createAssoPerm: Create and store association matrices for permuted data

In stefpeschel/NetCoMi: Network Construction and Comparison for Microbiome Data

Create and store association matrices for permuted data

Description

The 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 by netConstruct or netAnalyze).
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. If FALSE, 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 if fileLoadCountsPerm is not NULL.
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 NULL so that no log file is generated.
verbose	logical. If TRUE (default), status messages are shown.

Value

Invisible object: Matrix with permuted group labels.

Examples

  # 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")

  # 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)
  
  # 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)
                            
  # Run netcompare using the stored permutation association matrices:
  amgut_comp2 <- netCompare(amgut_props, permTest = TRUE, nPerm = 100L, 
                            fileLoadAssoPerm = "assoPerm")
  
  summary(amgut_comp1)
  summary(amgut_comp2)
  all.equal(amgut_comp1$properties, amgut_comp2$properties)
  
  # Run diffnet using the stored permutation count matrices in diffnet()
  diff1 <- diffnet(amgut_net, diffMethod = "permute", nPerm = 100L, 
                  fileLoadCountsPerm = c("countsPerm1", "countsPerm2"))
                  
  # Run diffnet using the stored permutation association matrices 
  diff2 <- diffnet(amgut_net, diffMethod = "permute", nPerm = 100L, 
                  fileLoadAssoPerm = "assoPerm")
                 
 #plot(diff1)
 #plot(diff2)
 # Note: Networks are empty (no significantly different associations) 
 # for only 100 permutations

stefpeschel/NetCoMi documentation built on Nov. 12, 2024, 7:12 a.m.