barebonesCoNet: Barebones R implementation of CoNet

In hallucigenia-sparsa/seqgroup: Analysis of Sequencing Data with Groups

Barebones R implementation of CoNet

Description

Build a network using Pearson, Spearman, Euclidean, Kullback-Leibler and/or Bray-Curtis. The original CoNet implementation with extended functionality is available at: http://systemsbiology.vub.ac.be/conet. For export of data to the original CoNet, see exportToCoNet, for a generic network building function wrapping barebonesCoNet and other network inference methods, see buildNetwork.

Usage

barebonesCoNet(
  abundances,
  metadata = NULL,
  methods = c("spearman", "kld"),
  T.up = NA,
  T.down = NA,
  method.num.T = 2,
  pval.T = 0.05,
  init.edge.num = max(2, round(sqrt(nrow(abundances)))),
  min.occ = round(ncol(abundances)/3),
  keep.filtered = TRUE,
  norm = FALSE,
  stand.rows = FALSE,
  clr = FALSE,
  pval.cor = FALSE,
  permut = FALSE,
  columnwise = FALSE,
  renorm = FALSE,
  permutandboot = FALSE,
  iters = 100,
  bh = TRUE,
  pseudocount = 1e-11,
  plot = FALSE,
  verbose = FALSE
)

Arguments

abundances	a matrix with taxa as rows and samples as columns
metadata	an optional data frame with metadata items as columns, where samples are in the same order as in abundances and all items are numeric; a bipartite network will be computed
methods	network construction methods, values can be combinations of: "pearson", "spearman", "kld", "euclid" or "bray"; note that the latter two are not defined for negative abundance values
T.up	upper threshold for scores (when more than one network construction method is provided, init.edge.num is given and/or p-values are computed, T.up is ignored)
T.down	lower threshold for scores (when more than one network construction method is provided, init.edge.num is given and/or p-values are computed, T.down is ignored)
method.num.T	threshold on method number (only used when more than one method is provided)
pval.T	threshold on p-value (only used when permut, permutandboot or pval.cor is true); if several methods are provided, only applied after merge
init.edge.num	the number of top and bottom initial edges, can be set to "all" to cover all possible edges (init.edge.num overrides T.up/T.down, set to NA to use T.up/T.down for a single method)
min.occ	only keep rows with at least the given number of non-zero values (carried out before network construction)
keep.filtered	sum all filtered rows and add the sum vector as additional row
norm	normalize matrix (carrried out after filtering)
stand.rows	standardize rows by dividing each entry by its corresponding row sum, applied after normalization
clr	apply CLR transform (after filtering and normalization); if true, stand.rows is ignored and keep.filtered is set to true; pseudocount is ignored for clr (clr with omit.zeros true)
pval.cor	compute p-values of correlations with cor.test (only valid for correlations; takes precedence over permut and permutandboot with or without renorm)
permut	compute edge-specific p-values with a permutation test
columnwise	permute columns instead of rows for the permutation test
renorm	use renormalization when computing permutation distribution (only applied to correlations; cannot be combined with metadata)
permutandboot	compute p-values from both permutation (with or without renorm) and bootstrap distribution
iters	number of iterations for the permutation and bootstrap distributions
bh	multiple-test-correct using Benjamini-Hochberg; if several methods are provided bh is applied to merged p-value
pseudocount	count added to zeros prior to taking logarithm (for KLD, p-value merge and significance)
plot	plot score or, if permut, permutandboot or pval.cor is true, p-value distribution, in both cases after thresholding
verbose	print the number of positive and negative edges and, if permut, permutandboot or pval.cor is true, details of p-value computation

Details

If renorm and permutandboot are both set to TRUE, p-value computation is equal to the ReBoot procedure implemented in CoNet. If more than one method is selected and p-value computation is enabled, p-values are merged with Fisher's method, multiple testing correction (if enabled) is applied on the merged p-value and the merged p-value is reported. If p-value computation is not enabled, the method number is reported as association strength. Edge signs (co-presence/mutual exclusion) are assigned using thresholds (T.up/T.down directly or indirectly via top/bottom initial edge number). Co-presence (high correlation/low dissimilarity) is encoded in green, mutual exclusion (low correlation/high dissimilarity) in red and sign conflicts (lack of agreement between methods) in gray. When metadata are provided, a bipartite network is computed. To circumvent bipartite network computation, metadata can be appended to abundances to form a single input matrix, but in this case, preprocessing on abundance data needs to be carried out before. When CLR transform is applied, the Euclidean distance is the recommended measure, which according to Gloor et al. (https://www.frontiersin.org/articles/10.3389/fmicb.2017.02224/full) is equivalent to the Aitchison distance after CLR.

Value

igraph object with edge weights being either association strengths (single method), the number of supporting methods or, if permut, permutandboot or pval.cor is true, significances (-1*log10(pval))

Examples

data("ibd_taxa")
data("ibd_lineages")
ibd_genera=aggregateTaxa(ibd_taxa,lineages = ibd_lineages,taxon.level = "genus")
# only keep significant ones among the top 50 positive and negative Spearman correlations
plot(barebonesCoNet(ibd_genera,methods="spearman",init.edge.num=50,permutandboot=TRUE))
# only keep edges supported by both Bray Curtis and Spearman
plot(barebonesCoNet(ibd_genera,methods=c("spearman","bray"),init.edge.num=50))
# keep significant Euclidean distance on CLR-transformed data (samples are completed to sum to one)
g=rbind(ibd_genera,rep(1,ncol(ibd_genera))-colSums(ibd_genera))
plot(barebonesCoNet(g,clr=TRUE,methods="euclid",init.edge.num=50,permut=TRUE,columnwise=TRUE))

hallucigenia-sparsa/seqgroup documentation built on July 6, 2022, 1:11 p.m.