sclsd-manuscript

Notebooks for reproducing analyses and figures from the LSD manuscript.

For the sclsd package, see csglab/sclsd.

Datasets

Folder	Dataset	Reference
BoneMarrow	Human hematopoiesis	Setty et al., Nat Biotechnol 2019
Cancer	Lung adenocarcinoma progression	Yang et al., Cell 2022
Dentategyrus	Dentate gyrus neurogenesis	Hochgerner et al., Nat Neurosci 2018
Erythroid	Mouse erythroid gastrulation	Pijuan-Sala et al., Nature 2019
Mouse_cortex	Mouse cortical development	Zheng et al., Cell 2024
Pancreas	Pancreatic endocrinogenesis	Klein et al., Nature 2025
Zebrafish	Zebrafish axial mesoderm development	Farrell et al., Science 2018
Vignette	Human hematopoiesis	Setty et al., Nat Biotechnol 2019
Unseen_Pancreas	Human hematopoiesis	Setty et al., Nat Biotechnol 2019

Setup

1. Create and activate a virtual environment:

   conda create -n sclsd python=3.10 -y
   conda activate sclsd

2. Install sclsd:

   pip install torch==2.4.1
   pip install sclsd

3. Install dependencies to run notebooks:

   pip install ipykernel ipywidgets

4. Install gseapy for gene set enrichment analysis:

   pip install gseapy

5. Download preprocessed data from Zenodo: [https://zenodo.org/records/18331587]

Vignette: end-to-end example from raw data

The Vignette/ directory provides an end-to-end example of training an LSD model starting from a raw single-cell dataset, including preprocessing, model fitting and post training analysis. This vignette is intended to illustrate the full workflow underlying the preprocessed AnnData objects distributed here and complements the dataset-focused notebooks in the reproducibility repository.

Citation

Poursina A, Hajhashemi S, Mikaeili Namini A, Saberi A, Emad A, Najafabadi HS. A Latent Space Thermodynamic Model of Cell Differentiation. 2026.