RSEC: Resampling-based Sequential Ensemble Clustering
In clusterExperiment: Compare Clusterings for Single-Cell Sequencing

Description Usage Arguments Details Value

Implementation of the RSEC algorithm (Resampling-based Sequential Ensemble Clustering) for single cell sequencing data. This is a wrapper function around the existing ClusterExperiment workflow that results in the output of RSEC.

## S4 method for signature 'SummarizedExperiment'
RSEC(x, ...)

## S4 method for signature 'data.frame'
RSEC(x, ...)

## S4 method for signature 'ClusterExperiment'
RSEC(x, eraseOld = FALSE, rerunClusterMany = FALSE, ...)

## S4 method for signature 'matrixOrHDF5'
RSEC(x, ...)

## S4 method for signature 'SingleCellExperiment'
RSEC(
  x,
  isCount = FALSE,
  transFun = NULL,
  reduceMethod = "PCA",
  nFilterDims = defaultNDims(x, reduceMethod, type = "filterStats"),
  nReducedDims = defaultNDims(x, reduceMethod, type = "reducedDims"),
  k0s = 4:15,
  subsample = TRUE,
  sequential = TRUE,
  clusterFunction = "hierarchical01",
  alphas = c(0.1, 0.2, 0.3),
  betas = 0.9,
  minSizes = 1,
  makeMissingDiss = if (ncol(x) < 1000) TRUE else FALSE,
  consensusProportion = 0.7,
  consensusMinSize,
  dendroReduce,
  dendroNDims,
  mergeMethod = "adjP",
  mergeCutoff,
  mergeLogFCcutoff,
  mergeDEMethod = if (isCount) "limma-voom" else "limma",
  verbose = FALSE,
  parameterWarnings = FALSE,
  mainClusterArgs = NULL,
  subsampleArgs = NULL,
  seqArgs = NULL,
  consensusArgs = NULL,
  whichAssay = 1,
  ncores = 1,
  random.seed = NULL,
  stopOnErrors = FALSE,
  run = TRUE
)

`x`	the data matrix on which to run the clustering. Can be object of the following classes: matrix (with genes in rows), `SummarizedExperiment`, `SingleCellExperiment` or `ClusterExperiment`.
`...`	For signature `matrix`, arguments to be passed on to mclapply (if ncores>1). For all the other signatures, arguments to be passed to the method for signature `matrix`.
`eraseOld`	logical. Only relevant if input `x` is of class `ClusterExperiment`. If TRUE, will erase existing workflow results (clusterMany as well as mergeClusters and makeConsensus). If FALSE, existing workflow results will have "`_i`" added to the clusterTypes value, where `i` is one more than the largest such existing workflow clusterTypes.
`rerunClusterMany`	logical. If the object is a ClusterExperiment object, determines whether to rerun the clusterMany step. Useful if want to try different parameters for combining clusters after the clusterMany step, without the computational costs of the clusterMany step.
`isCount`	if `transFun=NULL`, then `isCount=TRUE` will determine the transformation as defined by `function(x){log2(x+1)}`, and `isCount=FALSE` will give a transformation function `function(x){x}`. Ignored if `transFun=NULL`. If object is of class `ClusterExperiment`, the stored transformation will be used and giving this parameter will result in an error.
`transFun`	a transformation function to be applied to the data. If the transformation applied to the data creates an error or NA values, then the function will throw an error. If object is of class `ClusterExperiment`, the stored transformation will be used and giving this parameter will result in an error.
`reduceMethod`	character A character identifying what type of dimensionality reduction to perform before clustering. Options are 1) "none", 2) one of listBuiltInReducedDims() or listBuiltInFitlerStats OR 3) stored filtering or reducedDim values in the object.
`nFilterDims`	vector of the number of the most variable features to keep (when "var", "abscv", or "mad" is identified in `reduceMethod`).
`nReducedDims`	vector of the number of dimensions to use (when `reduceMethod` gives a dimensionality reduction method).
`k0s`	the k0 parameter for sequential clustering (see `seqCluster`)
`subsample`	logical as to whether to subsample via `subsampleClustering`. If TRUE, clustering in mainClustering step is done on the co-occurance between clusterings in the subsampled clustering results. If FALSE, the mainClustering step will be run directly on `x`/`diss`
`sequential`	logical whether to use the sequential strategy (see details of `seqCluster`). Can be used in combination with `subsample=TRUE` or `FALSE`.
`clusterFunction`	function used for the clustering. This must be either 1) a character vector of built-in clustering techniques, or 2) a named list of `ClusterFunction` objects. Current functions can be found by typing `listBuiltInFunctions()` into the command-line.
`alphas`	values of alpha to be tried. Only used for clusterFunctions of type '01'. Determines tightness required in creating clusters from the dissimilarity matrix. Takes on values in [0,1]. See documentation of `ClusterFunction`.
`betas`	values of `beta` to be tried in sequential steps. Only used for `sequential=TRUE`. Determines the similarity between two clusters required in order to deem the cluster stable. Takes on values in [0,1]. See documentation of `seqCluster`.
`minSizes`	the minimimum size required for a cluster (in the `mainClustering` step). Clusters smaller than this are not kept and samples are left unassigned.
`makeMissingDiss`	logical. Whether to calculate necessary distance matrices needed when input is not "diss". If TRUE, then when a clustering function calls for a inputType "diss", but the given matrix is of type "X", the function will calculate a distance function. A dissimilarity matrix will also be calculated if a post-processing argument like `findBestK` or `removeSil` is chosen, since these rely on calcualting silhouette widths from distances.
`consensusProportion`	passed to `proportion` in `makeConsensus`
`consensusMinSize`	passed to `minSize` in `makeConsensus`
`dendroReduce`	passed to `reduceMethod` in `makeDendrogram`
`dendroNDims`	passed to `nDims` in `makeDendrogram`
`mergeMethod`	passed to `mergeMethod` in `mergeClusters`
`mergeCutoff`	passed to `cutoff` in `mergeClusters`
`mergeLogFCcutoff`	passed to `logFCcutoff` in `mergeClusters`
`mergeDEMethod`	passed to `DEMethod` argument in `mergeClusters`. By default, unless otherwise chosen by the user, if `isCount=TRUE`, then `mergeDEMethod="limma-voom"`, otherwise `mergeDEMethod="limma"`. These choices are for speed considerations and the user may want to try `mergeDEMethod="edgeR"` on smaller datasets of counts.
`verbose`	logical. If TRUE it will print informative messages.
`parameterWarnings`	logical, as to whether warnings and comments from checking the validity of the parameter combinations should be printed.
`mainClusterArgs`	list of arguments to be passed for the mainClustering step, see help pages of `mainClustering`.
`subsampleArgs`	list of arguments to be passed to the subsampling step (if `subsample=TRUE`), see help pages of `subsampleClustering`.
`seqArgs`	list of arguments to be passed to `seqCluster`.
`consensusArgs`	list of additional arguments to be passed to `makeConsensus`
`whichAssay`	numeric or character specifying which assay to use. See `assay` for details.
`ncores`	the number of threads
`random.seed`	a value to set seed before each run of clusterSingle (so that all of the runs are run on the same subsample of the data). Note, if 'random.seed' is set, argument 'ncores' should NOT be passed via subsampleArgs; instead set the argument 'ncores' of clusterMany directly (which is preferred for improving speed anyway).
`stopOnErrors`	logical. If `FALSE`, if RSEC hits an error after the `clusterMany` step, it will return the results up to that point, rather than generating a stop error. The text of error will be printed as a NOTE. This allows the user to get the results to that point, so as to not have to rerun the computationally heavy earlier steps. The `TRUE` option is only provided for debugging purposes.
`run`	logical. If FALSE, doesn't run clustering, but just returns matrix of parameters that will be run, for the purpose of inspection by user (with rownames equal to the names of the resulting column names of clMat object that would be returned if `run=TRUE`). Even if `run=FALSE`, however, the function will create the dimensionality reductions of the data indicated by the user input.

Note that the argument isCount is mainly used when the input is a matrix or SingleCellExperiment Class and passed to clusterMany to set the transformation function of the data. However, if RSEC is being re-called on an existing ClusterExperiment object, it does not reset the transformation; in this case the only impact it will have is in setting the default value for DEMethod for mergeClusters step, but ONLY if mergeClusters hasn't already been calculated. To set arguments that allow you to recalculate the non-null probabilities of the hierarchy see mergeClusters.

A ClusterExperiment object is returned containing all of the clusterings from the steps of RSEC

clusterExperiment documentation built on Feb. 11, 2021, 2 a.m.