Detection and visualization of cell-cell interactions using `LRBaseDbi`, `LRBase.XXX.eg.db`, and `scTensor` package

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

Introduction

About Cell-Cell Interaction (CCI)

Due to the rapid development of single-cell RNA-Seq (scRNA-Seq) technologies, wide variety of cell types such as multiple organs of a healthy person, stem cell niche and cancer stem cell have been found. Such complex systems are composed of communication between cells (cell-cell interaction or CCI).

Many CCI studies are based on the ligand-receptor (L-R)-pair list of FANTOM5 project^[Jordan A. Ramilowski, A draft network of ligand-receptor-mediated multicellular signaling in human, Nature Communications, 2015] as the evidence of CCI (http://fantom.gsc.riken.jp/5/suppl/Ramilowski_et_al_2015/data/PairsLigRec.txt). The project proposed the L-R-candidate genes by following two reasons.

1. Subcellular Localization
   1. Known Annotation (UniProtKB and HPRD) : The term "Secreted" for candidate ligand genes and "Plasma Membrane" for candidate receptor genes
   2. Computational Prediction (LocTree3 and PolyPhobius)
2. Physical Binding of Proteins : Experimentally validated PPI (protein-protein interaction) information of HPRD and STRING

The project also merged the data with previous L-R database such as IUPHAR/DLRP/HPMR and filter out the list without PMIDs.

Besides, the recent L-R databases such as CellPhoneDB and SingleCellSignalR manually curated L-R pairs, which are not listed in IUPHAR/DLRP/HPMR.

In Bader Laboratory, many putative L-R databases are predicted by their standards.

In our framework, we expanded such L-R databases for 134 organisms based on the ortholog relationships. We implemented such a framework as multiple R/Bioconductor annotation packages for sustainable maintenance (r Biocpkg("LRBaseDbi") and LRBase.XXX.eg.db-type packages (Figure 1). XXX is the abbreviation of the scientific name of organisms such as r Biocpkg("LRBase.Hsa.eg.db") for L-R database of Homo sapiens. Besides, we also developed r Biocpkg("scTensor"), which is a method to detect CCI and the CCI-related L-R pairs simultaneously. This document provides the way to use r Biocpkg("LRBaseDbi"), LRBase.XXX.eg.db-type packages, and r CRANpkg("scTensor") package.

Dependencies of CCI-related Packages

Our framework is composed of some annotation packages and software packages (Figure 1).

r Biocpkg("LRBaseDbi") package defines the class "LRBaseDb" for LRBase.XXX.eg.db-type packages such as r Biocpkg("LRBase.Hsa.eg.db") or r Biocpkg("LRBase.Mmu.eg.db") and unify the object's behavior such as column function. r CRANpkg("nnTensor") which is a CRAN package, performs non-negative tensor decomposition, and r Biocpkg("scTensor") internally imports the nnTensor. r CRANpkg("scTensor") constructs CCI-tensor from a LRBase.XXX.eg.db package and scRNA-Seq dataset, decomposes to core tensor and factor matrices, and outputs HTML reports. See the following usage section for the details.

Usage

LRBase.XXX.eg.db-type packages (ligand-receptor database for 134 organisms)

To create the L-R-list of 134 organisms, we introduced 36 approarches including known/putative L-R pairing. Please see the evidence code of lrbase-workflow, which is the Snakemake workflow to create LRBase.XXX.eg.db. https://github.com/rikenbit/lrbase-workflow

columns, keytypes, keys, and select

Some data access functions are available for LRBase.XXX.eg.db-type packages. Any data table are retrieved by 4 functions defined by r Biocpkg("AnnotationDbi"); columns, keytypes, keys, and select and commonly implemented by r Biocpkg("LRBaseDbi") package. columns returns the rows which we can retrieve in LRBase.XXX.eg.db-type packages. keytypes returns the rows which can be used as the optional parameter in keys and select functions against LRBase.XXX.eg.db-type packages. keys function returns the value of keytype. select function returns the rows in particular columns, which are having user-specified keys. This function returns the result as a dataframe. See the vignette of r Biocpkg("AnnotationDbi") for more details.

if(!require(LRBase.Hsa.eg.db)){
    BiocManager::install("LRBase.Hsa.eg.db")
    suppressPackageStartupMessages(library(LRBase.Hsa.eg.db))
}

columns(LRBase.Hsa.eg.db)
keytypes(LRBase.Hsa.eg.db)
key_HSA <- keys(LRBase.Hsa.eg.db, keytype="GENEID_L")
head(select(LRBase.Hsa.eg.db, keys=key_HSA[1:2],
            columns=c("GENEID_L", "GENEID_R"), keytype="GENEID_L"))

Other functions

Other additional functions like species, nomenclature, and listDatabases are available. In each LRBase.XXX.eg.db-type package, species function returns the common name and nomenclature returns the scientific name. listDatabases function returns the source of data. dbInfo returns the information of the package. dbfile returns the directory where sqlite file is stored. dbschema returns the schema of the database. dbconn returns the connection to the sqlite database.

lrPackageName(LRBase.Hsa.eg.db)
lrNomenclature(LRBase.Hsa.eg.db)
species(LRBase.Hsa.eg.db)
lrListDatabases(LRBase.Hsa.eg.db)
lrVersion(LRBase.Hsa.eg.db)

dbInfo(LRBase.Hsa.eg.db)
dbfile(LRBase.Hsa.eg.db)
dbschema(LRBase.Hsa.eg.db)
dbconn(LRBase.Hsa.eg.db)

Combined with dbGetQuery function of r CRANpkg("RSQLite") package, more complicated queries also can be submitted.

suppressPackageStartupMessages(library("RSQLite"))
dbGetQuery(dbconn(LRBase.Hsa.eg.db),
  "SELECT * FROM DATA WHERE GENEID_L = '9068' AND GENEID_R = '14' LIMIT 10")

LRBaseDbi (Class definition and meta-packaging)

r Biocpkg("LRBaseDbi") regulates the class definition of LRBaseDb object instantiated from LRBaseDb-class. Besides, r Biocpkg("LRBaseDbi") the package generates user's original LRBase.XXX.eg.db-type packages by makeLRBasePackage function. This function is inspired by our previous package r Biocpkg("MeSHDbi"), which constructs user's original MeSH.XXX.eg.db-type packages. Here we call this function "meta"-packaging. The 12 LRBase.XXX.eg.db-type packages described above are also generated by this "meta"-packaging. In this case, the only user have to specify are 1. an L-R-list containing the columns "GENEID_L" (ligand NCBI Gene IDs) and "GENEID_R" (receptor NCBI Gene IDs) and 2. a meta information table describing the L-R-list. makeLRBasePackage function generates LRBase.XXX.eg.db like below. The gene identifier is limited as NCBI Gene ID for now.

suppressPackageStartupMessages(library("LRBaseDbi"))

example("makeLRBasePackage")

Although any package name is acceptable, note that if the organism that user summarized L-R-list is also described above (Table \@ref(tab:table)), same XXX-character is recommended. This is because of the HTML report function described later identifies the XXX-character and if the XXX is corresponding to the 12 organisms, the gene annotation of the generated HTML report will become rich.

scTensor (CCI-tensor construction, decomposition, and HTML reporting)

Combined with LRBase.XXX.eg.db-type package and user's gene expression matrix of scRNA-Seq, r Biocpkg("scTensor") detects CCIs and generates HTML reports for exploratory data inspection. The algorithm of r Biocpkg("scTensor") is as follows.

Firstly, r Biocpkg("scTensor") calculates the celltype-level mean vectors, searches the corresponding pair of genes in the row names of the matrix, and extracted as tow vectors.

Next, the cell type-level mean vectors of ligand expression and that of receptor expression are multiplied as outer product and converted to cell type $\times$ cell type matrix. Here, the multiple matrices can be represented as a three-order "tensor" (Ligand-Cell * Receptor-Cell * L-R-Pair). r Biocpkg("scTensor") decomposes the tensor into a small tensor (core tensor) and two factor matrices. Tensor decomposition is very similar to the matrix decomposition like PCA (principal component analysis). The core tensor is similar to the eigenvalue of PCA; this means that how much the pattern is outstanding. Likewise, three matrices are similar to the PC scores/loadings of PCA; These represent which ligand-cell/receptor-cell/L-R-pair are informative. When the matrices have negative values, interpreting which direction (+/-) is important and which is not, is a difficult and laboring task. That's why, r Biocpkg("scTensor") performs non-negative Tucker2 decomposition (NTD2), which is non-negative version of tensor decomposition (cf. r CRANpkg("nnTensor")).

Finally, the result of NTD2 is summarized as an HTML report. Because most of the plots are visualized by r CRANpkg("plotly") package, the precise information of the plot can be interactively confirmed by user's on-site web browser. The two factor matrices can be interactively viewed and which cell types and which L-R-pairs are likely to be interacted each other. The mode-3 (LR-pair direction) sum of the core tensor is calculated and visualized as Ligand-Receptor Patterns. Detail of (Ligand-Cell, Receptor-Cell, L-R-pair) Patterns are also visualized.

Creating a SingleCellExperiment object

Here, we use the scRNA-Seq dataset of male germline cells and somatic cells$^{3}$ GSE86146 as demo data. For saving the package size, the number of genes is strictly reduced by the standard of highly variable genes with a threshold of the p-value are 1E-150 (cf. Identifying highly variable genes). That's why we won't argue about the scientific discussion of the data here.

We assume that user has a scRNA-Seq data matrix containing expression count data summarised at the level of the gene. First, we create a r Biocpkg("SingleCellExperiment") object containing the data. The rows of the object correspond to features, and the columns correspond to cells. The gene identifier is limited as NCBI Gene ID for now.

To improve the interpretability of the following HTML report, we highly recommend that user specifies the two-dimensional data of input data (e.g. PCA, t-SNE, or UMAP). Such information is easily specified by reducedDims function of r Biocpkg("SingleCellExperiment") package and is saved to reducedDims slot of SingleCellExperiment object (Figure \@ref(fig:cellCellSetting)).

suppressPackageStartupMessages(library("scTensor"))
suppressPackageStartupMessages(library("SingleCellExperiment"))

data(GermMale)
data(labelGermMale)
data(tsneGermMale)

sce <- SingleCellExperiment(assays=list(counts = GermMale))
reducedDims(sce) <- SimpleList(TSNE=tsneGermMale$Y)
plot(reducedDims(sce)[[1]], col=labelGermMale, pch=16, cex=2,
  xlab="Dim1", ylab="Dim2", main="Germline, Male, GSE86146")
legend("topleft", legend=c(paste0("FGC_", 1:3), paste0("Soma_", 1:4)),
  col=c("#9E0142", "#D53E4F", "#F46D43", "#ABDDA4", "#66C2A5", "#3288BD", "#5E4FA2"),
  pch=16)

Note that if you want to use scTensor framework against other species such as mouse or rat, load corresponding LRBase.XXX.eg.db and MeSH.XXX.eg.db packages.

For example, if your scRNA-Seq dataset is sampled from Mouse, load r Biocpkg("LRBase.Mmu.eg.db") and r Biocpkg("MeSH.Mmu.eg.db") instead of r Biocpkg("LRBase.Hsa.eg.db") and r Biocpkg("MeSH.Hsa.eg.db").

if(!require(LRBase.Mmu.eg.db)){
    BiocManager::install("LRBase.Mmu.eg.db")
    suppressPackageStartupMessages(library(LRBase.Mmu.eg.db))
}

Parameter setting : cellCellSetting

To perform the tensor decomposition and HTML report, user is supposed to specify

• 1. LRBase.XXX.eg.db
• 1. color vector of each cell
• 1. cell type vector of each cell

to SingleCellExperiment object. The corresponding information is registered to the metadata slot of SingleCellExperiment object by cellCellSetting function.

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

CCI-tensor construction and decomposition : cellCellDecomp

After cellCellSetting, we can perform tensor decomposition by cellCellDecomp. Here the parameter ranks is specified as dimension of core tensor. For example, c(2, 3) means The data tensor is decomposed to 2 ligand-patterns and 3 receptor-patterns.

set.seed(1234)
cellCellDecomp(sce, ranks=c(2,3))

Although user has to specify the rank to perform cellCellDecomp, we implemented a simple rank estimation function based on the eigenvalues distribution of PCA in the matricised tensor in each mode in cellCellRank. rks$selected is also specified as rank parameter of cellCellDecomp.

(rks <- cellCellRanks(sce))
rks$selected

HTML Report : cellCellReport

If cellCellDecomp is properly finished, we can perform cellCellReport function to output the HTML report like below. Please type example(cellCellReport) and the report will be generated in the temporary directory (it costs 5 to 10 minutes). After cellCellReport, multiple R markdown files, compiled HTML files, figures, and R binary file containing the result of analysis are saved to out.dir (Figure 2). For more details, open the index.html by your web browser. Combined with cloud storage service such as Amazon Simple Storage Service (S3), it can be a simple web application and multiple people like collaborators can confirm the same report simultaneously.

Session information {.unnumbered}

sessionInfo()

scTensor documentation built on Nov. 8, 2020, 5 p.m.