Package 'scTensor' reference manual

Title:	Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition
Description:	The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.
Authors:	Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut]
Maintainer:	Koki Tsuyuzaki <[email protected]>
License:	Artistic-2.0
Version:	2.17.0
Built:	2024-12-27 06:21:11 UTC
Source:	https://github.com/bioc/scTensor

Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition

Description

The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.

Details

The DESCRIPTION file:

Package:	scTensor
Type:	Package
Title:	Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition
Version:	2.17.0
Authors@R:	c(person("Koki", "Tsuyuzaki", role = c("aut", "cre"), email = "[email protected]"), person("Kozo", "Nishida", role = "aut", email = "[email protected]"))
Depends:	R (>= 4.1.0)
Imports:	methods, RSQLite, igraph, S4Vectors, plotly, reactome.db, AnnotationDbi, SummarizedExperiment, SingleCellExperiment, nnTensor (>= 1.1.5), ccTensor (>= 1.0.2), rTensor (>= 1.4.8), abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi (>= 1.29.2), grDevices, graphics, stats, utils, outliers, Category, meshr (>= 1.99.1), GOstats, ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork, schex, ggplot2
Suggests:	testthat, LRBaseDbi, Seurat, scTGIF, Homo.sapiens, AnnotationHub
Description:	The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.
License:	Artistic-2.0
biocViews:	DimensionReduction, SingleCell, Software, GeneExpression
VignetteBuilder:	knitr
Config/pak/sysreqs:	libfontconfig1-dev libfreetype6-dev libgdal-dev gdal-bin libgeos-dev libglpk-dev make libmagick++-dev gsfonts libicu-dev libpng-dev libxml2-dev libssl-dev libproj-dev libsqlite3-dev libudunits2-dev libnode-dev
Repository:	https://bioc.r-universe.dev
RemoteUrl:	https://github.com/bioc/scTensor
RemoteRef:	HEAD
RemoteSha:	dceaf02b3939a0212a91f68c96f2dbb9ab2bf659
Author:	Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut]
Maintainer:	Koki Tsuyuzaki <[email protected]>

Index of help topics:

CCSParams-class         Class "CCSParams"
GermMale                The matrix which is used as test data of
                        scTensor.
cellCellDecomp          Performing scTensor
cellCellRanks           Rank estimation of the CCI-tensor
cellCellReport          HTML report of the result of scTensor
cellCellSetting         Parameter setting for scTensor
cellCellSimulate        Parameter Simulate for scTensor
getParam                Get a parameter
labelGermMale           The vector contains the celltype information
                        and color scheme of GermMale
m                       The gene-wise mean vector of Quartz-Seq data.
newCCSParams            New Params
scTensor-package        Detection of cell-cell interaction from
                        single-cell RNA-seq dataset by tensor
                        decomposition
setParam                Set a parameter
tsneGermMale            The result of Rtsne against GermMale
v                       The gene-wise variance vector of Quartz-Seq
                        data.

Author(s)

Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut]

Maintainer: Koki Tsuyuzaki <[email protected]>

Examples

ls("package:scTensor")
ls("package:scTensor")

Class "CCSParams"

Description

The parameter object to be specified against cellCellSimulate function.

Objects from the Class

Objects can be created by calls of the form new("CCSParams", ...).

Slots

nGene:: The number of genes.
nCell:: The number of cells.
cciInfo:: The parameter to describe the CCI.
lambda:: The parameter for dropout simulation.
seed:: The seed for using random numbers.

Methods

newCCSParams: Generator of CCSParams object.
getParam: Getter function of the slot in CCSParams object.
setParam<-: Setter function of the slot in CCSParams object.

Performing scTensor

Description

All parameters is saved to metadata slot of SingleCellExperiment object.

Usage

cellCellDecomp(sce, algorithm=c("ntd2", "ntd", "nmf", "cx", "pearson",
    "spearman", "distance", "pearson.lr", "spearman.lr", "distance.lr",
    "pcomb", "label.permutation", "cabello.aguilar", "halpern"), ranks=c(3,3), rank=3, thr1=log2(5), thr2=25, thr3=0.95, L1_A=0, L2_A=0, verbose=FALSE,
    centering=TRUE, mergeas=c("mean", "sum"), outerfunc=c("*", "+"),
    comb=c("random", "all"), num.sampling=100, num.perm=1000, assayNames = "counts", decomp=TRUE)
cellCellDecomp(sce, algorithm=c("ntd2", "ntd", "nmf", "cx", "pearson",
    "spearman", "distance", "pearson.lr", "spearman.lr", "distance.lr",
    "pcomb", "label.permutation", "cabello.aguilar", "halpern"), ranks=c(3,3), rank=3, thr1=log2(5), thr2=25, thr3=0.95, L1_A=0, L2_A=0, verbose=FALSE,
    centering=TRUE, mergeas=c("mean", "sum"), outerfunc=c("*", "+"),
    comb=c("random", "all"), num.sampling=100, num.perm=1000, assayNames = "counts", decomp=TRUE)

Arguments

`sce`	The object generated by instantization of SingleCellExperiment-class.
`algorithm`	Algorithm for constrcting cell-cell similarity matrix. "ntd2", "ntd", "nmf", "cx", "pearson", "spearman", "distance", "pearson.lr", "spearman.lr", "distance.lr", "pcomb" or "label.permutation" can be specified (Default: ntd2).
`ranks`	The size of the core tensor decomposed by NTD. Each element means (Number of Ligand-Cell Pattern, Number of Receptor-Cell Pattern, Number of LR-pairs Pattern) (Default: c(3,3)).
`rank`	The number of low dimension of NMF (Default: 3).
`thr1`	The threshold used by pcomb (Default: log2(5)).
`thr2`	The threshold used by pcomb (Default: 25).
`thr3`	The threshold used by cx (Default: 0.95).
`L1_A`	The parameter to control the sparseness (Default: 0).
`L2_A`	The parameter to control the outlier (Default: 0).
`verbose`	The verbose parameter for nnTensor::NTD (Default: FALSE).
`centering`	When the value is TRUE, input matrix is summarized as celltype-level vectors (Default: TRUE).
`mergeas`	When the centering is TRUE, "sum" (celltype-level sum vector) or "mean" (celltype-level average vector) is calculated (Default: "sum").
`outerfunc`	When the centering is TRUE, "+" (Kronecker sum) or "*" (Kronecker product) is calculated (Default: "+").
`comb`	When the centering is FALSE, "random" (random cell-cell pairing) or "all" (all possible cell-cell pairing) is calculed (Default: "random").
`num.sampling`	The number of random sampling used (Default: 100).
`num.perm`	The number of the permutation in label permutation test (Default: 1000).
`assayNames`	The unit of gene expression for using scTensor (e.g. normcounts, cpm...etc) (Default: "counts").
`decomp`	When the value is TRUE, cell-cell interaction tensor is decomposed (Default: TRUE).

Value

The result is saved to metadata slot of SingleCellExperiment object.

Author(s)

Koki Tsuyuzaki

Examples

showMethods("cellCellDecomp")
showMethods("cellCellDecomp")

Rank estimation of the CCI-tensor

Description

SVD is performed in each mode.

Usage

cellCellRanks(sce, centering=TRUE,
    mergeas=c("mean", "sum"), outerfunc=c("*", "+"), comb=c("random", "all"),
    num.sampling=100, num.perm=1000, assayNames = "counts", verbose=FALSE,
    num.iter1=5, num.iter2=5, num.iter3=NULL)
cellCellRanks(sce, centering=TRUE,
    mergeas=c("mean", "sum"), outerfunc=c("*", "+"), comb=c("random", "all"),
    num.sampling=100, num.perm=1000, assayNames = "counts", verbose=FALSE,
    num.iter1=5, num.iter2=5, num.iter3=NULL)

Arguments

`sce`	A object generated by instantization of SingleCellExperiment-class.
`centering`	When the value is TRUE, input matrix is summarized as celltype-level vectors (Default: TRUE).
`mergeas`	When the centering is TRUE, "mean" (celltype-level mean vector) or "sum" (celltype-level sum vector) is calculated (Default: "mean").
`outerfunc`	When the centering is TRUE, "*" (Kronecker product) or "+" (Kronecker sum) or is calculated (Default: "+").
`comb`	When the centering is FALSE, "random" (random cell-cell pairing) or "all" (all possible cell-cell pairing) is calculed (Default: "random").
`num.sampling`	The number of random sampling used (Default: 100).
`num.perm`	The number of the permutation in label permutation test (Default: 1000).
`assayNames`	The unit of gene expression for using scTensor (e.g. normcounts, cpm...etc) (Default: "counts").
`verbose`	The verbose parameter for nnTensor::NTD (Default: FALSE).
`num.iter1`	The number of iteration to estimate the rank of mode-1 matricised data tensor (Default: 5).
`num.iter2`	The number of iteration to estimate the rank of mode-2 matricised data tensor (Default: 5).
`num.iter3`	The number of iteration to estimate the rank of mode-3 matricised data tensor (Default: NULL).

Value

RSS: A list with three elements, in which each element means the average reconstructed error in each rank. selected: A vector with three elements, in which each element means the estimated ranks in mode-1, 2 and 3 matricization.

Author(s)

Koki Tsuyuzaki

Examples

showMethods("cellCellRanks")
showMethods("cellCellRanks")

HTML report of the result of scTensor

Description

The result is saved as HTML report which contains with multiple files.

Usage

cellCellReport(sce, reducedDimNames,
    out.dir=tempdir(), html.open=FALSE,
    title="The result of scTensor",
    author="The person who runs this script", assayNames = "counts", thr=100,
    top="full", p=0.05, upper=20,
    goenrich=TRUE, meshenrich=TRUE, reactomeenrich=TRUE,
    doenrich=TRUE, ncgenrich=TRUE, dgnenrich=TRUE, nbins=40)
cellCellReport(sce, reducedDimNames,
    out.dir=tempdir(), html.open=FALSE,
    title="The result of scTensor",
    author="The person who runs this script", assayNames = "counts", thr=100,
    top="full", p=0.05, upper=20,
    goenrich=TRUE, meshenrich=TRUE, reactomeenrich=TRUE,
    doenrich=TRUE, ncgenrich=TRUE, dgnenrich=TRUE, nbins=40)

Arguments

`sce`	A object generated by instantization of SingleCellExperiment-class.
`reducedDimNames`	The name of two-dimentional data saved in reducedDimNames slot of SingleCellExperiment object.
`out.dir`	The output directory for saving HTML report (out.dir: tempdir()).
`html.open`	Whether the result of HTML report is opened when the calculation is finished (Default: FALSE).
`title`	The title of HTML report (Default: "The result of scTensor").
`author`	The author of HTML report (Default: "The person who runs this script").
`assayNames`	The unit of gene expression for using scTensor (e.g. normcounts, cpm...etc) (Default: "counts").
`thr`	The threshold for selection of top pecentage of core tensor elements (Default: 100 (1 to 100)).
`top`	top genes in each (,,*)-pattern which are selected and summarized in the report (Default: "full")
`p`	The threshold of p-value of the enrichment analysis (Default: 1E-2)
`upper`	The maxium number of HTML reports generates (Default: 20)
`goenrich`	Whether GO-Enrichment analysis is performed (Default: TRUE)
`meshenrich`	Whether MeSH-Enrichment analysis is performed (Default: TRUE)
`reactomeenrich`	Whether Reactome-Enrichment analysis is performed (Default: TRUE)
`doenrich`	Whether DO-Enrichment analysis is performed (Default: TRUE)
`ncgenrich`	Whether NCG-Enrichment analysis is performed (Default: TRUE)
`dgnenrich`	Whether DGN-Enrichment analysis is performed (Default: TRUE)
`nbins`	The number of bins used for the two dimensional plot of schex (Default: 40)

Value

The result is saved as HTML report which contains with multiple files.

Author(s)

Koki Tsuyuzaki

Examples

	if(interactive()){
	# Package Loading
	library("SingleCellExperiment")
	library("AnnotationHub")
	if(!require(LRBaseDbi)){
	    BiocManager::install("LRBaseDbi")
	    library(LRBaseDbi)
	}
	ah <- AnnotationHub()
	dbfile <- query(ah, c("LRBaseDb", "Homo sapiens", "v002"))[[1]]
	LRBase.Hsa.eg.db <- LRBaseDbi::LRBaseDb(dbfile)

	# Data Loading
	data(GermMale)
	data(labelGermMale)
	data(tsneGermMale)

	# SingleCellExperiment Object
	sce <- SingleCellExperiment(assays=list(counts = GermMale))
	reducedDims(sce) <- SimpleList(TSNE=tsneGermMale$Y)

	# 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)
	cellCellSetting(sce, LRBase.Hsa.eg.db, names(labelGermMale))

	# Rank Estimation
	rks <- cellCellRanks(sce, assayNames="normcounts")

	# CCI Tensor Decomposition
	set.seed(1234)
	cellCellDecomp(sce, ranks=rks$selected, assayNames="normcounts")

	# HTML Report
	options(device.ask.default = FALSE)
	cellCellReport(sce, reducedDimNames="TSNE",
        out.dir=tempdir(), html.open=FALSE,
        title="The result of scTensor",
        author="The person who runs this script",
        assayNames="counts", thr=100,
        top="full", p=0.05, upper=20,
        goenrich=TRUE, meshenrich=TRUE, reactomeenrich=TRUE,
        doenrich=TRUE, ncgenrich=TRUE, dgnenrich=TRUE, nbins=40)
    }else{
        showMethods("cellCellReport")
    }
if(interactive()){
	# Package Loading
	library("SingleCellExperiment")
	library("AnnotationHub")
	if(!require(LRBaseDbi)){
	    BiocManager::install("LRBaseDbi")
	    library(LRBaseDbi)
	}
	ah <- AnnotationHub()
	dbfile <- query(ah, c("LRBaseDb", "Homo sapiens", "v002"))[[1]]
	LRBase.Hsa.eg.db <- LRBaseDbi::LRBaseDb(dbfile)

	# Data Loading
	data(GermMale)
	data(labelGermMale)
	data(tsneGermMale)

	# SingleCellExperiment Object
	sce <- SingleCellExperiment(assays=list(counts = GermMale))
	reducedDims(sce) <- SimpleList(TSNE=tsneGermMale$Y)

	# 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)
	cellCellSetting(sce, LRBase.Hsa.eg.db, names(labelGermMale))

	# Rank Estimation
	rks <- cellCellRanks(sce, assayNames="normcounts")

	# CCI Tensor Decomposition
	set.seed(1234)
	cellCellDecomp(sce, ranks=rks$selected, assayNames="normcounts")

	# HTML Report
	options(device.ask.default = FALSE)
	cellCellReport(sce, reducedDimNames="TSNE",
        out.dir=tempdir(), html.open=FALSE,
        title="The result of scTensor",
        author="The person who runs this script",
        assayNames="counts", thr=100,
        top="full", p=0.05, upper=20,
        goenrich=TRUE, meshenrich=TRUE, reactomeenrich=TRUE,
        doenrich=TRUE, ncgenrich=TRUE, dgnenrich=TRUE, nbins=40)
    }else{
        showMethods("cellCellReport")
    }

Parameter setting for scTensor

Description

All parameters is saved to metadata slot of SingleCellExperiment object.

Usage

cellCellSetting(sce, lrbase, label, lr.evidence="known", color=NULL)
cellCellSetting(sce, lrbase, label, lr.evidence="known", color=NULL)

Arguments

`sce`	A object generated by instantization of SingleCellExperiment-class.
`lrbase`	Ligand-Receptor database (LRBase.XXX.eg.db-type package).
`label`	Cellular label information for distingusishing which cells belong to common celltypes.
`lr.evidence`	The evidence code for L-R pair list (Default: "known"). When you specify "known", DLRP, IUPHAR, HPMR, CELLPHONEDB, SINGLECELLSIGNALR are searched, and other databases are searched, when you specify "putative". You can also specify multiple databases at once (e.g. c("SWISSPROT_STRING", "TREMBL_STRING")). cf. https://github.com/rikenbit/lrbase-workflow
`color`	Color scheme for adding color against the cells (Default: NULL). If the value is not specified, automatically the color vector is generated.

Value

The result is saved to metadata slot of SingleCellExperiment object.

Author(s)

Koki Tsuyuzaki

Examples

showMethods("cellCellSetting")
showMethods("cellCellSetting")

Parameter Simulate for scTensor

Description

All parameters is saved to metadata slot of SingleCellExperiment object.

Usage

cellCellSimulate(params = newCCSParams(), verbose = TRUE)
cellCellSimulate(params = newCCSParams(), verbose = TRUE)

Arguments

`params`	A parameter object generated by newCCSParams().
`verbose`	Whether the message is outputted or not (Default: TRUE).

Value

A list object containing simcount, LR, and celltype. simcount is the synthetic count matrix, LR is the synthetic ligand-receptor pair list, and celltype is the vector to specity the celltype of the each column of simcount.

Author(s)

Koki Tsuyuzaki

Examples

showMethods("cellCellSimulate")
showMethods("cellCellSimulate")

The matrix which is used as test data of scTensor.

Description

A matrix with 242 rows (genes) * 852 columns (cells).

Usage

data(GermMale)data(GermMale)

Details

The data matrix is downloaded from GEO Series GSE86146 (https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE86146&format=file). Only male data is extracted and then the gene symbol is converted to NCBI Gene ID by Homo.sapiens package.

For saving the package size, the number of genes are strictlly reduced by the standard of highlly variable genes with threshold of p-value is 1E-300.

References

Li L. and Dong J. and Yan L. and Yong J. et al. (2017) Single-Cell RNA-Seq Analysis Maps Development of Human Germline Cells and Gonadal Niche Interactions. Cell Stem Cell, 20(6): 858-873

Examples

data(GermMale)
data(GermMale)

Get a parameter

Description

Accessor function for getting parameter values.

Usage

getParam(object, name)

## S4 method for signature 'CCSParams'
getParam(object, name)
getParam(object, name)

## S4 method for signature 'CCSParams'
getParam(object, name)

Arguments

`object`	object to get parameter from.
`name`	name of the parameter to get.

Value

The extracted parameter value

Examples

params <- newCCSParams()

getParam(params, "nGene")
getParam(params, "nCell")
getParam(params, "cciInfo")
getParam(params, "lambda")
getParam(params, "seed")
params <- newCCSParams()

getParam(params, "nGene")
getParam(params, "nCell")
getParam(params, "cciInfo")
getParam(params, "lambda")
getParam(params, "seed")

The vector contains the celltype information and color scheme of GermMale

Description

A vector with 852 length (cells).

Usage

data(labelGermMale)data(labelGermMale)

Details

The Cluster label is downloaded from original paper page of Cell Stem Cell (https://www.sciencedirect.com/science/article/pii/S1934590917300784)

References

Li L. and Dong J. and Yan L. and Yong J. et al. (2017) Single-Cell RNA-Seq Analysis Maps Development of Human Germline Cells and Gonadal Niche Interactions. Cell Stem Cell, 20(6): 858-873

Examples

data(labelGermMale)
data(labelGermMale)

The gene-wise mean vector of Quartz-Seq data.

Description

This data is internally used in cellCellSimulate function.

Usage

data(m)data(m)

Examples

data(m)
data(m)

New Params

Description

Create a new CCSParams object.

Usage

newCCSParams()
newCCSParams()

Arguments

Nothing.

Value

New Params object.

Examples

params <- newCCSParams()
params <- newCCSParams()

Set a parameter

Description

Function for setting parameter values.

Usage

setParam(object, name) <- value
## S4 method for signature 'CCSParams'
setParam(object, name, value)
setParam(object, name) <- value
## S4 method for signature 'CCSParams'
setParam(object, name, value)

Arguments

`object`	object to set parameter in.
`name`	name of the parameter to set.
`value`	value to set the paramter to.

Value

Object with new parameter value.

Examples

params <- newCCSParams()

setParam(params, "nGene") <- 20000
setParam(params, "nCell") <- c(12, 43, 323)
setParam(params, "cciInfo") <- list(nPair=2000,
                                   CCI1=list(
                                       LPattern=c(1,0,0),
                                       RPattern=c(0,1,1),
                                       nGene=100,
                                       fc="E10"),
                                   CCI2=list(
                                       LPattern=c(0,0,1),
                                       RPattern=c(1,1,1),
                                       nGene=200,
                                       fc="E10"),
                                   CCI3=list(
                                       LPattern=c(1,1,1),
                                       RPattern=c(1,0,1),
                                       nGene=300,
                                       fc="E10")
                                   )
setParam(params, "lambda") <- 0.1
setParam(params, "seed") <- 111
params <- newCCSParams()

setParam(params, "nGene") <- 20000
setParam(params, "nCell") <- c(12, 43, 323)
setParam(params, "cciInfo") <- list(nPair=2000,
                                   CCI1=list(
                                       LPattern=c(1,0,0),
                                       RPattern=c(0,1,1),
                                       nGene=100,
                                       fc="E10"),
                                   CCI2=list(
                                       LPattern=c(0,0,1),
                                       RPattern=c(1,1,1),
                                       nGene=200,
                                       fc="E10"),
                                   CCI3=list(
                                       LPattern=c(1,1,1),
                                       RPattern=c(1,0,1),
                                       nGene=300,
                                       fc="E10")
                                   )
setParam(params, "lambda") <- 0.1
setParam(params, "seed") <- 111

The result of Rtsne against GermMale

Description

A List contains some parameters and the result of Rtsne function.

Usage

data(tsneGermMale)data(tsneGermMale)

Details

Rtsne is performed as follows.

library(Rtsne) set.seed(123) tsneGermMale <- Rtsne(dist(t(GermMale)), is_distance=TRUE, perplexity=40)

References

Li L. and Dong J. and Yan L. and Yong J. et al. (2017) Single-Cell RNA-Seq Analysis Maps Development of Human Germline Cells and Gonadal Niche Interactions. Cell Stem Cell, 20(6): 858-873

Examples

data(tsneGermMale)
data(tsneGermMale)

The gene-wise variance vector of Quartz-Seq data.

Description

This data is internally used in cellCellSimulate function.

Usage

data(v)data(v)

Examples

data(v)
data(v)

Package 'scTensor'

Help Index

Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition

Description

Details

Author(s)

See Also

Examples

Class "CCSParams"

Description

Objects from the Class

Slots

Methods

See Also

Performing scTensor

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Rank estimation of the CCI-tensor

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

HTML report of the result of scTensor

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Parameter setting for scTensor

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Parameter Simulate for scTensor

Description

Usage

Arguments

Value

Author(s)

Examples

The matrix which is used as test data of scTensor.

Description

Usage

Details

References

See Also

Examples

Get a parameter

Description

Usage

Arguments

Value

Examples

The vector contains the celltype information and color scheme of GermMale

Description

Usage

Details

References

See Also

Examples

The gene-wise mean vector of Quartz-Seq data.

Description

Usage

Examples

New Params

Description

Usage