Package 'scTGIF' reference manual

Title:	Cell type annotation for unannotated single-cell RNA-Seq data
Description:	scTGIF connects the cells and the related gene functions without cell type label.
Authors:	Koki Tsuyuzaki [aut, cre]
Maintainer:	Koki Tsuyuzaki <[email protected]>
License:	Artistic-2.0
Version:	1.19.0
Built:	2024-06-30 06:14:36 UTC
Source:	https://github.com/bioc/scTGIF

Cell type annotation for unannotated single-cell RNA-Seq data

Description

scTGIF connects the cells and the related gene functions without cell type label.

Details

The DESCRIPTION file:

Package:	scTGIF
Type:	Package
Title:	Cell type annotation for unannotated single-cell RNA-Seq data
Version:	1.19.0
Authors@R:	person("Koki", "Tsuyuzaki", role = c("aut", "cre"), email = "[email protected]")
Depends:	R (>= 3.6.0)
Imports:	GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly, tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr, S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor, methods, scales, msigdbr, schex, tibble, ggplot2, igraph
Suggests:	testthat
Description:	scTGIF connects the cells and the related gene functions without cell type label.
License:	Artistic-2.0
biocViews:	DimensionReduction, QualityControl, SingleCell, Software, GeneExpression
VignetteBuilder:	knitr
Repository:	https://bioc.r-universe.dev
RemoteUrl:	https://github.com/bioc/scTGIF
RemoteRef:	HEAD
RemoteSha:	24ef24ee6de00c3820be3c1df4aebaca846fa514
Author:	Koki Tsuyuzaki [aut, cre]
Maintainer:	Koki Tsuyuzaki <[email protected]>

Index of help topics:

DistalLungEpithelium    Gene expression matrix of DistalLungEpithelium
                        dataset containing five cluster.
calcTGIF                Function for connecting cellular patterns and
                        functional patterns using jNMF
cellMarkerToGmt         A function to convert the CellMarker data to
                        GMT files.
convertRowID            A function to change the row names of a matrix.
label.DistalLungEpithelium
                        Cellular label of DistalLungEpithelium dataset
                        containing five cluster.
pca.DistalLungEpithelium
                        The result of PCA of the DistalLungEpithelium
                        dataset.
reportTGIF              Function for reporting the result of 'calcTGIF'
                        function
scTGIF-package          Cell type annotation for unannotated
                        single-cell RNA-Seq data
settingTGIF             Paramter setting for scTGIF

calcTGIF function calculates what kind of cellular patterns and functional patterns are contained in single-cell RNA-seq data and reportTGIF function generates report of analytic result. The algorithm is based on joint NMF, which is implemented in nnTensor package.

Author(s)

Koki Tsuyuzaki [aut, cre]

Maintainer: Koki Tsuyuzaki <[email protected]>

References

Dominic Grun, Anna Lyubimova, Lennart Kester, Kay Wiebrands, Onur Basak, Nobuo Sasaki, Hans Clevers, Alexander van Oudenaarden (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature, 525: 251-255

Function for connecting cellular patterns and functional patterns using jNMF

Description

calcTGIF function calculates what kind of cellular patterns and functional patterns are contained in single-cell RNA-seq data and reportTGIF function generates report of analytic result.

Usage

calcTGIF(sce, ndim, verbose=FALSE, droplet=TRUE)
calcTGIF(sce, ndim, verbose=FALSE, droplet=TRUE)

Arguments

`sce`	A object generated by instantization of SingleCellExperiment-class.
`ndim`	The number of low-dimension of joint NMF algorithm.
`verbose`	The verbose parameter for nnTensor::jNMF (Default: FALSE).
`droplet`	Whether Droplet-based single-cell RNA-Seq or not (Default: TRUE).

Value

The result is saved to metadata slot of SingleCellExperiment object.

Author(s)

Koki Tsuyuzaki [aut, cre]

Examples

showMethods("calcTGIF")
showMethods("calcTGIF")

A function to convert the CellMarker data to GMT files.

Description

The GMT (Gene Matrix Transposed file format : *.gmt) file is formatted by the Broad Institute (https://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats#GMT:_Gene_Matrix_Transposed_file_format_.28.2A.gmt.29). The data can be downloaded from the website of CellMarker (http://biocc.hrbmu.edu.cn/CellMarker).

Usage

cellMarkerToGmt(infile, outfile,
  uniq.column=c("tissueType", "cellName"),
  geneid.type=c("geneID", "geneSymbol"))
cellMarkerToGmt(infile, outfile,
  uniq.column=c("tissueType", "cellName"),
  geneid.type=c("geneID", "geneSymbol"))

Arguments

`infile`	The input file downloaded from CellMarker website
`outfile`	The output GMT file converted from the CellMarker data
`uniq.column`	The duplicated terms in the specified column are aggrgated as a row of GMT file (Default: geneID)
`geneid.type`	Output gene identifier. (Default: geneID)

Value

output

A GMT file is generated.

Author(s)

Koki Tsuyuzaki [aut, cre]

Examples

  library("GSEABase")

  tmp <- tempdir()
  infile1 = paste0(tmp, "/Human_cell_markers.txt")
  outfile1_1 = paste0(tmp, "/Human_cell_markers_1.gmt")
  outfile1_2 = paste0(tmp, "/Human_cell_markers_2.gmt")
  outfile1_3 = paste0(tmp, "/Human_cell_markers_3.gmt")
  outfile1_4 = paste0(tmp, "/Human_cell_markers_4.gmt")

  sink(infile1)
  cat("speciesType\ttissueType\tUberonOntologyID\tcancerType\tcellType\tcellName\tCellOntologyID\tcellMarker\tgeneSymbol\tgeneID\tproteinName\tproteinID\tmarkerResource\tPMID\tCompany\n")
  cat("Human\tKidney\tUBERON_0002113\tNormal\tNormal cell\tProximal tubular cell\tNA\tIntestinal Alkaline Phosphatase\tALPI\t248\tPPBI\tP09923\tExperiment\t9263997\tNA\n")
  cat("Human\tLiver\tUBERON_0002107\tNormal\tNormal cell\tIto cell (hepatic stellate cell)\tCL_0000632\tSynaptophysin\tSYP\t6855\tSYPH\tP08247\tExperiment\t10595912\tNA\n")
  cat("Human\tEndometrium\tUBERON_0001295\tNormal\tNormal cell\tTrophoblast cell\tCL_0000351\tCEACAM1\tCEACAM1\t634\tCEAM1\tP13688\tExperiment\t10751340\tNA\n")
  cat("Human\tGerm\tUBERON_0000923\tNormal\tNormal cell\tPrimordial germ cell\tCL_0000670\tVASA\tDDX4\t54514\tDDX4\tQ9NQI0\tExperiment\t10920202\tNA\n")
  cat("Human\tCorneal epithelium\tUBERON_0001772\tNormal\tNormal cell\tEpithelial cell\tCL_0000066\tKLF6\tKLF6\t1316\tKLF6\tQ99612\tExperiment\t12407152\tNA\n")
  cat("Human\tPlacenta\tUBERON_0001987\tNormal\tNormal cell\tCytotrophoblast\tCL_0000351\tFGF10\tFGF10\t2255\tFGF10\tO15520\tExperiment\t15950061\tNA\n")
  cat("Human\tPeriosteum\tUBERON_0002515\tNormal\tNormal cell\tPeriosteum-derived progenitor cell\tNA\tCD166, CD45, CD9, CD90\tALCAM, PTPRC, CD9, THY1\t214, 5788, 928, 7070\tCD166, PTPRC, CD9, THY1\tQ13740, P08575, P21926, P04216\tExperiment\t15977065\tNA\n")
  cat("Human\tAmniotic membrane\tUBERON_0009742\tNormal\tNormal cell\tAmnion epithelial cell\tCL_0002536\tNANOG, OCT3/4\tNANOG, POU5F1\t79923, 5460\tNANOG, PO5F1\tQ9H9S0, Q01860\tExperiment\t16081662\tNA\n")
  cat("Human\tPrimitive streak\tUBERON_0004341\tNormal\tNormal cell\tPrimitive streak cell\tNA\tLHX1, MIXL1\tLHX1, MIXL1\t3975, 83881\tLHX1, MIXL1\tP48742, Q9H2W2\tExperiment\t16258519\tNA\n")
  sink()

  cellMarkerToGmt(infile1, outfile1_1, uniq.column=c("tissueType"),
    geneid.type=c("geneID"))
  cellMarkerToGmt(infile1, outfile1_2, uniq.column=c("tissueType"),
    geneid.type=c("geneSymbol"))
  cellMarkerToGmt(infile1, outfile1_3, uniq.column=c("cellName"),
    geneid.type=c("geneID"))
  cellMarkerToGmt(infile1, outfile1_4, uniq.column=c("cellName"),
    geneid.type=c("geneSymbol"))

  gmt1_1 <- getGmt(outfile1_1)
  gmt1_2 <- getGmt(outfile1_2)
  gmt1_3 <- getGmt(outfile1_3)
  gmt1_4 <- getGmt(outfile1_4)
library("GSEABase")

  tmp <- tempdir()
  infile1 = paste0(tmp, "/Human_cell_markers.txt")
  outfile1_1 = paste0(tmp, "/Human_cell_markers_1.gmt")
  outfile1_2 = paste0(tmp, "/Human_cell_markers_2.gmt")
  outfile1_3 = paste0(tmp, "/Human_cell_markers_3.gmt")
  outfile1_4 = paste0(tmp, "/Human_cell_markers_4.gmt")

  sink(infile1)
  cat("speciesType\ttissueType\tUberonOntologyID\tcancerType\tcellType\tcellName\tCellOntologyID\tcellMarker\tgeneSymbol\tgeneID\tproteinName\tproteinID\tmarkerResource\tPMID\tCompany\n")
  cat("Human\tKidney\tUBERON_0002113\tNormal\tNormal cell\tProximal tubular cell\tNA\tIntestinal Alkaline Phosphatase\tALPI\t248\tPPBI\tP09923\tExperiment\t9263997\tNA\n")
  cat("Human\tLiver\tUBERON_0002107\tNormal\tNormal cell\tIto cell (hepatic stellate cell)\tCL_0000632\tSynaptophysin\tSYP\t6855\tSYPH\tP08247\tExperiment\t10595912\tNA\n")
  cat("Human\tEndometrium\tUBERON_0001295\tNormal\tNormal cell\tTrophoblast cell\tCL_0000351\tCEACAM1\tCEACAM1\t634\tCEAM1\tP13688\tExperiment\t10751340\tNA\n")
  cat("Human\tGerm\tUBERON_0000923\tNormal\tNormal cell\tPrimordial germ cell\tCL_0000670\tVASA\tDDX4\t54514\tDDX4\tQ9NQI0\tExperiment\t10920202\tNA\n")
  cat("Human\tCorneal epithelium\tUBERON_0001772\tNormal\tNormal cell\tEpithelial cell\tCL_0000066\tKLF6\tKLF6\t1316\tKLF6\tQ99612\tExperiment\t12407152\tNA\n")
  cat("Human\tPlacenta\tUBERON_0001987\tNormal\tNormal cell\tCytotrophoblast\tCL_0000351\tFGF10\tFGF10\t2255\tFGF10\tO15520\tExperiment\t15950061\tNA\n")
  cat("Human\tPeriosteum\tUBERON_0002515\tNormal\tNormal cell\tPeriosteum-derived progenitor cell\tNA\tCD166, CD45, CD9, CD90\tALCAM, PTPRC, CD9, THY1\t214, 5788, 928, 7070\tCD166, PTPRC, CD9, THY1\tQ13740, P08575, P21926, P04216\tExperiment\t15977065\tNA\n")
  cat("Human\tAmniotic membrane\tUBERON_0009742\tNormal\tNormal cell\tAmnion epithelial cell\tCL_0002536\tNANOG, OCT3/4\tNANOG, POU5F1\t79923, 5460\tNANOG, PO5F1\tQ9H9S0, Q01860\tExperiment\t16081662\tNA\n")
  cat("Human\tPrimitive streak\tUBERON_0004341\tNormal\tNormal cell\tPrimitive streak cell\tNA\tLHX1, MIXL1\tLHX1, MIXL1\t3975, 83881\tLHX1, MIXL1\tP48742, Q9H2W2\tExperiment\t16258519\tNA\n")
  sink()

  cellMarkerToGmt(infile1, outfile1_1, uniq.column=c("tissueType"),
    geneid.type=c("geneID"))
  cellMarkerToGmt(infile1, outfile1_2, uniq.column=c("tissueType"),
    geneid.type=c("geneSymbol"))
  cellMarkerToGmt(infile1, outfile1_3, uniq.column=c("cellName"),
    geneid.type=c("geneID"))
  cellMarkerToGmt(infile1, outfile1_4, uniq.column=c("cellName"),
    geneid.type=c("geneSymbol"))

  gmt1_1 <- getGmt(outfile1_1)
  gmt1_2 <- getGmt(outfile1_2)
  gmt1_3 <- getGmt(outfile1_3)
  gmt1_4 <- getGmt(outfile1_4)

A function to change the row names of a matrix.

Description

To avoid to specify the duplicated row names against matrix, multiple aggregation rules are implemented.

Usage

convertRowID(input, rowID, LtoR,
    aggr.rule=c("sum", "mean", "large.mean", "large.var", "large.cv2"))
convertRowID(input, rowID, LtoR,
    aggr.rule=c("sum", "mean", "large.mean", "large.var", "large.cv2"))

Arguments

`input`	A matrix filled with number (n * m).
`rowID`	A vector to specify the row names of input (length: n).
`LtoR`	A corresponding table to covert the row names of input as different type of IDs. (Left: current row names -> Right: new row names)
`aggr.rule`	The aggregation rule to change the row names of input and collapse/select the values, if the row names changed by LtoR are duplicated. sum: Collapses multiple row vectors by summation. mean: Collapses multiple row vectors by mean. large.mean: Select a vector having the largest mean in the duplicated vectors. large.var: Select a vector having the largest variance in the duplicated vectors. large.cv2: Select a vector having the largest CV2 in the duplicated vectors.

Value

`output`	A matrix, in which the row names are changed, according to the aggregation rule user specified.
`ctable`	The corresponding table explaining the relationship between previous row names and changed row names of input.

Author(s)

Koki Tsuyuzaki [aut, cre]

Examples

    input <- matrix(1:20, nrow=4, ncol=5)
    rowID <- c("A", "B", "C", "D")
    LtoR <- rbind(
      c("A", "3"),
      c("B", "2"),
      c("C", "4"),
      c("D", "7"))
    (input2 <- convertRowID(input, rowID, LtoR, "sum"))
    (input3 <- convertRowID(input, rowID, LtoR, "mean"))
    (input4 <- convertRowID(input, rowID, LtoR, "large.mean"))
    (input5 <- convertRowID(input, rowID, LtoR, "large.var"))
    (input6 <- convertRowID(input, rowID, LtoR, "large.cv2"))
input <- matrix(1:20, nrow=4, ncol=5)
    rowID <- c("A", "B", "C", "D")
    LtoR <- rbind(
      c("A", "3"),
      c("B", "2"),
      c("C", "4"),
      c("D", "7"))
    (input2 <- convertRowID(input, rowID, LtoR, "sum"))
    (input3 <- convertRowID(input, rowID, LtoR, "mean"))
    (input4 <- convertRowID(input, rowID, LtoR, "large.mean"))
    (input5 <- convertRowID(input, rowID, LtoR, "large.var"))
    (input6 <- convertRowID(input, rowID, LtoR, "large.cv2"))

Gene expression matrix of DistalLungEpithelium dataset containing five cluster.

Description

A data frame with 3397 rows (genes) with following 80 columns (cells).

The data is downloaded as supplementary information of the distal lung epithelium paper (https://www.nature.com/articles/nature13173).

Low-expressed genes are filted.

All Gene ID is converted to Human Entrez Gene ID for applying the data to scTGIF.

Usage

data("DistalLungEpithelium")data("DistalLungEpithelium")

Source

http://www.nature.com/nbt/journal/v33/n2/full/nbt.3102.html

References

Treutlein, B. et al. (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371-375

Examples

data("DistalLungEpithelium")
data("DistalLungEpithelium")

Cellular label of DistalLungEpithelium dataset containing five cluster.

Description

A vector containing 80 elements (cells).

Usage

data("label.DistalLungEpithelium")data("label.DistalLungEpithelium")

References

Treutlein, B. et al. (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371-375

Examples

data("label.DistalLungEpithelium")
data("label.DistalLungEpithelium")

The result of PCA of the DistalLungEpithelium dataset.

Description

A matrix having 80 (cells) * 2 (PCs) elements.

Usage

data("pca.DistalLungEpithelium")data("pca.DistalLungEpithelium")

References

Treutlein, B. et al. (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371-375

Examples

data("pca.DistalLungEpithelium")
data("pca.DistalLungEpithelium")

Function for reporting the result of `calcTGIF` function

Description

calcTGIF function calculates what kind of cellular patterns and functional patterns are contained in single-cell RNA-seq data and reportTGIF function generates report of analytic result.

Usage

reportTGIF(sce, out.dir=tempdir(), html.open=FALSE,
    title="The result of scTGIF",
    author="The person who runs this script",
    assayNames="counts")
reportTGIF(sce, out.dir=tempdir(), html.open=FALSE,
    title="The result of scTGIF",
    author="The person who runs this script",
    assayNames="counts")

Arguments

`sce`	A object generated by instantization of SingleCellExperiment-class.
`out.dir`	Output directory user want to save the report (Default: tempdir()).
`html.open`	Whether html is opened when `reportTGIF` is finished (Default: FALSE)
`title`	Title of report (Default: "The result of scTGIF")
`author`	The name of user name (Default: "The person who runs this script")
`assayNames`	The unit of gene expression for using scTGIF (e.g. normcounts, cpm...etc) (Default: "counts").

Value

Some file is generated to output directory user specified.

Author(s)

Koki Tsuyuzaki [aut, cre]

Examples

    if(interactive()){
        # Package loading
        library("SingleCellExperiment")
        library("GSEABase")
        library("msigdbr")

        # Test data
        data("DistalLungEpithelium")
        data("pca.DistalLungEpithelium")
        data("label.DistalLungEpithelium")

        # Test data
        par(ask=FALSE)
        plot(pca.DistalLungEpithelium, col=label.DistalLungEpithelium, pch=16,
            main="Distal lung epithelium dataset", xlab="PCA1",
            ylab="PCA2", bty="n")
        text(0.1, 0.05, "AT1", col="#FF7F00", cex=2)
        text(0.07, -0.15, "AT2", col="#E41A1C", cex=2)
        text(0.13, -0.04, "BP", col="#A65628", cex=2)
        text(0.125, -0.15, "Clara", col="#377EB8", cex=2)
        text(0.09, -0.2, "Cilliated", col="#4DAF4A", cex=2)

        # Load the gmt file from MSigDB
        # Only "Entrez Gene ID" can be used in scTGIF
        # e.g. gmt <- GSEABase::getGmt(
        #     "/PATH/YOU/SAVED/THE/GMTFILES/h.all.v6.0.entrez.gmt")
        # Here we use msigdbr to retrieve mouse gene sets

        # Mouse gene set (NCBI Gene ID)
        m_df <- msigdbr(species = "Mus musculus", category = "H")[,
            c("gs_name", "entrez_gene")]

        # Convert to GeneSetCollection
        hallmark = unique(m_df$gs_name)
        gsc <- lapply(hallmark, function(h){
            target = which(m_df$gs_name == h)
            geneIds = unique(as.character(m_df$entrez_gene[target]))
            GeneSet(setName=h, geneIds)
        })
        gmt <- GeneSetCollection(gsc)

        # SingleCellExperiment-class
        sce <- SingleCellExperiment(
            assays = list(counts = DistalLungEpithelium))
        reducedDims(sce) <- SimpleList(PCA=pca.DistalLungEpithelium)

        # User's Original Normalization Function
        CPMED <- function(input){
            libsize <- colSums(input)
            median(libsize) * t(t(input) / libsize)
        }
        # Normalization
        normcounts(sce) <- log10(CPMED(counts(sce)) + 1)

        # Registration of required information into metadata(sce)
        settingTGIF(sce, gmt, reducedDimNames="PCA",
            assayNames="normcounts")

        # Functional Annotation based on jNMF
        calcTGIF(sce, ndim=7)

        # HTML Reprt
        reportTGIF(sce,
            html.open=TRUE,
            title="scTGIF Report for DistalLungEpithelium dataset",
            author="Koki Tsuyuzaki")
    }
if(interactive()){
        # Package loading
        library("SingleCellExperiment")
        library("GSEABase")
        library("msigdbr")

        # Test data
        data("DistalLungEpithelium")
        data("pca.DistalLungEpithelium")
        data("label.DistalLungEpithelium")

        # Test data
        par(ask=FALSE)
        plot(pca.DistalLungEpithelium, col=label.DistalLungEpithelium, pch=16,
            main="Distal lung epithelium dataset", xlab="PCA1",
            ylab="PCA2", bty="n")
        text(0.1, 0.05, "AT1", col="#FF7F00", cex=2)
        text(0.07, -0.15, "AT2", col="#E41A1C", cex=2)
        text(0.13, -0.04, "BP", col="#A65628", cex=2)
        text(0.125, -0.15, "Clara", col="#377EB8", cex=2)
        text(0.09, -0.2, "Cilliated", col="#4DAF4A", cex=2)

        # Load the gmt file from MSigDB
        # Only "Entrez Gene ID" can be used in scTGIF
        # e.g. gmt <- GSEABase::getGmt(
        #     "/PATH/YOU/SAVED/THE/GMTFILES/h.all.v6.0.entrez.gmt")
        # Here we use msigdbr to retrieve mouse gene sets

        # Mouse gene set (NCBI Gene ID)
        m_df <- msigdbr(species = "Mus musculus", category = "H")[,
            c("gs_name", "entrez_gene")]

        # Convert to GeneSetCollection
        hallmark = unique(m_df$gs_name)
        gsc <- lapply(hallmark, function(h){
            target = which(m_df$gs_name == h)
            geneIds = unique(as.character(m_df$entrez_gene[target]))
            GeneSet(setName=h, geneIds)
        })
        gmt <- GeneSetCollection(gsc)

        # SingleCellExperiment-class
        sce <- SingleCellExperiment(
            assays = list(counts = DistalLungEpithelium))
        reducedDims(sce) <- SimpleList(PCA=pca.DistalLungEpithelium)

        # User's Original Normalization Function
        CPMED <- function(input){
            libsize <- colSums(input)
            median(libsize) * t(t(input) / libsize)
        }
        # Normalization
        normcounts(sce) <- log10(CPMED(counts(sce)) + 1)

        # Registration of required information into metadata(sce)
        settingTGIF(sce, gmt, reducedDimNames="PCA",
            assayNames="normcounts")

        # Functional Annotation based on jNMF
        calcTGIF(sce, ndim=7)

        # HTML Reprt
        reportTGIF(sce,
            html.open=TRUE,
            title="scTGIF Report for DistalLungEpithelium dataset",
            author="Koki Tsuyuzaki")
    }

Paramter setting for scTGIF

Description

All parameters is saved to metadata slot of SingleCellExperiment object.

Usage

settingTGIF(sce, gmt, reducedDimNames, assayNames="counts", nbins=40)
settingTGIF(sce, gmt, reducedDimNames, assayNames="counts", nbins=40)

Arguments

`sce`	A object generated by instantization of SingleCellExperiment-class.
`gmt`	Object generated from GSEABase::getGmt function. GMT file can be downloaded from MSigDB web (site http://software.broadinstitute.org/gsea/login.jsp#msigdb). Please confirm that the gmt file contains Human Entrez Gene ID, not gene symbol. Also confirm that the DataMatrix has Human Entrez Gene ID.
`reducedDimNames`	The names of reducedDim(sce) that user want use in scTGIF.
`assayNames`	The unit of gene expression for using scTGIF (e.g. normcounts, cpm...etc) (Default: "counts").
`nbins`	The number of bins of schex (Default: 40).

Value

The result is saved to metadata slot of SingleCellExperiment object.

Author(s)

Koki Tsuyuzaki [aut, cre]

Examples

showMethods("settingTGIF")
showMethods("settingTGIF")

Package 'scTGIF'

Help Index

Cell type annotation for unannotated single-cell RNA-Seq data

Description

Details

Author(s)

References

Function for connecting cellular patterns and functional patterns using jNMF

Description

Usage

Arguments

Value

Author(s)

Examples

A function to convert the CellMarker data to GMT files.

Description

Usage

Arguments

Value

Author(s)

Examples

A function to change the row names of a matrix.

Description

Usage

Arguments

Value

Author(s)

Examples

Gene expression matrix of DistalLungEpithelium dataset containing five cluster.

Description

Usage

Source

References

Examples

Cellular label of DistalLungEpithelium dataset containing five cluster.

Description

Usage

References

Examples

The result of PCA of the DistalLungEpithelium dataset.

Description

Usage

References

Examples

Function for reporting the result of calcTGIF function

Description

Usage

Arguments

Value

Author(s)

Examples

Paramter setting for scTGIF

Description

Usage

Arguments

Value

Author(s)

Examples

Function for reporting the result of `calcTGIF` function