Introduction

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")

Via Bioconductor

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("LinTInd")
library(LinTInd)

Import data

The input for LinTInd consists three required files:

and an optional file:

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=TRUE)
ref<-ReadFasta(fafile)
cutsite<-read.table(cutsite,col.names = c("indx","start","end"))
celltype<-read.table(celltype,header=TRUE,stringsAsFactors=FALSE)

For the sequence file, only the column contain reads’ strings is requeired, the cell barcodes and UMIs are both optional.

head(data,3)
##                                   Read.ID
## 1  @A01045:289:HM7K3DRXX:2:2101:9896:1031
## 2 @A01045:289:HM7K3DRXX:2:2101:13367:1031
## 3  @A01045:289:HM7K3DRXX:2:2101:9959:1047
##                                                                                                                                                                                                                                                     Read.Seq
## 1 GAACGCGTAGGATAACATGGCCATCATCAAGGAGTTCTCATGCGCTTCAAGGTGCACATGGTTTATTGGAGCCGTACATGAACTGAGGTTAAGGACAGGATGTCCCAGGCGTAGGTAATTGGCCCCCTGCCCTTCGCCTGGGTTATAAGCTTCGGGTTTAAACGGGCCCTGGGGGTGGCATCCCTGTGACCCCTCCCCAGTGCCTCTCCTGGCCCTGGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTC
## 2 GAACGCGTAGGATAACATGGCCATCATCAAGGAGTTCTCATGCGCTTCAAGGTGCACATGGTTTATTGGAGCCGTACATGAACTGAGGTTAAGGACAGGATGTCCCAGGCGTAGGTAATTGGCCCCCTGCCCTTCGCCTGGGTTATAAGCTTCGGGTTTAAACGGGCCCTGGGGGTGGCATCCCTGTGACCCCTCCCCAGTGCCTCTCCTGGCCCTGGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTC
## 3 GAACGCGTAGGATAACATGGCCATCATCAAGGAGTTCTCATGCGCTTCAAGGTGCACATGGTTTATTGGAGCCGTACATGAACTGAGGTTAAGGACAGGATGTCCCAGGCGTAGGTAATTGGCCCCCTGCCCTTCGCCTGGGTTATAAGCTTCGGGTTTAAACGGGCCCTGGGGGTGGCATCCCTGTGACCCCTCCCCAGTGCCTCTCCTGGCCCTGGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTC
##            Cell.BC        UMI
## 1 GAAGGGTAGCCTCAGC CTTCTCCCGA
## 2 ACCCTCACAAGACTGG TGTAATTTTT
## 3 GAAGGGTAGCCTCAGC CTTCTCCCGA
ref
## $scarfull
## 333-letter DNAString object
## seq: GAACGCGTAGGATAACATGGCCATCATCAAGGAGTT...GGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTCCT
cutsite
##   indx start end
## 1    0    39 267
## 2    1     1  23
## 3    2    28  50
## 4    3    55  77
## 5    4    82 104
## 6    5   109 131
## 7    6   136 158
## 8    7   163 185
head(celltype,3)
##            Cell.BC Cell.type
## 1 AAGCGAGTCTTCTGTA         A
## 2 AATCGACTCGTAGTGT         A
## 3 ACATGCAGTCCACACG         A

Array identify and indel visualization

In the first step, we shold use FindIndel() to alignment and find indels, and the function IndelForm() will help us to generate an array-form string for each read.

scarinfo<-FindIndel(data=data,scarfull=ref,scar=cutsite,indel.coverage="All",type="test",cln=1)
scarinfo<-IndelForm(scarinfo,cln=1)

Then for single-cell sequencing, we shold define a final-version of array-form string for each cell use IndelIdents(), there are three method are provided :

For bulk sequencing, in this step, we will generate a “cell barcode” for each read.

cellsinfo<-IndelIdents(scarinfo,method.use="umi.num",cln=1)

After define the indels for each cell, we can use IndelPlot() to visualise them.

IndelPlot(cellsinfo = cellsinfo)

Indel extract and similarity calculate

We can use the function TagProcess() to extract indels for cells/reads. The parameter Cells is optional.

tag<-TagProcess(cellsinfo$info,Cells=celltype)

And if the annotation of each cells are provided, we can also use TagDist() to calculate the relationship between each group in three way:

The heatmap of this result will be saved as a pdf file.

tag_dist=TagDist(tag,method = "Jaccard")
## Using Cell.type as value column: use value.var to override.
## Aggregation function missing: defaulting to length
tag_dist
##           A         B         C         D         E
## A 1.0000000 0.4925373 0.2794118 0.2985075 0.2058824
## B 0.4925373 1.0000000 0.5588235 0.6060606 0.4117647
## C 0.2794118 0.5588235 1.0000000 0.9047619 0.7500000
## D 0.2985075 0.6060606 0.9047619 1.0000000 0.6666667
## E 0.2058824 0.4117647 0.7500000 0.6666667 1.0000000

Tree reconstruct

In the laste part, we can use BuildTree() to Generate an array generant tree.

treeinfo<-BuildTree(tag)
## Using Cell.num as value column: use value.var to override.

Finally, we can use the function PlotTree() to visualise the tree created before.

plotinfo<-PlotTree(treeinfo = treeinfo,data.extract = "TRUE",annotation = "TRUE")
## Using tags as id variables
plotinfo$p

Session Info

sessionInfo()
## R Under development (unstable) (2022-10-25 r83175)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              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       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
## [1] LinTInd_1.3.0       S4Vectors_0.37.0    BiocGenerics_0.45.0
## [4] ggplot2_3.3.6      
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.0       dplyr_1.0.10           farver_2.1.1          
##  [4] Biostrings_2.67.0      bitops_1.0-7           fastmap_1.1.0         
##  [7] RCurl_1.98-1.9         lazyeval_0.2.2         digest_0.6.30         
## [10] lifecycle_1.0.3        tidytree_0.4.1         magrittr_2.0.3        
## [13] compiler_4.3.0         rlang_1.0.6            sass_0.4.2            
## [16] tools_4.3.0            igraph_1.3.5           utf8_1.2.2            
## [19] yaml_2.3.6             data.table_1.14.4      knitr_1.40            
## [22] labeling_0.4.2         htmlwidgets_1.5.4      plyr_1.8.7            
## [25] RColorBrewer_1.1-3     aplot_0.1.8            withr_2.5.0           
## [28] purrr_0.3.5            grid_4.3.0             fansi_1.0.3           
## [31] colorspace_2.0-3       data.tree_1.0.0        scales_1.2.1          
## [34] cli_3.4.1              rmarkdown_2.17         crayon_1.5.2          
## [37] treeio_1.23.0          generics_0.1.3         rlist_0.4.6.2         
## [40] stringdist_0.9.9       ggtree_3.7.0           reshape2_1.4.4        
## [43] DBI_1.1.3              ape_5.6-2              cachem_1.0.6          
## [46] stringr_1.4.1          zlibbioc_1.45.0        assertthat_0.2.1      
## [49] ggplotify_0.1.0        XVector_0.39.0         vctrs_0.5.0           
## [52] yulab.utils_0.0.5      jsonlite_1.8.3         gridGraphics_0.5-1    
## [55] IRanges_2.33.0         patchwork_1.1.2        ggnewscale_0.4.8      
## [58] jquerylib_0.1.4        tidyr_1.2.1            glue_1.6.2            
## [61] cowplot_1.1.1          stringi_1.7.8          gtable_0.3.1          
## [64] GenomeInfoDb_1.35.0    munsell_0.5.0          tibble_3.1.8          
## [67] pillar_1.8.1           htmltools_0.5.3        GenomeInfoDbData_1.2.9
## [70] R6_2.5.1               networkD3_0.4          evaluate_0.17         
## [73] lattice_0.20-45        highr_0.9              pheatmap_1.0.12       
## [76] ggfun_0.0.7            bslib_0.4.0            Rcpp_1.0.9            
## [79] nlme_3.1-160           xfun_0.34              pkgconfig_2.0.3