---
title: "How to reanalyze the results of scTensor"
author:
- name: Koki Tsuyuzaki
  affiliation: Laboratory for Bioinformatics Research,
    RIKEN Center for Biosystems Dynamics Research
- name: Manabu Ishii
  affiliation: Laboratory for Bioinformatics Research,
    RIKEN Center for Biosystems Dynamics Research
- name: Itoshi Nikaido
  affiliation: Laboratory for Bioinformatics Research,
    RIKEN Center for Biosystems Dynamics Research
  email: k.t.the-answer@hotmail.co.jp
package: scTensor
output:
  BiocStyle::html_document
vignette: |
  %\VignetteIndexEntry{scTensor: 4. Reanalysis of the results of scTensor}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Summary of the output objects of scTensor

Here, we introduced the objects saved in reanalysis.RData.

```{r reanalysis.RData, eval=FALSE}
library("scTensor")
load("reanalysis.RData")
```

After performing `cellCellReport`, some R objects are saved in the reanalysis.RData as follows;

- **sce** : SingleCellExperiment object
    - **metadata(sce)$lrbase** : The file pass to the database file of LRBase
    - **metadata(sce)$color** : The color vector specified by `cellCellSetting`
    - **metadata(sce)$label** : The label vector specified by `cellCellSetting`
    - **metadata(sce)$algorithm** : The algorithm for performing `r Biocpkg("scTensor")`
    - **metadata(sce)$sctensor** : The results of `r Biocpkg("scTensor")`
        - **metadata(sce)\$sctensor\$ligand** : The factor matrix (Ligand)
        - **metadata(sce)\$sctensor\$receptor** : The factor matrix (Receptor)
        - **metadata(sce)\$sctensor\$lrpair** : The core tensor
    - **metadata(sce)$datasize** : The data size of CCI tensor
    - **metadata(sce)$ranks** : The number of lower dimension in each direction of CCI tensor
    - **metadata(sce)$recerror** : Reconstruction Error of NTD
    - **metadata(sce)$relchange** : Relative Change of NTD
- **input** : The gene expression matrix <# Genes * # Cells>
- **twoD** : The result of 2D dimensional reduction (e.g. t-SNE)
- **LR** : The Ligand-Receptor corresponding table extracted from LRBase.XXX.eg.db
- **celltypes** : The celltype label and color scheme
- **index** : The core tensor values
- **corevalue** : The core tensor values (normalized)
- **selected** : The selected corevalue position with thr threshold "thr"
- **ClusterL** : The result of analysis in each L vector
- **ClusterR** : The result of analysis in each R vector
- **out.vecLR** : The result of analysis in LR pairs
- **g** : The igraph object to visualize ligand-receptor gene network

# Execution of scTensor with the different options

Using the `reanalysis.RData`, some users may want to perform `r Biocpkg("scTensor")` with different parameters.

For example, some users want to perform `cellCellDecomp` with different ranks, perform `cellCellReport` with omitting some enrichment analysis, provide the results to their collaborators.

To do such tasks, just type like belows.

```{r Reanalysis, eval=FALSE}
library("AnnotationHub")
library("LRBaseDbi")

# Create LRBase object
ah <- AnnotationHub()
dbfile <- query(ah, c("LRBaseDb", "Homo sapiens", "v002"))[[1]]
LRBase.Hsa.eg.db <- LRBaseDbi::LRBaseDb(dbfile)

# Register the file pass of user's LRBase
metadata(sce)$lrbase <- dbfile(LRBase.Hsa.eg.db)

# CCI Tensor Decomposition
cellCellDecomp(sce, ranks=c(6,5), assayNames="normcounts")

# HTML Report
cellCellReport(sce, reducedDimNames="TSNE", assayNames="normcounts",
    title="Cell-cell interaction within Germline_Male, GSE86146",
    author="Koki Tsuyuzaki", html.open=TRUE,
    goenrich=TRUE, meshenrich=FALSE, reactomeenrich=FALSE,
    doenrich=FALSE, ncgenrich=FALSE, dgnenrich=FALSE)
```

# Session information {.unnumbered}

```{r sessionInfo, echo=FALSE}
sessionInfo()
```