LinTInd - tutorial
Luyue Wang
2021/12/22
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:
- 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.
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 CTTCTCCCGAref## $scarfull
## 333-letter DNAString object
## seq: GAACGCGTAGGATAACATGGCCATCATCAAGGAGTT...GGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTCCTcutsite## indx start end
## 1 0 39 267
## 2 1 1 23
## 3 2 28 50
## 4 3 55 77
## 5 4 82 104
## 6 5 109 131
## 7 6 136 158
## 8 7 163 185head(celltype,3)## Cell.BC Cell.type
## 1 AAGCGAGTCTTCTGTA A
## 2 AATCGACTCGTAGTGT A
## 3 ACATGCAGTCCACACG AArray identify and indel visualization
In the first step, we shold use FindIndel() to alignment and find indels, and the function IndelForm() will help us to generate an array-form string for each read.
scarinfo<-FindIndel(data=data,scarfull=ref,scar=cutsite,indel.coverage="All",type="test",cln=1)
scarinfo<-IndelForm(scarinfo,cln=1)Then for single-cell sequencing, we shold define a final-version of array-form string for each cell use IndelIdents(), there are three method are provided :
- “reads.num”(default): find an array-form stirng supported by most reads in a cell
- “umi.num”: find an array-form stirng supported by most UMIs in a cell
- “consensus”: find the consistent sequences in each cell, and then generate array-form strings from the new reads
For bulk sequencing, in this step, we will generate a “cell barcode” for each read.
cellsinfo<-IndelIdents(scarinfo,method.use="umi.num",cln=1)After define the indels for each cell, we can use IndelPlot() to visualise them.
IndelPlot(cellsinfo = cellsinfo)
Indel extract and similarity calculate
We can use the function TagProcess() to extract indels for cells/reads. The parameter Cells is optional.
tag<-TagProcess(cellsinfo$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:
- “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.
tag_dist=TagDist(tag,method = "Jaccard")## Using Cell.type as value column: use value.var to override.## Aggregation function missing: defaulting to lengthtag_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 1.0000000Tree 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 variablesplotinfo$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
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## [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