Create and store association matrices for permuted data
Source:R/createAssoPerm.R
createAssoPerm.RdThe function creates and returns a matrix with permuted group labels and saves association matrices computed for the permuted data to an external file.
Usage
createAssoPerm(
x,
computeAsso = TRUE,
nPerm = 1000L,
cores = 1L,
seed = NULL,
permGroupMat = NULL,
fileStoreAssoPerm = "assoPerm",
append = TRUE,
storeCountsPerm = FALSE,
fileStoreCountsPerm = c("countsPerm1", "countsPerm2"),
logFile = NULL,
verbose = TRUE
)Arguments
- x
object of class
"microNet"or"microNetProps"(returned bynetConstructornetAnalyze).- computeAsso
logical indicating whether the association matrices should be computed. If
FALSE, only the permuted group labels are computed and returned.- nPerm
integer indicating the number of permutations.
- cores
integer indicating the number of CPU cores used for permutation tests. If cores > 1, the tests are performed in parallel. Is limited to the number of available CPU cores determined by
detectCores. Defaults to 1L (no parallelization).- seed
integer giving a seed for reproducibility of the results.
- permGroupMat
an optional matrix with permuted group labels (with nPerm rows and n1+n2 columns).
- fileStoreAssoPerm
character giving the name of a file to which the matrix with associations/dissimilarities of the permuted data is saved. Can also be a path.
- append
logical indicating whether existing files (given by fileStoreAssoPerm and fileStoreCountsPerm) should be extended. If
TRUE, a new file is created only if the file is not existing. IfFALSE, a new file is created in any case.- storeCountsPerm
logical indicating whether the permuted count matrices should be saved to an external file. Defaults to
FALSE. Ignored iffileLoadCountsPermis notNULL.- fileStoreCountsPerm
character vector with two elements giving the names of two files storing the permuted count matrices belonging to the two groups.
- logFile
character string naming the log file to which the current iteration number is written. Defaults to
NULLso that no log file is generated.- verbose
logical. If
TRUE(default), status messages are shown.
Examples
# \donttest{
# Load data sets from American Gut Project (from SpiecEasi package)
data("amgut1.filt")
# Generate a random group vector
set.seed(123456)
group <- sample(1:2, nrow(amgut1.filt), replace = TRUE)
# Network construction:
amgut_net <- netConstruct(amgut1.filt, group = group,
measure = "pearson",
filtTax = "highestVar",
filtTaxPar = list(highestVar = 30),
zeroMethod = "pseudoZO", normMethod = "clr")
#> Checking input arguments ...
#> Done.
#> Data filtering ...
#> 97 taxa removed.
#> 30 taxa and 289 samples remaining.
#>
#> Zero treatment:
#> Zero counts replaced by 1
#>
#> Normalization:
#> Execute clr(){SpiecEasi} ...
#> Done.
#>
#> Calculate 'pearson' associations ...
#> Done.
#>
#> Calculate associations in group 2 ...
#> Done.
#>
#> Sparsify associations via 't-test' ...
#>
#> Adjust for multiple testing via 'adaptBH' ...
#> Done.
#> Done.
#>
#> Sparsify associations in group 2 ...
#>
#> Adjust for multiple testing via 'adaptBH' ...
#> Done.
#> Done.
# Network analysis:
amgut_props <- netAnalyze(amgut_net, clustMethod = "cluster_fast_greedy")
# Use 'createAssoPerm' to create "permuted" count and association matrices,
# which can be reused by netCompare() and diffNet()
# Note:
# createAssoPerm() accepts objects 'amgut_net' and 'amgut_props' as input
createAssoPerm(amgut_props, nPerm = 100L,
computeAsso = TRUE,
fileStoreAssoPerm = "assoPerm",
storeCountsPerm = TRUE,
fileStoreCountsPerm = c("countsPerm1", "countsPerm2"),
append = FALSE, seed = 123456)
#> Create matrix with permuted group labels ...
#> Done.
#> Files 'assoPerm.bmat and assoPerm.desc.txt created.
#> Files 'countsPerm1.bmat, countsPerm1.desc.txt, countsPerm2.bmat, and countsPerm2.desc.txt created.
#> Compute permutation associations ...
#>
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#> Loading required package: dynamicTreeCut
#> Loading required package: fastcluster
#>
#> Attaching package: ‘fastcluster’
#> The following object is masked from ‘package:stats’:
#>
#> hclust
#>
#> Attaching package: ‘WGCNA’
#> The following object is masked from ‘package:stats’:
#>
#> cor
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.6-8
#>
#> Attaching package: ‘LaplacesDemon’
#> The following object is masked from ‘package:permute’:
#>
#> Blocks
#> Loading required package: S4Vectors
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#>
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#>
#> Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#> as.data.frame, basename, cbind, colnames, dirname, do.call,
#> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#> pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table,
#> tapply, union, unique, unsplit, which.max, which.min
#>
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#>
#> findMatches
#> The following objects are masked from ‘package:base’:
#>
#> I, expand.grid, unname
#> Loading required package: IRanges
#>
#> Attaching package: ‘IRanges’
#> The following object is masked from ‘package:phyloseq’:
#>
#> distance
#> Loading required package: GenomicRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> Loading required package: Biobase
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#>
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#>
#> rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#>
#> anyMissing, rowMedians
#> The following object is masked from ‘package:phyloseq’:
#>
#> sampleNames
#>
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#> Done.
# Run netcompare using the stored permutation count matrices
# (association matrices are still computed within netCompare):
amgut_comp1 <- netCompare(amgut_props, permTest = TRUE, nPerm = 100L,
fileLoadCountsPerm = c("countsPerm1",
"countsPerm2"),
seed = 123456)
#> Checking input arguments ...
#> Done.
#> Calculate network properties ...
#> Done.
#> Execute permutation tests ...
#>
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#> Done.
#> Calculating p-values ...
#> Done.
#> Adjust for multiple testing using 'adaptBH' ...
#> Done.
# Run netcompare using the stored permutation association matrices:
amgut_comp2 <- netCompare(amgut_props, permTest = TRUE, nPerm = 100L,
fileLoadAssoPerm = "assoPerm")
#> Checking input arguments ...
#> Done.
#> Calculate network properties ...
#> Done.
#> Execute permutation tests ...
#>
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#> Done.
#> Calculating p-values ...
#> Done.
#> Adjust for multiple testing using 'adaptBH' ...
#> Done.
summary(amgut_comp1)
#>
#> Comparison of Network Properties
#> ----------------------------------
#> CALL:
#> netCompare(x = amgut_props, permTest = TRUE, nPerm = 100, seed = 123456,
#> fileLoadCountsPerm = c("countsPerm1", "countsPerm2"))
#>
#> ______________________________
#> Global network properties
#> `````````````````````````
#> Largest connected component (LCC):
#> group '1' group '2' abs.diff. p-value
#> Relative LCC size 0.900 0.967 0.067 0.584158
#> Clustering coefficient 0.522 0.394 0.128 0.277228
#> Modularity 0.269 0.209 0.060 0.584158
#> Positive edge percentage 43.023 32.432 10.591 0.039604 *
#> Edge density 0.245 0.182 0.063 0.346535
#> Natural connectivity 0.062 0.052 0.010 0.198020
#> Vertex connectivity 1.000 1.000 0.000 1.000000
#> Edge connectivity 1.000 1.000 0.000 1.000000
#> Average dissimilarity* 0.927 0.949 0.021 0.217822
#> Average path length** 1.624 1.819 0.195 0.435644
#>
#> Whole network:
#> group '1' group '2' abs.diff. p-value
#> Number of components 4.000 2.000 2.000 0.544554
#> Clustering coefficient 0.522 0.394 0.128 0.277228
#> Modularity 0.269 0.209 0.060 0.594059
#> Positive edge percentage 43.023 32.432 10.591 0.029703 *
#> Edge density 0.198 0.170 0.028 0.752475
#> Natural connectivity 0.054 0.049 0.004 0.613861
#> -----
#> p-values: one-tailed test with null hypothesis diff=0
#> *: Dissimilarity = 1 - edge weight
#> **: Path length = Units with average dissimilarity
#>
#> ______________________________
#> Jaccard index (similarity betw. sets of most central nodes)
#> ```````````````````````````````````````````````````````````
#> Jacc P(<=Jacc) P(>=Jacc)
#> degree 0.333 0.631521 0.606925
#> betweenness centr. 0.333 0.631521 0.606925
#> closeness centr. 0.600 0.980338 0.076564 .
#> eigenvec. centr. 0.778 0.999035 0.008281 **
#> hub taxa 0.000 0.197531 1.000000
#> -----
#> Jaccard index in [0,1] (1 indicates perfect agreement)
#>
#> ______________________________
#> Adjusted Rand index (similarity betw. clusterings)
#> ``````````````````````````````````````````````````
#> wholeNet LCC
#> ARI 0.095 0.095
#> p-value 0.047 0.064
#> -----
#> ARI in [-1,1] with ARI=1: perfect agreement betw. clusterings
#> ARI=0: expected for two random clusterings
#> p-value: permutation test (n=1000) with null hypothesis ARI=0
#>
#> ______________________________
#> Graphlet Correlation Distance
#> `````````````````````````````
#> wholeNet LCC
#> GCD 0.421000 0.945000
#> p-value 0.990099 0.811881
#> -----
#> GCD >= 0 (GCD=0 indicates perfect agreement between GCMs)
#> p-value: permutation test with null hypothesis GCD=0
#>
#> ______________________________
#> Centrality measures
#> - In decreasing order
#> - Centrality of disconnected components is zero
#> ````````````````````````````````````````````````
#> Degree (normalized):
#> group '1' group '2' abs.diff. adj.p-value
#> 181095 0.207 0.000 0.207 0.965347
#> 158660 0.414 0.241 0.172 0.965347
#> 301645 0.414 0.241 0.172 0.965347
#> 130663 0.069 0.207 0.138 0.965347
#> 331820 0.241 0.103 0.138 0.965347
#> 326977 0.207 0.069 0.138 0.965347
#> 364563 0.241 0.345 0.103 0.965347
#> 322235 0.310 0.207 0.103 0.965347
#> 353985 0.138 0.034 0.103 0.965347
#> 470973 0.138 0.034 0.103 0.965347
#>
#> Betweenness centrality (normalized):
#> group '1' group '2' abs.diff. adj.p-value
#> 130663 0.000 0.169 0.169 0.878790
#> 512309 0.000 0.124 0.124 0.878790
#> 181095 0.105 0.000 0.105 0.292930
#> 326792 0.098 0.000 0.098 0.878790
#> 326977 0.086 0.000 0.086 0.894207
#> 331820 0.000 0.071 0.071 0.894207
#> 248140 0.095 0.161 0.066 0.894207
#> 188236 0.062 0.124 0.063 0.894207
#> 361496 0.000 0.058 0.058 0.894207
#> 9753 0.258 0.206 0.052 0.894207
#>
#> Closeness centrality (normalized):
#> group '1' group '2' abs.diff. adj.p-value
#> 181095 0.733 0.000 0.733 0.933522
#> 259569 0.000 0.573 0.573 0.933522
#> 127309 0.000 0.443 0.443 0.933522
#> 549871 0.000 0.394 0.394 0.933522
#> 470973 0.695 0.480 0.216 0.933522
#> 331820 0.764 0.568 0.197 0.933522
#> 353985 0.717 0.530 0.187 0.933522
#> 541301 0.621 0.448 0.173 0.933522
#> 158660 0.935 0.777 0.157 0.933522
#> 244304 0.631 0.486 0.145 0.933522
#>
#> Eigenvector centrality (normalized):
#> group '1' group '2' abs.diff. adj.p-value
#> 331820 0.504 0.101 0.402 0.866062
#> 301645 1.000 0.698 0.302 0.283710
#> 158660 0.639 0.352 0.287 0.866062
#> 181095 0.242 0.000 0.242 0.496493
#> 307981 0.993 0.762 0.231 0.496493
#> 364563 0.564 0.762 0.198 0.866062
#> 326977 0.354 0.159 0.196 0.866062
#> 353985 0.246 0.063 0.183 0.866062
#> 188236 0.621 0.763 0.142 0.866062
#> 259569 0.000 0.132 0.132 0.866062
#>
#> _________________________________________________________
#> Significance codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1
summary(amgut_comp2)
#>
#> Comparison of Network Properties
#> ----------------------------------
#> CALL:
#> netCompare(x = amgut_props, permTest = TRUE, nPerm = 100, fileLoadAssoPerm = "assoPerm")
#>
#> ______________________________
#> Global network properties
#> `````````````````````````
#> Largest connected component (LCC):
#> group '1' group '2' abs.diff. p-value
#> Relative LCC size 0.900 0.967 0.067 0.584158
#> Clustering coefficient 0.522 0.394 0.128 0.277228
#> Modularity 0.269 0.209 0.060 0.584158
#> Positive edge percentage 43.023 32.432 10.591 0.039604 *
#> Edge density 0.245 0.182 0.063 0.346535
#> Natural connectivity 0.062 0.052 0.010 0.198020
#> Vertex connectivity 1.000 1.000 0.000 1.000000
#> Edge connectivity 1.000 1.000 0.000 1.000000
#> Average dissimilarity* 0.927 0.949 0.021 0.217822
#> Average path length** 1.624 1.819 0.195 0.435644
#>
#> Whole network:
#> group '1' group '2' abs.diff. p-value
#> Number of components 4.000 2.000 2.000 0.544554
#> Clustering coefficient 0.522 0.394 0.128 0.277228
#> Modularity 0.269 0.209 0.060 0.594059
#> Positive edge percentage 43.023 32.432 10.591 0.029703 *
#> Edge density 0.198 0.170 0.028 0.752475
#> Natural connectivity 0.054 0.049 0.004 0.613861
#> -----
#> p-values: one-tailed test with null hypothesis diff=0
#> *: Dissimilarity = 1 - edge weight
#> **: Path length = Units with average dissimilarity
#>
#> ______________________________
#> Jaccard index (similarity betw. sets of most central nodes)
#> ```````````````````````````````````````````````````````````
#> Jacc P(<=Jacc) P(>=Jacc)
#> degree 0.333 0.631521 0.606925
#> betweenness centr. 0.333 0.631521 0.606925
#> closeness centr. 0.600 0.980338 0.076564 .
#> eigenvec. centr. 0.778 0.999035 0.008281 **
#> hub taxa 0.000 0.197531 1.000000
#> -----
#> Jaccard index in [0,1] (1 indicates perfect agreement)
#>
#> ______________________________
#> Adjusted Rand index (similarity betw. clusterings)
#> ``````````````````````````````````````````````````
#> wholeNet LCC
#> ARI 0.095 0.095
#> p-value 0.051 0.057
#> -----
#> ARI in [-1,1] with ARI=1: perfect agreement betw. clusterings
#> ARI=0: expected for two random clusterings
#> p-value: permutation test (n=1000) with null hypothesis ARI=0
#>
#> ______________________________
#> Graphlet Correlation Distance
#> `````````````````````````````
#> wholeNet LCC
#> GCD 0.421000 0.945000
#> p-value 0.990099 0.811881
#> -----
#> GCD >= 0 (GCD=0 indicates perfect agreement between GCMs)
#> p-value: permutation test with null hypothesis GCD=0
#>
#> ______________________________
#> Centrality measures
#> - In decreasing order
#> - Centrality of disconnected components is zero
#> ````````````````````````````````````````````````
#> Degree (normalized):
#> group '1' group '2' abs.diff. adj.p-value
#> 181095 0.207 0.000 0.207 0.965347
#> 158660 0.414 0.241 0.172 0.965347
#> 301645 0.414 0.241 0.172 0.965347
#> 130663 0.069 0.207 0.138 0.965347
#> 331820 0.241 0.103 0.138 0.965347
#> 326977 0.207 0.069 0.138 0.965347
#> 364563 0.241 0.345 0.103 0.965347
#> 322235 0.310 0.207 0.103 0.965347
#> 353985 0.138 0.034 0.103 0.965347
#> 470973 0.138 0.034 0.103 0.965347
#>
#> Betweenness centrality (normalized):
#> group '1' group '2' abs.diff. adj.p-value
#> 130663 0.000 0.169 0.169 0.878790
#> 512309 0.000 0.124 0.124 0.878790
#> 181095 0.105 0.000 0.105 0.292930
#> 326792 0.098 0.000 0.098 0.878790
#> 326977 0.086 0.000 0.086 0.894207
#> 331820 0.000 0.071 0.071 0.894207
#> 248140 0.095 0.161 0.066 0.894207
#> 188236 0.062 0.124 0.063 0.894207
#> 361496 0.000 0.058 0.058 0.894207
#> 9753 0.258 0.206 0.052 0.894207
#>
#> Closeness centrality (normalized):
#> group '1' group '2' abs.diff. adj.p-value
#> 181095 0.733 0.000 0.733 0.933522
#> 259569 0.000 0.573 0.573 0.933522
#> 127309 0.000 0.443 0.443 0.933522
#> 549871 0.000 0.394 0.394 0.933522
#> 470973 0.695 0.480 0.216 0.933522
#> 331820 0.764 0.568 0.197 0.933522
#> 353985 0.717 0.530 0.187 0.933522
#> 541301 0.621 0.448 0.173 0.933522
#> 158660 0.935 0.777 0.157 0.933522
#> 244304 0.631 0.486 0.145 0.933522
#>
#> Eigenvector centrality (normalized):
#> group '1' group '2' abs.diff. adj.p-value
#> 331820 0.504 0.101 0.402 0.866062
#> 301645 1.000 0.698 0.302 0.283710
#> 158660 0.639 0.352 0.287 0.866062
#> 181095 0.242 0.000 0.242 0.496493
#> 307981 0.993 0.762 0.231 0.496493
#> 364563 0.564 0.762 0.198 0.866062
#> 326977 0.354 0.159 0.196 0.866062
#> 353985 0.246 0.063 0.183 0.866062
#> 188236 0.621 0.763 0.142 0.866062
#> 259569 0.000 0.132 0.132 0.866062
#>
#> _________________________________________________________
#> Significance codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1
all.equal(amgut_comp1$properties, amgut_comp2$properties)
#> [1] TRUE
# Run diffnet using the stored permutation count matrices in diffnet()
diff1 <- diffnet(amgut_net, diffMethod = "permute", nPerm = 100L,
fileLoadCountsPerm = c("countsPerm1", "countsPerm2"))
#> Checking input arguments ...
#> Done.
#> Execute permutation tests ...
#>
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#> Adjust for multiple testing using 'lfdr' ...
#>
#> Execute fdrtool() ...
#> Step 1... determine cutoff point
#> Step 2... estimate parameters of null distribution and eta0
#> Step 3... compute p-values and estimate empirical PDF/CDF
#> Step 4... compute q-values and local fdr
#>
#> Done.
#> No significant differential associations detected after multiple testing adjustment.
# Run diffnet using the stored permutation association matrices
diff2 <- diffnet(amgut_net, diffMethod = "permute", nPerm = 100L,
fileLoadAssoPerm = "assoPerm")
#> Checking input arguments ...
#> Done.
#> Execute permutation tests ...
#>
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#> Adjust for multiple testing using 'lfdr' ...
#>
#> Execute fdrtool() ...
#> Step 1... determine cutoff point
#> Step 2... estimate parameters of null distribution and eta0
#> Step 3... compute p-values and estimate empirical PDF/CDF
#> Step 4... compute q-values and local fdr
#>
#> Done.
#> No significant differential associations detected after multiple testing adjustment.
#plot(diff1)
#plot(diff2)
# Note: Networks are empty (no significantly different associations)
# for only 100 permutations
# }