# LinTInd
+ **Single-cell RNA sequencing has become a common approach to trace developmental processes of cells, however, using exogenous barcodes is more direct than predicting from expression profiles recently, based on that, as gene-editing technology matures, combining this technological method with exogenous barcodes can generate more complex dynamic information for single-cell. In this application note, we introduce an R package: LinTInd for reconstructing a tree from alleles generated by the genome-editing tool known as CRISPR for a moderate time period based on the order in which editing occurs, and for sc-RNA seq, ScarLin can also quantify the similarity between each cluster in three ways.**
## Installation via GitHub
```
devtools::install_github("mana-W/LinTInd")
```
* Depends:<br />
+ ggplot2<br />
+ parallel<br />
+ stats<br />
+ S4Vectors<br />
+ data.tree<br />
+ reshape2<br />
+ networkD3<br />
+ stringdist<br />
+ purrr<br />
+ ape<br />
+ cowplot<br />
+ ggnewscale<br />
+ stringr<br />
+ dplyr<br />
+ rlist<br />
+ pheatmap<br />
+ Biostrings<br />
+ IRanges<br />
+ BiocGenerics(>= 0.36.1)<br />
+ ggtree<br />
## Installation via Bioconductor
```
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("LinTInd")
```
## Data prepare
To generate the **CB_UMI** from fastq files, which will be used in the following.<br />
You can use **CB_UMI.sh** in: https://github.com/mana-W/chenlab_you.<br />
## Usage
**Input fileļ¼**<br />
*Example files is in LinTInd/inst/extdata*<br />
data is from CB_UMI<br />
fa is ref file<br />
cutsite is a file define each sgRNA start and end positon<br />
celltype.tsv is a file include cell barcode and its' annotations, header: Cell.BC Cell.type
### Quick start
```
library(LinTInd)
data<-paste0(system.file("extdata",package = 'LinTInd'),"/CB_UMI")
fafile<-paste0(system.file("extdata",package = 'LinTInd'),"/V3.fasta")
cutsite<-paste0(system.file("extdata",package = 'LinTInd'),"/V3.cutSites")
celltype<-paste0(system.file("extdata",package = 'LinTInd'),"/celltype.tsv")
data<-read.table(data,sep="\t",header=T)
ref<-ReadFasta(fafile)
cutsite<-read.table(cutsite,col.names = c("indx","start","end"))
celltype<-read.table(celltype,header=T,stringsAsFactors=F)
```
Or load the example data
```
data("example_data",package = "LinTInd")
```
### Array identify<br />
Alignment
```
scarinfo<-FindIndel(data=data,scarfull=ref,scar=cutsite,indel.coverage="All",type="test",cln=1)
scarinfo<-IndelForm(scarinfo,cln=1)
```
Define scar pattern for each cell<br />
```
cellsinfo<-IndelIdents(scarinfo,method.use="umi.num",cln=1)
```
Pattern visualization <br />
```
IndelPlot(cellsinfo = cellsinfo)
```
<p align="center">
<img src="https://github.com/mana-W/LinTInd/blob/main/vignettes/Indel_pattern.png" width = "620" height = "450" align=center />
</p >
<br />
### Indel extracted
```
tag<-TagProcess(cellsinfo$info,Cells=celltype)
```
### Tree reconstruct
```
treeinfo<-BuildTree(tag)
```
### Visualization
**Similarity of each pair of clusters**
```
tag_dist=TagDist(tag,method = "Jaccard")
```
<p align="center">
<img src="https://github.com/mana-W/LinTInd/blob/main/vignettes/Indel.png" width = "500" height = "300" align=center />
</p >
<p align="center">
<img src="https://github.com/mana-W/LinTInd/blob/main/vignettes/cluster_similarity.png" width = "490" height = "450" align=center />
</p >
***Visualization for tree***
```
plotinfo<-PlotTree(treeinfo = treeinfo,data.extract = "TRUE",annotation = "TRUE")
plotinfo$p
```
<p align="center">
<img src="https://github.com/mana-W/LinTInd/blob/main/vignettes/tree_tag_pattern.png" width = "400" height = "400" align=center />
</p >
Or <br />
```
plotinfo<-PlotTree(treeinfo = treeinfo,data.extract = "TRUE",annotation = "FALSE")
plotinfo$p
```
<p align="center">
<img src="https://github.com/mana-W/LinTInd/blob/main/vignettes/tree_pattern.png" width = "400" height = "400" align=center />
</p >
