constructors: constructors

In ilyessr/conclus: ScRNA-seq Workflow CONCLUS - From CONsensus CLUSters To A Meaningful CONCLUSion

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))

ilyessr/conclus documentation built on April 8, 2022, 1:43 p.m.