Topological and Geometrical Tools for Single-Cell Data

tviblindi is a trajectory inference toolkit for single-cell data.

This package is under development and depends on several libraries - issues during installation are expected.

We recommend to pull the Docker container provided with all dependencies and an RStudio server.

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Parts of code from following libraries are used:
RcppAnnoy
Gudhi
Phat
CGAL

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tviblindi puts concepts from graph theory and algebraic topology to use for trajectory inference (TI) in high-dimensional biological data (cytometry, scRNA-seq, CITE-seq).

We provide easy-to-use tools for identifying potential developmental trajectories and grouping them in a classification tree. This includes a graphical user interface that enables the user to

• inspect trajectories in different classes by topological relationships,

• view the trajectories drawn on a 2-dimensional layout of input data,

• track progression of expression levels for markers of interest at different stages of development,

• check the composition of tracked populations in terms of labelled cell populations and

• export enhanced FCS files for viewing results of the TI analysis in FlowJo or other gating software.

Docker container

(Apple Silicon is not supported for the time being: Mac compatibility is limited to Intel.)

With Docker installed, run the following code in a Unix terminal.

port=7777\
data_path="path to data folder to mount"\
rpassword="password for rstudio server (user=rstudio)"\
docker run -it -d -p $port:8787 --name tviblindi_container -v $data_path:/data -e PASSWORD=$rpassword stuchly/tviblindi

Then navigate to localhost:7777 in your web browser. Enter the default credentials when prompted (user: rstudio, password: rpassword).

localhost may also depend on your Docker daemon setting.

Direct installation

Currently, tviblindi depends on the CGAL library version 4.14 (not higher).

See installation instructions here or follow the instructions below on Intel Macs.

brew tap-new CGAL/legacy
brew extract --version=4.14 CGAL CGAL/legacy
brew install CGAL/legacy/CGAL@4.14

To install tviblindi in R, run

devtools::install_github('stuchly/tviblindi')

To be able to use default dimensionality reduction, install vaevictis

reticulate::py_install("git+https://github.com/stuchly/vaevictis.git",pip=TRUE)

Usage

We include sample code below to run the tviblindi pipeline on synthetic data.

library(tviblindi)
data(tviblindi_dyntoydata)
group_id<-tviblindi_dyntoydata[,1]
datainput<-as.matrix(tviblindi_dyntoydata[,-1])
tv1<-tviblindi(data=datainput,labels=group_id)
DimRed(tv1)
DimRed(tv1,method="umap")

Set_origin(tv1,label = "M4",origin_name = "M4_hitting_time")
Set_origin(tv1,label = "M4",origin_name = "M4_hitting_distance")
KNN(tv1)
Cluster(tv1,K=225) #kmeans clustering
Filtration(tv1) #default setting is very conservative, less simplices could be
# created with same resolution (e.g. Filtration(tv1,alpha2=1))

Pseudotime(tv1,weighted = FALSE,origin_name = "M4_hitting_time")
Walks(tv1,N=1000,origin_name = "M4_hitting_time")

Pseudotime(tv1,weighted = TRUE,origin_name = "M4_hitting_distance")
Walks(tv1,N=1000,origin_name = "M4_hitting_distance")
launch_shiny(tv1)

To inspect the connectedness of different populations in your dataset based on Louvain clustering, use the fully automated 'connectome' functionality: produces a PNG file.

Connectome(tv1)

Reference

Preprint describing and validating the method.

See supplemetary_note.pdf in vignette for technical background.

The Shiny app (GUI) was developed as part of David Novak's master thesis: Studying lymphocyte development using mass cytometry.