constructors {conclus} | R Documentation

## constructors

### Description

Constructor of the class scRNAseq.

### Usage

    singlecellRNAseq(experimentName, countMatrix, species, outputDirectory,
    tSNElist=list(new("Tsne")), dbscanlist=list(new("Dbscan")),
    cellSimMat= matrix(nrow = 1, ncol = 1, dimnames = list("c1", "c1"),
    data = 1), clustSimMat=matrix(nrow = 1, ncol = 1, dimnames = list("1", "1"),
    data = 1), clustSimOrdered=factor(1), markgenlist=list(data.frame(
    Gene = c("gene1"), mean_log10_fdr = c(NA), n_05 = c(NA), score = c(NA))),
    clustMark=data.frame(geneName="gene1", clusters=NA), genesInf = data.frame(
    uniprot_gn_symbol=c("symbol"), clusters="1", external_gene_name="gene",
    go_id="GO1,GO2", mgi_description="description",
    entrezgene_description="descr", gene_biotype="gene", chromosome_name="1",
    Symbol="symbol", ensembl_gene_id="ENS", mgi_id="MGI", entrezgene_id="1",
    uniprot_gn_id="ID"))

    TsneCluster(name, pc, perplexity, coordinates)

    DbscanCluster(name, epsilon, minPoints, clustering)

### Arguments

`experimentName` | Character string representing the name of the experiment.
`countMatrix` | An integer matrix representing the raw count matrix with reads or unique molecular identifiers (UMIs).
`species` | Character string representing the species of interest. Currently limited to "mouse" and "human". Other organisms can be added on demand.
`outputDirectory` | A character string of the path to the root output folder.
`tSNElist` | List of 'Tsne' objects representing the different tSNE coordinates generated by CONCLUS.
`dbscanlist` | List of 'Dbscan' objects representing the different Dbscan clustering generated by CONCLUS.
`cellSimMat` | A numeric Matrix defining how many times two cells have been associated to the same cluster across the 84 solutions (by default) of clustering.
`clustSimMat` | A numeric matrix comparing the robustness of the consensus clusters.
`clustSimOrdered` | A factor representing the clusters ordered by similarity.
`markgenlist` | List of data.frames. Each data frame contains the ranked genes of one cluster.
`clustMark` | A data frame containing the top 10 (by default) marker genes of each clusters.
`genesInf` | A data frame containing informations of the markers genes for each clusters.
`name` | A 'character' string representing the name of the Dbscan clustering.
`pc` | A 'numeric' value representing the number of principal components used by CONCLUS to perfom a PCA before calculating the tSNE.
`perplexity` | A 'numeric' vector. Default: c(30, 40)
`coordinates` | A 'numeric' matrix that contains the coordinates of one tSNE solution.
`epsilon` | A 'numeric' vector. The epsilon is the distance to consider two points belonging to the same cluster. Default = c(1.3, 1.4, 1.5)
`minPoints` | A 'numeric' value. The minPoints is the minimum number of points to construct a cluster.
`clustering` | A 'matrix' that contains the result of one DBSCAN clustering solution.

### Value

Object of class scRNAseq

Object of class Tsne

Object of class Dbscan

### See Also

scRNAseq-class

Tsne-class

Dbscan-class

### Examples

    experimentName <- "Bergiers"
    outputDirectory <- "YourOutputDirectory"

    ## Load the count matrix
    countmatrixPath <- system.file("extdata/countMatrix.tsv", package="conclus")
    countMatrix <- loadDataOrMatrix(file=countmatrixPath, type="countMatrix",
                                    ignoreCellNumber=TRUE)

    ## Load the coldata
    coldataPath <- system.file("extdata/colData.tsv", package="conclus")
    columnsMetaData <- loadDataOrMatrix(file=coldataPath, type="coldata",
                                        columnID="cell_ID")

    ## Create the initial object
    scr <- singlecellRNAseq(experimentName = experimentName,
                    countMatrix     = countMatrix,
                    species         = "mouse",
                    outputDirectory = outputDirectory)

    mat <- matrix(seq_len(20), ncol=2)
    colnames(mat) <- c("X", "Y")
    TsneCluster(name = "test", pc = 30, perplexity = 4,
    coordinates = mat)

    DbscanCluster("test", 0.5, 2, matrix(1:10))

[Package conclus version 1.0.0 Index]