README.md

In JuanXie19/TCRlineageClust:

TCRlineageClust

The TCRlineageClust package is an R package for clonotype lineage inference and clustering analysis.

Cell differentiation is a long-standing focus in developmental biology. It is widely accepted that there are no clear distinctions between cellular states, but instead a smooth trasition. Thus cells can be viewed as points along a continuum/lineage. Inference of lineage structure help understanding how cells change states. Many existing tools perform lienage inference based on single-cell transcriptomics data for lineage inference, owing to the fact that cells in different states express different sets of genes, and thus transriptional changes may provide clues for the dynamic biological process that cells go through. The general idea is to learn the different states that cells might go through according to the changes of gene expression, and then place cells onto the infered path. However, although single-cell transcriptomics data provide detailed phenotypic information, the predicted lineage trajectories do not necessarilly reflect genetic relationships[1], as cells sharing same differentiation state may come from different founder cells.

scTCR-seq data compliments the scRNA-seq data in the sense that it provides the clonotype information for each cell, and we can be sure that cells share the same clonotype share the same progenitor. By integrating scTCR-seq data with scRNA-seq data, we could have a detailed insight of transcriptional changes that occur as cells differentiate.

The TCRlineageClust package allows for inference of lineages for cells of the same clonotype. It takes

Installation

The TCRlineageClust package can be installed directly from this repository using devtools.

devtools::install_github('JuanXie19/TCRlineageClust')

Vignettes

Requirements

The TCRlineageClust package has multiple dependencies, including slingshot, tidyverse,Seurat and TrajectoryUtils

Usage

Below are R commands for construction and clustering of clonotype lineages for a pubic mouse data published by Khatun et al

# load package
library(TCRlineageClust)
library(slingshot)
library(tidyverse)
library(Seurat)
library(TrajectoryUtils)

# load data
TCR <-read.csv('/path/to/scTCR-seq/TCR.csv')   # scTCR-seq data
load('Mice.sub.rda')  # seurat object, with clustering labels and UMAP

# combine TCR and scRNA-seq data
Combined <-getCombinedDataSet(TCR,Mice.sub)

# plot distributiopn of clonotype
plot(Combined,1)   # plot the distribution of the first clonotype on UMAP

# infer trajectory
Trajectory <- getClonotypeLineages(Combined,start.clus = NULL, end.clus = NULL, dist.method = 'simple', use.median = TRUE)

# take a look at the infered lineage
Trajectory@clonotype
Trajectory@trajectoryParams   

# overlay the lineages
plot(Trajectory)  

# prepare input for clustering
ClusterInput <- getClusteringInput(Trajectory)

#  lineage clustering analysis
RST <- clustLineages(ClusterInput,k=7L)

We also developed a shiny app for clustering analysis of the clonotype lineages, which can be accessed at clustlineages. The shiny app takes a csv file as input, and users can investigate how will the tuning parameters affect the clustering results. Users can run the following code to prepare the input for the shiny app:

INPUT <- shinyInput(ClusterInput)
write.csv(INPUT, file='/path/to/save/file/')

Reference

Kester, L., & van Oudenaarden, A. (2018). Single-cell transcriptomics meets lineage tracing. Cell stem cell, 23(2), 166-179.

JuanXie19/TCRlineageClust documentation built on Feb. 26, 2022, 12:33 a.m.