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StagewiseNN

DOI

StagewiseNN is a computational tool for constructing developmental tree from multi-staged single-cell RNA-seq data.

It starts from building a single-cell graph by connecting each cell to its k-nearest neighbors in the parent stage, followed by the voting-based tree-construction and an adaptive cluster refinement.

see StagewiseNN Documentation for detailed guides

The single-cell graph can be further visualized using graph embedding methods, e.g. UMAP, SPRING.

We have used it to build the developmental tree from the snRNA-seq of amphioxus embryonic cells, across nine developmental stages ("B", "G3", "G4", "G5", "G6", "N0", "N1", "N3", "L0"), where seven major lineages were recognized.

StagewiseNN can also be applied on scATAC-seq data sampled at multiple timepoints, once the peak-by-cell matrix is transformed into a gene-by-cell matrix (i.e., the gene activities).

It is easy to use:

import swnn

# ====== Inputs ======
# data_matrix = ..
# stage_labels = ..
# group_labels = ..
# stage_order = [f'stage_{i}' for i in range(5)]

builder = swnn.Builder(stage_order=stage_order)

# step1: building a (stage-preserved) single-cell graph
distmat, connect = builder.build_graph(
       X=data_matrix, stage_lbs=stage_labels,
   )
# step2: build a developmental tree from the single-cell graph
edgedf, refined_group_lbs = builder.build_tree(group_labels, stage_labels,)

Here is the detailed tutorial

Installation

Requirements:

Install stagewiseNN by running (in the command line):

pip install swnn

or install from source code:

git clone https://github.com/zhanglabtools/stagewiseNN.git
cd stagewiseNN
python setup.py install

Contribute

Support

If you are having issues, please let us know. We have a mailing list located at:

Citation

If you find StagewiseNN helps, please cite:

Pengcheng Ma, Xingyan Liu, Zaoxu Xu et al., Joint profiling of gene expression and chromatin accessibility of amphioxus development at single-cell resolution, Cell Reports (2022), https://doi.org/10.1016/j.celrep.2022.110979