NetCoMi 1.1.0

This is a major release, which comes with new features, improvements, bugfixes, and reformatted R code.

The commit history has been considerably revised to follow general conventions, which is why the commit hashes have changed and are not in line with the old "master" branch anymore.

New features

• renameTaxa(): New function for renaming taxa in a taxonomic table.
  It comes with functionality for
  making unknown and unclassified taxa unique and substituting them by the next
  higher known taxonomic level. E.g., an unknown genus "g__", where family is the
  next higher known level, can automatically be renamed to
  "1_Streptococcaceae(F)". User-defined patterns determine the format
  of known and substituted names. Unknown names (e.g., NAs) and unclassified taxa
  can be handled separately. Duplicated names within one or more chosen ranks can
  also be made unique by numbering them consecutively.

• editLabels(): New function for editing node labels, i.e., shortening to a
  certain length and removing unwanted characters. It is used by NetCoMi's plot
  functions plot.microNetProps() and plot.diffnet().

• In netCompare(): The adjusted Rand index is also computed for the
  largest connected component (LCC). The summary method has been adapted.

• Argument "testRand" added to netCompare(). Performing a permutation
  test for the adjusted Rand index can now be disabled to save run time.

• Graphlet-based network measures implemented. NetCoMi contains two new
  exported functions calcGCM() and calcGCD() to compute the Graphlet
  Correlation Matrix (GCM) of a network and the Graphlet Correlation Distance
  (GCD) between two networks.
  Orbits for graphlets with up to four nodes are considered.
  Furthermore, the GCM is computed with netAnalyze() and the
  GCD with netCompare() (for the whole network and the largest connected
  component, respectively). Also the orbit counts are returned. The GCD is added
  to the summary for class microNetComp objects returned by netCompare().

• Significance test for the GCD: If permutation tests are conducted with
  netCompare(), the GCD is tested for being significantly different from zero.

• New function testGCM() to test graphlet-based measures for
  significance. For a single GCM, the correlations are tested for being
  significantly different from zero.
  If two GCMs are given, it is tested if the correlations are
  significantly different between the two groups, that is, the absolute
  differences between correlations ( $|gc1_{ij}-gc2_{ij}|$ ) are tested
  for being different from zero.

• New function plotHeat() for plotting a mixed heatmap where, for
  instance, values are shown in the upper triangle and corresponding p-values or
  significance codes in the lower triangle. The function is used for plotting
  heatmaps of the GCMs, but could also be used for association matrices.

• netAnalyze() now by default returns a heatmap of the GCM(s) with
  graphlet correlations in the upper triangle and significance codes in the lower
  triangle.

• Argument "doPlot" added to plot.microNetProps() to suppress the plot if
  only the return value is of interest.

• New "show" arguments are added to the summary methods for class
  microNetProps and microNetComp objects. They specify which network
  properties should be printed in the summary. See the help pages of
  summary.microNetProps and summary.microNetComp() for details.

• New zero replacement method "pseudoZO" available in netConstruct().
  Instead of adding the desired pseudo count to the whole count matrix, it is
  added to zero counts only if pseudoZO is chosen. The behavior of "pseudo"
  (a further available method where a pseudo count is added to all counts) has not
  changed. Adding a pseudo count only to zeros preserves the ratios between
  non-zero counts, which is desirable.

• createAssoPerm() now accepts objects of class microNet as input (in
  addition to objects of class microNetProps).

• SPRING's fast version of latent correlation computation (implemented in
  mixedCCA) is available again.
  It can be used by setting the netConstruct() parameter measurePar$Rmethod
  to "approx", which is now the default again.

• The function multAdjust() now has an argument pTrueNull to pre-define
  the proportion of true null hypotheses for the adaptive BH method.

• netConstruct() has a new argument assoBoot, which enables the
  computation of bootstrap association matrices outside netConstruct() if
  bootstrapping is used for sparsification. An example has been added to the
  help page ?netConstruct. This feature might be useful for very large
  association matrices (for which the working memory might reach its limit).

Bug fixes

• In netConstruct():

  • Using "bootstrap" as sparsification method in
    combination with one of the association methods "bicor", "cclasso", "ccrepe", or
    "gcoda" led to the error: argument "verbose" is missing, with no default,
    which has been fixed.
  • The "signedPos" transformation did not work properly.
    Dissimilarities corresponding to negative correlations were set to zero instead
    of infinity.

• In editLabels(): The function (and thus also plot.microNetProps)
  threw an error if taxa have been renamed with
  renameTaxa and the data contain more than 9 taxa with equal names, so that
  double-digit numbers were added to avoid duplicates.

• Issues in network analysis and plotting if association matrices are used
  for network construction, but row and/or column names are missing.
  (issue #65)

• diffnet() threw an error if association matrices are used for network
  construction instead of count matrices.
  (issue #66)

• In plot.microNetProps():

  • The function now directly returns an error if x has not the
    expected class.
  • The cut parameter could not be changed.

• In cclasso(): In rare cases, the function produced complex numbers,
  which led to an error.

Further changes

• In permutation tests: The permuted group labels must now be different from
  the original group vector. In other words, the original group vector is strictly
  avoided in the matrix with permuted group labels. So far, only duplicates were
  avoided. Only in exact permutation tests (if nPerm equals the possible number
  of permutations), the original group vector is still included in the permutation
  matrix. The calculation of p-values has been adapted to the new behavior:
  p=B/N for exact p-values and p=(B+1)/(N+1) for approximated p-values, where
  B is the number of permutation test statistics being larger than or equal to
  the observed one, and N is the number of permutations. So far, p=(B+1)/(N+1)
  has been used in all cases.

• In plot.microNetProps():

  • The default of shortenLabels is now "none", i.e. the labels are not
    shortened by default, to avoid confusion about the node labels.
  • The edge filter (specified via edgeFilter and edgeInvisFilter) now
    refers to the estimated association/dissimilarities instead of edge weights.
    E.g., setting the threshold to 0.3 for an association network hides edges
    with a corresponding absolute association below 0.3 even though the edge
    weight might be different (depending on the transformation used for network
    construction). (issue #26)
  • If two networks are constructed and the cut parameter is not
    user-defined, the mean of the two determined cut parameters is now used for
    both networks so that edge thicknesses are comparable.

• More expressive messages and errors in diffnet and plot.diffnet if no
  differential associations are detected.

• New function .suppress_warnings() to suppress certain warnings returned
  by external functions.

• In netConstruct if "multRepl" is used for zero handling:
  The warning about the proportion of zeros is suppressed by setting the
  multRepl() parameter "z.warning" to 1.

• The functions makeCluster and stopCluster from parallel package
  are now used for parallel computation because those from snow package
  sometimes led to problems on Unix machines.

Style

• The whole R code has been reformatted to follow general conventions.

• The element "clustering_lcc" as part of the netAnalyze output has changed
  to "clusteringLCC" to be in line with the remaining output.

• Input argument checking of exported function has been revised. New functions
  .checkArgsXxx() are added to perform argument checking outside the main
  functions.

• Non-exported functions have been renamed to follow general naming conventions,
  i.e. that of Bioconductor:

  • Use camelCase for all functions.
  • Non-exported functions have prefix "."
  • See the NEWS file for an overview of the changed function names.