Description:

cellpath 1.0

CellPath(Inference of multiple trajectories in single cell RNA-Seq data from RNA velocity)
CellPath v0.1.0
Zhang's Lab, Georgia Institute of Technology
Developed by Ziqi Zhang
Description
CellPath is a single cell trajectory inference method that infers cell developmental trajectory using single-cell RNA Sequencing data and RNA-velocity data. The preprint is posted on bioarxiv: https://www.biorxiv.org/content/10.1101/2020.09.30.321125v2
News
Include leiden algorithm for meta-cell clustering, which is more suitable for datasets with intricate trajectories. You can specify the clustering algorithm you use with either flavor = "leiden" or flavor = "k-means" in cellpath.meta_cell_construction() or cellpath.all_in_one(), please check the run_cellpath.ipynb for more details.
Dependencies
Python >= 3.6.0

numpy >= 1.18.2

scipy >= 1.4.1

networkx>=2.5

pandas >= 1.1.5

scikit-learn >= 0.22.1

anndata >= 0.7.6

scvelo >= 0.2.3

seaborn >= 0.10.0

statsmodels >= 0.12.1 (optional, for differentially expressed gene analysis)

rpy2 >= 3.3.0 (optional, for principal curve only)

Installation
Install from pypi
pip install cellpath

Install from github
Clone the repository with
git clone https://github.com/PeterZZQ/CellPaths.git

And run
cd CellPaths/
pip install .

Uninstall using
pip uninstall cellpath

Usage
run_cellpath.ipynb provide a short pipeline of running cellpaths using cycle-tree trajectory dataset in the paper.

Initialize using adata with calculated velocity using scvelo

cellpath_obj = cp.CellPath(adata = adata, preprocess = True)

preprocessing: the velocity has been calculated and stored in adata or not, if False, the velocity will be calculated during initialization with scvelo

Run cellpath all in one

cellpath_obj.all_in_one(num_metacells = num_metacells, n_neighs = 10, pruning = False, num_trajs = num_trajs, insertion = True, prop_insert = 0.50)

num_metacells: number of meta-cells in total
n_neighs: number of neighbors for each meta-cell
pruning: way to construct symmetric k-nn graph, prunning knn edges or including more edges
num_trajs: number of trajectories to output in the end
insertion: insert unassigned cells to trajectories or not
prop_insert: proportion of cells to be incorporated into the trajectories
`Pseudo-time and branching assignment result
cellpath_obj.pseudo_order


Additional visualizations, please check run_cellpath.ipynb for details.

Datasets


You can access the real dataset that we used for the benchmarking through: https://www.dropbox.com/sh/6wcxj6x5szrp29v/AAB1FtWR18n41xoBn9tbGHKBa?dl=0. You can reproduce the result by putting the file into the root directory and run the notebook in ./Examples/.

./Examples/CellPath_hema.ipynb: mouse hematopoiesis dataset.
./Examples/CellPath_dg.ipynb: dentate-gyrus dataset.
./Examples/CellPath_pe.ipynb: pancreatic endocrinogenesis dataset.
./Examples/CellPath_forebrain.ipynb: forebrain dataset.



Contents

CellPath/ contains the python code for the package
example_data/real/ contains four real datasets, used in the paper, dentate-gyrus dataset, pancreatic endocrinogenesis dataset and human forebrain dataset. Files in real_data folder can be downloaded from dropbox
example_data/simulated/ contains simulated cycle-tree dataset

Test in manuscript
Test script for the result in manuscript can be found with the link

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

Share

• Share on Facebook
• Share on Twitter