CrossICC {CrossICC} | R Documentation

## CrossICC: Automatically Aggregating and Summarizing Bioinformatics Results for Interactive Report.

### Description

CrossICC: Automatically Aggregating and Summarizing Bioinformatics Results for Interactive Report.

The Main Function of the package

### Usage

    CrossICC(..., study.names, filter.cutoff = 0.5, fdr.cutoff = 0.001,
      output.dir = "~", max.K = 10, max.iter = 20, rep.runs = 1000,
      n.platform = 2, pItem = 0.8, pFeature = 1, clusterAlg = "hc",
      distance = "euclidean", sil.filter = "soft",
      heatmap.order = "up.based", com.mode = "overlap", cc.seed = NULL,
      cluster.cutoff = 0.05, ebayes.cutoff = 0.1, ebayes.mode = "up",
      cross = "cluster", supercluster.method = "hclust",
      skip.merge.dup = TRUE, skip.mm = FALSE, skip.mfs = FALSE,
      use.shiny = FALSE, overwrite = FALSE)

### Arguments

`...` | all datasets (matrices is better)
`study.names` | a vector containing all study names
`filter.cutoff` | low variability (median absolute deviation (MAD)) cutoff threshold, default is 0.5.
`fdr.cutoff` | cutoff value during fdr filtering.
`output.dir` | the results' output directory.
`max.K` | the maximum cluster number of ConsensusClusterPlus. Default is 10, but was set as number of samples when there're less than 10 samples.
`max.iter` | the maximum number of iterations.
`rep.runs` | number of subsamples during clustering.
`n.platform` | to filter the signature with it's meta-cluster group in platforms. That is, if the parameter is set to 2 (default), the signature (like hgnc symbol ESR1) in a certain meta-cluser (like K1) must exists more than 2 times among data of all platforms; otherwise, it will not be reported.
`pItem` | proportion of items to sample during clustering.
`pFeature` | proportion of features to sample during clustering.
`clusterAlg` | cluster algorithm. Could be 'hc' heirarchical (hclust), 'pam' for paritioning around medoids, 'km' for k-means upon data matrix, 'kmdist' for k-means upon distance matrices (former km option), or a function that returns a clustering.
`distance` | Could be 'pearson': (1 - Pearson correlation), 'spearman' (1 - Spearman correlation), 'euclidean', 'canberra', 'minkowski" or custom distance function.
`sil.filter` | silhouetee width filtering mode. Could be "soft" or "hard". If "hard", all negtive silhouetee width value will be set to 0. Default is "soft" (to do nothing).
`heatmap.order` | gene order for heatmaps. Default is "up.based", with which genes will be arranged as up-regulated order in meta-clusters across all matrices. Or can be set to "concordant" for all in same order.
`com.mode` | mode for choose common features when pre-processing data. Could be "overlap" (use intersection, default) or "merge" (keep all features).
`cc.seed` | sets random seed for reproducible results.
`cluster.cutoff` | cutoff value during determining cluster numbers.
`ebayes.cutoff` | p-value cutoff when select differentially expressed probes.
`ebayes.mode` | 'up' or 'both'. Choose only up-regulated genes or all differentially expressed genes when determining MDEGs. default is 'up'
`cross` | object type when determining meta-cluster. Could be "cluster" for clusters by ConsencusClusterPlus, "sample" for samples or "none" (only used for single dataset).
`supercluster.method` | method for super-clustering. Default is 'hclust', can also be 'kmeans'.
`skip.merge.dup` | skip merge multiple probes for one gene (duplicates) or not. Default is TRUE (it is highly recommended that user has their data pre-processed well).
`skip.mm` | skip MergeMaid processing or not. Default is FALSE (not skip).
`skip.mfs` | by default, the datasets will be normalized at the start, and the genes or features that have no or few contributions to the final clusters will be filtered out. To skip this process, you can set this parameter to TRUE. Only try when you're sure that you're working with pre-processed datasets.
`use.shiny` | if TRUE, a shiny app will appear after running this main function. Note: You must keep output.dir with default value '~' for using shiny app.
`overwrite` | if user allow overwrite result file? Default is FALSE.

### Value

A nested list with iteration time as its name and list containing consensus cluster, gene signature and balanced cluster as its value.

### See Also

`ConsensusClusterPlus`

### Examples

    data("demo.platforms")
    CrossICC.obj <- CrossICC(demo.platforms, skip.mfs = TRUE, max.iter = 1, overwrite = TRUE, output.dir = tempdir())

[Package CrossICC version 1.0.0 Index]