LinTInd - tutorial
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")Import data
The input for LinTInd consists three required files:
- sequence
- reference
- position of cutsites
and an optional file:
- celltype
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.
## 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## $scarfull
## 333-letter DNAString object
## seq: GAACGCGTAGGATAACATGGCCATCATCAAGGAGTT...GGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTCCT## 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## Cell.BC Cell.type
## 1 AAGCGAGTCTTCTGTA A
## 2 AATCGACTCGTAGTGT A
## 3 ACATGCAGTCCACACG AArray 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 :
- “reads.num”(default): find an array-form stirng supported by most reads in a cell
- “umi.num”: find an array-form stirng supported by most UMIs in a cell
- “consensus”: find the consistent sequences in each cell, and then generate array-form strings from the new reads
For bulk sequencing, in this step, we will generate a “cell barcode” for each read.
After define the indels for each cell, we can use
IndelPlot() to visualise them.
Indel extract and similarity calculate
We can use the function TagProcess() to extract indels
for cells/reads. The parameter Cells is optional.
And if the annotation of each cells are provided, we can also use
TagDist() to calculate the relationship between each group
in three way:
- “Jaccard”(default): calculate the weighted jaccard similarity of indels between each pair of groups
- “P”: right-tailed test, compare the Indels intersection level with the hypothetical result generated from random editing, and the former is expected to be significantly higher than the latter
- “spearman”: Spearman correlation of indels between each pair of groups
The heatmap of this result will be saved as a pdf file.
## Using Cell.type as value column: use value.var to override.## Aggregation function missing: defaulting to length## 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 1.0000000Tree reconstruct
In the laste part, we can use BuildTree() to Generate an
array generant tree.
## Using Cell.num as value column: use value.var to override.Finally, we can use the function PlotTree() to visualise
the tree created before.
## Using tags as id variables## ℹ invalid tbl_tree object. Missing column: parent,node.
## ℹ invalid tbl_tree object. Missing column: parent,node.
## ℹ invalid tbl_tree object. Missing column: parent,node.
## ℹ invalid tbl_tree object. Missing column: parent,node.
Session Info
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] LinTInd_1.9.0 S4Vectors_0.43.2 BiocGenerics_0.51.1
## [4] ggplot2_3.5.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.1.4 farver_2.1.2
## [4] Biostrings_2.73.1 fastmap_1.2.0 lazyeval_0.2.2
## [7] digest_0.6.37 lifecycle_1.0.4 pwalign_1.1.0
## [10] tidytree_0.4.6 magrittr_2.0.3 compiler_4.4.1
## [13] rlang_1.1.4 sass_0.4.9 tools_4.4.1
## [16] igraph_2.0.3 utf8_1.2.4 yaml_2.3.10
## [19] data.table_1.16.0 knitr_1.48 labeling_0.4.3
## [22] htmlwidgets_1.6.4 plyr_1.8.9 RColorBrewer_1.1-3
## [25] aplot_0.2.3 withr_3.0.1 purrr_1.0.2
## [28] sys_3.4.2 grid_4.4.1 fansi_1.0.6
## [31] colorspace_2.1-1 data.tree_1.1.0 scales_1.3.0
## [34] cli_3.6.3 rmarkdown_2.28 crayon_1.5.3
## [37] treeio_1.29.1 generics_0.1.3 rlist_0.4.6.2
## [40] stringdist_0.9.12 ggtree_3.13.1 httr_1.4.7
## [43] reshape2_1.4.4 ape_5.8 cachem_1.1.0
## [46] stringr_1.5.1 zlibbioc_1.51.1 ggplotify_0.1.2
## [49] XVector_0.45.0 yulab.utils_0.1.7 vctrs_0.6.5
## [52] jsonlite_1.8.8 gridGraphics_0.5-1 IRanges_2.39.2
## [55] patchwork_1.2.0 maketools_1.3.0 ggnewscale_0.5.0
## [58] tidyr_1.3.1 jquerylib_0.1.4 glue_1.7.0
## [61] cowplot_1.1.3 stringi_1.8.4 gtable_0.3.5
## [64] GenomeInfoDb_1.41.1 UCSC.utils_1.1.0 munsell_0.5.1
## [67] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.8.1
## [70] GenomeInfoDbData_1.2.12 R6_2.5.1 networkD3_0.4
## [73] evaluate_0.24.0 lattice_0.22-6 highr_0.11
## [76] pheatmap_1.0.12 ggfun_0.1.6 bslib_0.8.0
## [79] Rcpp_1.0.13 nlme_3.1-166 xfun_0.47
## [82] fs_1.6.4 buildtools_1.0.0 pkgconfig_2.0.3