bioconductor-lintind

name

bioconductor-lintind

description

When we combine gene-editing technology and sequencing technology, we need to reconstruct a lineage tree from alleles generated and calculate the similarity between each pair of groups. FindIndel() and IndelForm() function will help you align each read to reference sequence and generate scar form strings respectively. IndelIdents() function will help you to define a scar form for each cell or read. IndelPlot() function will help you to visualize the distribution of deletion and insertion. TagPro

LinTInd

Workflows

Standard Workflow

Reconstruct a lineage tree and visualize indel patterns from CRISPR-edited amplicon sequencing data.

library(LinTInd)

# Define paths to package raw data
data_path <- paste0(system.file("extdata", package = 'LinTInd'), "/CB_UMI")
fafile <- paste0(system.file("extdata", package = 'LinTInd'), "/V3.fasta")
cutsite_path <- paste0(system.file("extdata", package = 'LinTInd'), "/V3.cutSites")
celltype_path <- paste0(system.file("extdata", package = 'LinTInd'), "/celltype.tsv")

# Read input files
data <- read.table(data_path, sep="\t", header=TRUE)
ref <- ReadFasta(fafile)
cutsite <- read.table(cutsite_path, col.names = c("indx", "start", "end"))
celltype <- read.table(celltype_path, header=TRUE, stringsAsFactors=FALSE)

# Align reads and identify indels
scarinfo <- FindIndel(data=data, scarfull=ref, scar=cutsite, indel.coverage="All", type="test", cln=1)

# Generate array-form scar strings
scarinfo <- IndelForm(scarinfo, cln=1)

# Define final scar form for each cell
cellsinfo <- IndelIdents(scarinfo, method.use="umi.num", cln=1)

# Visualize indel distribution
IndelPlot(cellsinfo = cellsinfo)

# Extract indels and calculate group similarity
tag <- TagProcess(cellsinfo$info, Cells=celltype)
tag_dist <- TagDist(tag, method = "Jaccard")

# Reconstruct and plot the lineage tree
treeinfo <- BuildTree(tag)
plotinfo <- PlotTree(treeinfo = treeinfo, data.extract = "TRUE", annotation = "TRUE")
plotinfo$p

Note on inputs/outputs:

• Input: A data frame of sequencing reads (with optional cell barcodes and UMIs), a reference FASTA file, a cut site position table, and an optional cell type annotation table.
• Output: A reconstructed lineage tree object and visualization plots of indels and tree structure.

When to Use

• CRISPR Lineage Tracing: Reconstructing lineage trees from alleles generated by CRISPR-mediated gene editing using BuildTree().
• Indel Identification: Aligning sequencing reads to a reference sequence and identifying insertions/deletions using FindIndel().
• Scar Profiling: Generating standardized array-form scar strings for each read or single cell using IndelForm() and IndelIdents().
• Group Similarity Analysis: Quantifying similarity (e.g., Jaccard, Spearman, or hypergeometric test) between cell groups/clusters using TagDist().

When NOT to Use

• General Single-Cell Analysis: For standard single-cell RNA-seq clustering or cell-type annotation, use Seurat or scran because LinTInd is specialized for lineage tracing via CRISPR-induced scars.
• Phylogenetics from SNPs: For standard phylogenetic tree reconstruction from natural genomic mutations (e.g., SNPs) without CRISPR-induced scars, use ape because LinTInd relies on specific cut-site and indel array structures.

Data Requirements

• Sequence Data: A data frame containing a column of read sequences (Read.Seq), with optional columns for cell barcodes (Cell.BC) and UMIs (UMI).
• Reference Sequence: A FASTA file containing the reference sequence, loaded using ReadFasta().
• Cut Sites: A table containing cut site indices, start positions, and end positions.
• Cell Type Annotation (optional): A tab-separated file mapping cell barcodes (Cell.BC) to cell types (Cell.type).

Key Parameters

• indel.coverage ("All"): Parameter in FindIndel() specifying which indels to cover.
• type ("test"): Parameter in FindIndel() specifying the run type.
• cln (1): Number of cores/threads to use in FindIndel(), IndelForm(), and IndelIdents().
• method.use ("reads.num"): Method to define the scar form per cell in IndelIdents(); options include "reads.num", "umi.num", and "consensus".
• method ("Jaccard"): Similarity calculation method in TagDist(); options include "Jaccard", "P", and "spearman".
• data.extract ("TRUE"): Logical parameter in PlotTree() to extract data.
• annotation ("TRUE"): Logical parameter in PlotTree() to annotate the tree plot.

Best Practices

• Verify that the reference sequence and cut sites match the experimental design before running FindIndel().
• Use IndelForm() immediately after FindIndel() to convert identified indels into standardized array-form strings.
• For single-cell data with UMIs, use method.use="umi.num" in IndelIdents() to define cell scar forms based on UMI consensus rather than raw read counts.
• Provide cell type annotations to TagProcess() to enable group-level similarity calculations with TagDist().

Common Pitfalls

• Missing Sequence Columns: Missing or mismatched column names in the input sequence data frame (e.g., missing Read.Seq). Fix: Ensure the sequence column is present and correctly named before running FindIndel().
• Missing Annotations in TagDist: Running TagDist() without cell type annotations. Fix: Pass a valid cell type data frame to TagProcess() via the Cells parameter before computing distances.
• High Computational Time: High computational time when processing large datasets. Fix: Increase the cln parameter in FindIndel(), IndelForm(), and IndelIdents() to utilize multiple cores.

Alternatives

• Seurat for general single-cell analysis and clustering.
• scater for single-cell quality control and visualization.
• scran for single-cell expression data normalization and variance modeling.
• ape for general phylogenetic tree reconstruction and analysis.

Citations

• Wang L. (2021). LinTInd: Reconstruction of lineage trees from CRISPR-mediated gene editing. R package.

References

• Homepage: https://bioconductor.org/packages/lintind
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/lintind/inst/doc/vignette_main.html