0 0
FALSE 0 10200
> dSorensen(tab)
[1] NaN
> duppSorensen(tab)
[1] NaN
> seSorensen(tab)
[1] NaN
> equivTestSorensen(tab)
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = NaN, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
NaN
attr(,"se")
standard error
NaN
>
> proc.time()
user system elapsed
288.182 7.528 450.650
goSorensen.Rcheck/goSorensen-Ex.timings
| name | user | system | elapsed | |
| allEquivTestSorensen | 0.000 | 0.001 | 0.000 | |
| buildEnrichTable | 20.630 | 1.793 | 34.399 | |
| dSorensen | 0.115 | 0.078 | 0.342 | |
| duppSorensen | 0.198 | 0.050 | 0.412 | |
| equivTestSorensen | 0.143 | 0.005 | 0.229 | |
| getDissimilarity | 0.280 | 0.118 | 0.658 | |
| getEffNboot | 1.318 | 0.063 | 2.139 | |
| getNboot | 1.309 | 0.058 | 2.124 | |
| getPvalue | 0.278 | 0.120 | 0.675 | |
| getSE | 0.285 | 0.121 | 0.677 | |
| getTable | 0.315 | 0.146 | 0.794 | |
| getUpper | 0.266 | 0.115 | 0.634 | |
| nice2x2Table | 0.003 | 0.001 | 0.006 | |
| pbtAllOntosAndLevels | 0.122 | 0.052 | 0.299 | |
| seSorensen | 0.002 | 0.002 | 0.006 | |
| upgrade | 0.883 | 0.247 | 1.814 | |