Detection and visualization of cell-cell interactions using LRBase and scTensor

This vignette has been changed in BioC 3.14, when each data package (LRBase.XXX.eg.db) is deprecated and the way to provide LRBase data has changed to AnnotationHub-style.

Specification change of LRBase and scTensor from BioC 3.14 (Nov. 2021)

This section is for the users of previous LRBase.XXX.eg.db-type packages and scTensor. The specifications of the LRBase.XXX.eg.db and scTensor have changed significantly since BioC 3.14. Specifically, the distribution of all LRBase.XXX.eg.db-type packages will be abolished, and the policy has been switched to one where the data is placed on a cloud server called AnnotationHub, and users are allowed to retrieve the data only when they really need it. The following are the advantages of this AnnotationHub-style.

  • The installation time of the entire Bioconductor packages will be reduced.
  • Old data will be archived.
  • Data reproducibility is ensured (e.g. the version of the data can be specified, such as “v002”).

Introduction

About Cell-Cell Interaction (CCI) databases

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 project1 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 basises.

  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. The recent L-R databases such as CellPhoneDB and SingleCellSignalR also 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. For the details, check the summary of rikenbit/lrbase-workflow2, which is the Snakemake workflow to create LRBase data in each bi-annual update of Bioconductor.

LRBase and scTensor framework

Our L-R databases (LRBase) are provided a cloud server called AnnotationHub, and users are allowed to retrieve the data only when they really need it. Downloaded data is stored as a cache file on our local machines by the BiocFileCache mechanism. Then, the data is converted to LRBase object by LRBaseDbi. We also developed scTensor, which is a method to detect CCI and the CCI-related L-R pairs simultaneously. This document provides the way to use LRBaseDbi, LRBase objects, and scTensor (Figure 1).

Figure 1 : Workflow of L-R-related packages
Figure 1 : Workflow of L-R-related packages

Usage

LRBase objects (ligand-receptor database for 134 organisms)

To create the LRBase of 134 organisms, we introduced 36 approarches including known/putative L-R pairing. Please see the evidence code of lrbase-workflow3.

Data retrieval from AnnotationHub

First of all, we download the data of LRBase from AnnotationHub. AnnotationHub::AnnotationHub retrieve the metadata of all the data stored in cloud server.

library("AnnotationHub")
## Loading required package: BiocGenerics
## Loading required package: generics
## 
## Attaching package: 'generics'
## The following objects are masked from 'package:base':
## 
##     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
##     setequal, union
## 
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
## 
##     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
##     as.data.frame, basename, cbind, colnames, dirname, do.call,
##     duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
##     mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
##     rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
##     unsplit, which.max, which.min
## Loading required package: BiocFileCache
## Loading required package: dbplyr
ah <- AnnotationHub()
mcols(ah)
## DataFrame with 68904 rows and 15 columns
##                         title           dataprovider                species
##                   <character>            <character>            <character>
## AH5012        Chromosome Band                   UCSC           Homo sapiens
## AH5013            STS Markers                   UCSC           Homo sapiens
## AH5014            FISH Clones                   UCSC           Homo sapiens
## AH5015            Recomb Rate                   UCSC           Homo sapiens
## AH5016           ENCODE Pilot                   UCSC           Homo sapiens
## ...                       ...                    ...                    ...
## AH121957 org.Mmu.eg.db.sqlite ftp://ftp.ncbi.nlm.n..         Macaca mulatta
## AH121958  org.Ce.eg.db.sqlite ftp://ftp.ncbi.nlm.n.. Caenorhabditis elegans
## AH121959  org.Xl.eg.db.sqlite ftp://ftp.ncbi.nlm.n..         Xenopus laevis
## AH121960 org.Sc.sgd.db.sqlite ftp://ftp.ncbi.nlm.n.. Saccharomyces cerevi..
## AH121961  org.Dr.eg.db.sqlite ftp://ftp.ncbi.nlm.n..            Danio rerio
##          taxonomyid       genome            description coordinate_1_based
##           <integer>  <character>            <character>          <integer>
## AH5012         9606         hg19 GRanges object from ..                  1
## AH5013         9606         hg19 GRanges object from ..                  1
## AH5014         9606         hg19 GRanges object from ..                  1
## AH5015         9606         hg19 GRanges object from ..                  1
## AH5016         9606         hg19 GRanges object from ..                  1
## ...             ...          ...                    ...                ...
## AH121957       9544 NCBI genomes NCBI gene ID based a..                  1
## AH121958       6239 NCBI genomes NCBI gene ID based a..                  1
## AH121959       8355 NCBI genomes NCBI gene ID based a..                  1
## AH121960     559292 NCBI genomes NCBI gene ID based a..                  1
## AH121961       7955 NCBI genomes NCBI gene ID based a..                  1
##                      maintainer rdatadateadded          preparerclass
##                     <character>    <character>            <character>
## AH5012   Marc Carlson <mcarls..     2013-03-26 UCSCFullTrackImportP..
## AH5013   Marc Carlson <mcarls..     2013-03-26 UCSCFullTrackImportP..
## AH5014   Marc Carlson <mcarls..     2013-03-26 UCSCFullTrackImportP..
## AH5015   Marc Carlson <mcarls..     2013-03-26 UCSCFullTrackImportP..
## AH5016   Marc Carlson <mcarls..     2013-03-26 UCSCFullTrackImportP..
## ...                         ...            ...                    ...
## AH121957 Bioconductor Maintai..     2025-10-29 OrgDbFromPkgsImportP..
## AH121958 Bioconductor Maintai..     2025-10-29 OrgDbFromPkgsImportP..
## AH121959 Bioconductor Maintai..     2025-10-29 OrgDbFromPkgsImportP..
## AH121960 Bioconductor Maintai..     2025-10-29 OrgDbFromPkgsImportP..
## AH121961 Bioconductor Maintai..     2025-10-29 OrgDbFromPkgsImportP..
##                                  tags  rdataclass              rdatapath
##                                <AsIs> <character>            <character>
## AH5012        cytoBand,UCSC,track,...     GRanges goldenpath/hg19/data..
## AH5013          stsMap,UCSC,track,...     GRanges goldenpath/hg19/data..
## AH5014      fishClones,UCSC,track,...     GRanges goldenpath/hg19/data..
## AH5015      recombRate,UCSC,track,...     GRanges goldenpath/hg19/data..
## AH5016   encodeRegions,UCSC,track,...     GRanges goldenpath/hg19/data..
## ...                               ...         ...                    ...
## AH121957         NCBI,Gene,Annotation       OrgDb ncbi/standard/3.22/o..
## AH121958         NCBI,Gene,Annotation       OrgDb ncbi/standard/3.22/o..
## AH121959         NCBI,Gene,Annotation       OrgDb ncbi/standard/3.22/o..
## AH121960         NCBI,Gene,Annotation       OrgDb ncbi/standard/3.22/o..
## AH121961         NCBI,Gene,Annotation       OrgDb ncbi/standard/3.22/o..
##                       sourceurl   sourcetype
##                     <character>  <character>
## AH5012   rtracklayer://hgdown..   UCSC track
## AH5013   rtracklayer://hgdown..   UCSC track
## AH5014   rtracklayer://hgdown..   UCSC track
## AH5015   rtracklayer://hgdown..   UCSC track
## AH5016   rtracklayer://hgdown..   UCSC track
## ...                         ...          ...
## AH121957 ftp://ftp.ncbi.nlm.n.. NCBI/ensembl
## AH121958 ftp://ftp.ncbi.nlm.n.. NCBI/ensembl
## AH121959 ftp://ftp.ncbi.nlm.n.. NCBI/ensembl
## AH121960 ftp://ftp.ncbi.nlm.n.. NCBI/ensembl
## AH121961 ftp://ftp.ncbi.nlm.n.. NCBI/ensembl

Specifying some keywords in query(), we can find LRBase data in AnnotationHub.

dbfile <- query(ah, c("LRBaseDb", "Homo sapiens"))[[1]]
## downloading 1 resources
## retrieving 1 resource
## loading from cache

AnnotationHub also keeps old data as an archive, so please make sure you have the latest version (e.g. “v002” or higher) when you search for LRBaseDb.

Then, we can convert dbfile into LRBase object by using LRBaseDbi.

library("LRBaseDbi")
## LRBase.XXX.eg.db-type packages are deprecated since Bioconductor 3.14. Use AnnotationHub instead. For details, check the vignette of LRBaseDbi
LRBase.Hsa.eg.db <- LRBaseDbi::LRBaseDb(dbfile)

columns, keytypes, keys, and select

Some data access functions are available for LRBase objects. Any data table are retrieved by 4 functions defined by AnnotationDbi; columns, keytypes, keys, and select and commonly implemented by LRBaseDbi package. columns returns the rows which we can retrieve in LRBase objects. keytypes returns the rows which can be used as the optional parameter in keys and select functions against LRBase objects. 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 AnnotationDbi for more details.

columns(LRBase.Hsa.eg.db)
## [1] "GENEID_L" "GENEID_R" "SOURCEDB" "SOURCEID"
keytypes(LRBase.Hsa.eg.db)
## [1] "GENEID_L" "GENEID_R" "SOURCEDB" "SOURCEID"
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"))
##   GENEID_L GENEID_R
## 1        1     1398
## 2        1     6622
## 3        1      310
## 4        1    10321
## 5        1    10549
## 6      100     1803

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.

lrNomenclature(LRBase.Hsa.eg.db)
## [1] "Homo sapiens"
species(LRBase.Hsa.eg.db)
## [1] "Human"
lrListDatabases(LRBase.Hsa.eg.db)
##             SOURCEDB
## 1           BADERLAB
## 2        CELLPHONEDB
## 3               DLRP
## 4            FANTOM5
## 5               HPMR
## 6             IUPHAR
## 7  SINGLECELLSIGNALR
## 8     SWISSPROT_HPRD
## 9   SWISSPROT_STRING
## 10       TREMBL_HPRD
## 11     TREMBL_STRING
lrVersion(LRBase.Hsa.eg.db)
##        NAME VALUE
## 1 LRVERSION  2018
dbInfo(LRBase.Hsa.eg.db)
##               NAME
## 1       SOURCEDATE
## 2      SOURCENAME1
## 3      SOURCENAME2
## 4      SOURCENAME3
## 5      SOURCENAME4
## 6      SOURCENAME5
## 7      SOURCENAME6
## 8      SOURCENAME7
## 9      SOURCENAME8
## 10     SOURCENAME9
## 11    SOURCENAME10
## 12    SOURCENAME11
## 13      SOURCEURL1
## 14      SOURCEURL2
## 15      SOURCEURL3
## 16      SOURCEURL4
## 17      SOURCEURL5
## 18      SOURCEURL6
## 19      SOURCEURL7
## 20      SOURCEURL8
## 21      SOURCEURL9
## 22     SOURCEURL10
## 23     SOURCEURL11
## 24        DBSCHEMA
## 25 DBSCHEMAVERSION
## 26        ORGANISM
## 27         SPECIES
## 28         package
## 29         Db type
## 30       LRVERSION
## 31           TAXID
##                                                                            VALUE
## 1                                                                     2025-02-03
## 2                                                                           DLRP
## 3                                                                         IUPHAR
## 4                                                                           HPMR
## 5                                                                    CELLPHONEDB
## 6                                                              SINGLECELLSIGNALR
## 7                                                                        FANTOM5
## 8                                                                       BADERLAB
## 9                                                                      SWISSPROT
## 10                                                                          HPRD
## 11                                                                        TREMBL
## 12                                                                        STRING
## 13                                     https://dip.doe-mbi.ucla.edu/dip/DLRP.cgi
## 14                              https://www.guidetopharmacology.org/download.jsp
## 15                                                                              
## 16                                                   https://www.cellphonedb.org
## 17 http://www.bioconductor.org/packages/release/bioc/html/SingleCellSignalR.html
## 18                                                    http://fantom.gsc.riken.jp
## 19                                      http://baderlab.org/CellCellInteractions
## 20                            http://www.uniprot.org/uniprot/?query=reviewed:yes
## 21                                                      http://hprd.org/download
## 22                             http://www.uniprot.org/uniprot/?query=reviewed:no
## 23                                         https://string-db.org/cgi/download.pl
## 24                                                              LRBase.Hsa.eg.db
## 25                                                                           1.0
## 26                                                                  Homo sapiens
## 27                                                                         Human
## 28                                                                 AnnotationDbi
## 29                                                                      LRBaseDb
## 30                                                                          2018
## 31                                                                          9606
dbfile(LRBase.Hsa.eg.db)
##                                                  AH121554 
## "/github/home/.cache/R/AnnotationHub/85216d7531a2_128300"
dbschema(LRBase.Hsa.eg.db)
## [1] "CREATE TABLE DATA (\n  GENEID_L VARCHAR(10) NOT NULL,       -- e.g., 19\n  GENEID_R VARCHAR(10) NOT NULL,       -- e.g., 3763409\n  SOURCEID VARCHAR (10) NOT NULL,      -- e.g., 27535533\n  SOURCEDB VARCHAR (10) NOT NULL       -- e.g., SWISSPROT_STRING\n)"
## [2] "CREATE TABLE METADATA (\n  NAME NOT NULL,\n  VALUE TEXT\n)"                                                                                                                                                                                                     
## [3] "CREATE INDEX A ON DATA (GENEID_L)"                                                                                                                                                                                                                              
## [4] "CREATE INDEX B ON DATA (GENEID_R)"                                                                                                                                                                                                                              
## [5] "CREATE INDEX C ON DATA (SOURCEDB)"                                                                                                                                                                                                                              
## [6] "CREATE INDEX D ON DATA (SOURCEID)"
dbconn(LRBase.Hsa.eg.db)
## <SQLiteConnection>
##   Path: /github/home/.cache/R/AnnotationHub/85216d7531a2_128300
##   Extensions: TRUE

Combined with dbGetQuery function of 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")
## [1] GENEID_L GENEID_R SOURCEID SOURCEDB
## <0 rows> (or 0-length row.names)

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

Combined with LRBase object and user’s gene expression matrix of scRNA-Seq, scTensor detects CCIs and generates HTML reports for exploratory data inspection. The algorithm of scTensor is as follows.

Firstly, scTensor calculates the celltype-level mean vectors, searches the corresponding pair of genes in the row names of the matrix, and extracted as two 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). 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, scTensor performs non-negative Tucker2 decomposition (NTD2), which is non-negative version of tensor decomposition (cf. nnTensor).

Finally, the result of NTD2 is summarized as an HTML report. Because most of the plots are visualized by 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 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 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)
Germline, Male, GSE86146

Germline, Male, GSE86146

Parameter setting: cellCellSetting

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

    1. LRBaseDb object (e.g. LRBase.Hsa.eg.db)
    1. cell type vector of each cell (e.g. names(labelGermMale))

to SingleCellExperiment object.

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

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

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))
## Input data matrix may contains 7 gene symbols because the name contains some alphabets.
## scTensor uses only NCBI Gene IDs for now.
## Here, the gene symbols are removed and remaining 235 NCBI Gene IDs are used for scTensor next step.
## 7 * 7 * 4 Tensor is created

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))
## Each rank, multiple NMF runs are performed
##   |                                                                              |                                                                      |   0%  |                                                                              |==========                                                            |  14%  |                                                                              |====================                                                  |  29%  |                                                                              |==============================                                        |  43%  |                                                                              |========================================                              |  57%  |                                                                              |==================================================                    |  71%  |                                                                              |============================================================          |  86%  |                                                                              |======================================================================| 100%
## Each rank estimation method
##   |                                                                              |                                                                      |   0%  |                                                                              |==========                                                            |  14%  |                                                                              |====================                                                  |  29%  |                                                                              |==============================                                        |  43%  |                                                                              |========================================                              |  57%  |                                                                              |==================================================                    |  71%  |                                                                              |============================================================          |  86%  |                                                                              |======================================================================| 100%
## Each rank, multiple NMF runs are performed
##   |                                                                              |                                                                      |   0%  |                                                                              |==========                                                            |  14%  |                                                                              |====================                                                  |  29%  |                                                                              |==============================                                        |  43%  |                                                                              |========================================                              |  57%  |                                                                              |==================================================                    |  71%  |                                                                              |============================================================          |  86%  |                                                                              |======================================================================| 100%
## Each rank estimation method
##   |                                                                              |                                                                      |   0%  |                                                                              |==========                                                            |  14%  |                                                                              |====================                                                  |  29%  |                                                                              |==============================                                        |  43%  |                                                                              |========================================                              |  57%  |                                                                              |==================================================                    |  71%  |                                                                              |============================================================          |  86%  |                                                                              |======================================================================| 100%
## $RSS
## $RSS$rss1
## [1] 124587604751  25929822763   2085959976    438461900    312129322
## [6]    277726907    183431820
## 
## $RSS$rss2
## [1] 122493907374   6776993503   2568919182   3253818525   1438749444
## [6]    414514697   1111518908
## 
## 
## $selected
## [1] 3 2
rks$selected
## [1] 3 2

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.

Figure2 : cellCellReport function of scTensor
Figure2 : cellCellReport function of scTensor

Session Information

## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] SingleCellExperiment_1.32.0 SummarizedExperiment_1.40.0
##  [3] Biobase_2.70.0              GenomicRanges_1.62.0       
##  [5] Seqinfo_1.0.0               IRanges_2.44.0             
##  [7] S4Vectors_0.48.0            MatrixGenerics_1.22.0      
##  [9] matrixStats_1.5.0           scTensor_2.20.0            
## [11] RSQLite_2.4.4               LRBaseDbi_2.20.0           
## [13] AnnotationHub_4.0.0         BiocFileCache_3.0.0        
## [15] dbplyr_2.5.1                BiocGenerics_0.56.0        
## [17] generics_0.1.4              BiocStyle_2.38.0           
## 
## loaded via a namespace (and not attached):
##   [1] rTensor_1.4.9           splines_4.5.2           bitops_1.0-9           
##   [4] ggplotify_0.1.3         filelock_1.0.3          fields_17.1            
##   [7] tibble_3.3.0            R.oo_1.27.1             polyclip_1.10-7        
##  [10] graph_1.88.0            XML_3.99-0.20           lifecycle_1.0.4        
##  [13] httr2_1.2.1             tcltk_4.5.2             lattice_0.22-7         
##  [16] MASS_7.3-65             backports_1.5.0         dendextend_1.19.1      
##  [19] magrittr_2.0.4          plotly_4.11.0           sass_0.4.10            
##  [22] rmarkdown_2.30          plot3D_1.4.2            jquerylib_0.1.4        
##  [25] yaml_2.3.10             plotrix_3.8-4           ggtangle_0.0.8         
##  [28] spam_2.11-1             cowplot_1.2.0           DBI_1.2.3              
##  [31] buildtools_1.0.0        RColorBrewer_1.1-3      maps_3.4.3             
##  [34] abind_1.4-8             purrr_1.2.0             R.utils_2.13.0         
##  [37] ggraph_2.2.2            RCurl_1.98-1.17         yulab.utils_0.2.1      
##  [40] tweenr_2.0.3            rappdirs_0.3.3          misc3d_0.9-1           
##  [43] gdtools_0.4.4           seriation_1.5.8         enrichplot_1.30.2      
##  [46] ggrepel_0.9.6           AnnotationForge_1.52.0  tidytree_0.4.6         
##  [49] maketools_1.3.2         reactome.db_1.94.0      genefilter_1.92.0      
##  [52] schex_1.24.0            ccTensor_1.0.3          annotate_1.88.0        
##  [55] codetools_0.2-20        DelayedArray_0.36.0     ggforce_0.5.0          
##  [58] DOSE_4.4.0              tidyselect_1.2.1        GOstats_2.76.0         
##  [61] outliers_0.15           aplot_0.2.9             farver_2.1.2           
##  [64] viridis_0.6.5           TSP_1.2-5               webshot_0.5.5          
##  [67] jsonlite_2.0.0          tidygraph_1.3.1         survival_3.8-3         
##  [70] iterators_1.0.14        systemfonts_1.3.1       foreach_1.5.2          
##  [73] tools_4.5.2             treeio_1.34.0           tagcloud_0.7.0         
##  [76] Rcpp_1.1.0              glue_1.8.0              gridExtra_2.3          
##  [79] SparseArray_1.10.1      xfun_0.54               qvalue_2.42.0          
##  [82] dplyr_1.1.4             ca_0.71.1               withr_3.0.2            
##  [85] BiocManager_1.30.26     Category_2.76.0         fastmap_1.2.0          
##  [88] entropy_1.3.2           digest_0.6.37           R6_2.6.1               
##  [91] gridGraphics_0.5-1      GO.db_3.22.0            R.methodsS3_1.8.2      
##  [94] hexbin_1.28.5           tidyr_1.3.1             fontLiberation_0.1.0   
##  [97] data.table_1.17.8       meshr_2.16.0            graphlayouts_1.2.2     
## [100] httr_1.4.7              htmlwidgets_1.6.4       S4Arrays_1.10.0        
## [103] graphite_1.56.0         pkgconfig_2.0.3         gtable_0.3.6           
## [106] blob_1.2.4              registry_0.5-1          S7_0.2.0               
## [109] XVector_0.50.0          sys_3.4.3               htmltools_0.5.8.1      
## [112] fontBitstreamVera_0.1.1 dotCall64_1.2           fgsea_1.36.0           
## [115] RBGL_1.86.0             GSEABase_1.72.0         scales_1.4.0           
## [118] png_0.1-8               ggfun_0.2.0             knitr_1.50             
## [121] reshape2_1.4.4          visNetwork_2.1.4        checkmate_2.3.3        
## [124] nlme_3.1-168            curl_7.0.0              cachem_1.1.0           
## [127] stringr_1.6.0           BiocVersion_3.23.1      concaveman_1.2.0       
## [130] parallel_4.5.2          AnnotationDbi_1.72.0    ReactomePA_1.54.0      
## [133] pillar_1.11.1           grid_4.5.2              vctrs_0.6.5            
## [136] cluster_2.1.8.1         xtable_1.8-4            Rgraphviz_2.54.0       
## [139] evaluate_1.0.5          cli_3.6.5               compiler_4.5.2         
## [142] rlang_1.1.6             crayon_1.5.3            MeSHDbi_1.46.0         
## [145] heatmaply_1.6.0         fdrtool_1.2.18          plyr_1.8.9             
## [148] fs_1.6.6                ggiraph_0.9.2           stringi_1.8.7          
## [151] viridisLite_0.4.2       BiocParallel_1.44.0     assertthat_0.2.1       
## [154] Biostrings_2.78.0       lazyeval_0.2.2          fontquiver_0.2.1       
## [157] GOSemSim_2.36.0         Matrix_1.7-4            patchwork_1.3.2        
## [160] nnTensor_1.3.0          bit64_4.6.0-1           ggplot2_4.0.0          
## [163] KEGGREST_1.50.0         igraph_2.2.1            memoise_2.0.1          
## [166] bslib_0.9.0             ggtree_4.0.1            fastmatch_1.1-6        
## [169] bit_4.6.0               gson_0.1.0              ape_5.8-1

  1. Jordan A. Ramilowski, A draft network of ligand-receptor-mediated multicellular signaling in human, Nature Communications, 2015↩︎

  2. https://github.com/rikenbit/lrbase-workflow#summary↩︎

  3. https://github.com/rikenbit/lrbase-workflow↩︎