sc_bifurc_figs

This is a repo for remaking all the figures in the paper A dynamical systems treatment of transcriptomic trajectories in hematopoiesis. While the instructions are specific to reproducing the results in the paper, many of the scripts are generic and can be easily applied to other datasets.

Minimal Requirements

• Latex
• Python 3

While Anaconda is not strictly required, it'll be easiest to have all the correct packages / versions in an Anaconda environment. Create the environment via:

conda env create -f environment.yml

and activate it with

conda activate bifurc

If not using anaconda, consult environment.yml for package info.

fig 1

python python/grn_sim_runner.py --dir out/no_bifurc --nresp 0 --hill 1 --scale 200
python python/grn_sim_runner.py --dir out/sn1
python python/grn_sim_runner.py --dir out/pf_scale20 --nresp 0 --run_pf
python python/fig1.py

fig S1

output: model_v_data.pdf run the notebook:

ipynb/model_v_data.ipynb 

fig 2, S3, S4 -- toy model saddle node

outputs: saddle_node.pdf (2), resample.pdf (S3), saddle_node_slingshot (S4)

python python/grn_sim_runner.py --dt_save 0.1 --m1_range 2 5 1 --dir out/tc_traj

run the notebook

ipynb/saddle_node.ipynb:

fig S2 -- toy model phase planes##

output: figS2_dyn_syss.pdf

python python/phase_planes.py

fig S5 -- toy pitchfork

run the notebook:

ipynb/pitchfork.ipynb

output: figS5_pitchfork.pdf

fig S6 -- noise induced transition##

fig S7 -- unequilibrated saddle node##

python python/grn_sim_runner.py --tmax 5 --dir out/sn_tmax5
python python/unequilib_fig.py

Fig S8 -- Small Errors##

run the notebook

ipynb/small_errors.ipynb

output: figS8_small_errors.pdf

Fig 3,4,S9

download neutrophil files from Klein

bash download_neutrophil_files.sh

the gene expression matrix loads a lot (~3x) faster as an npz file than an mtx file, so convert it, since we have to read it a few times

python python/convert_gene_expr.py
rm -r neutrophil_data/gene_expr.mtx.gz # optional

run the eigenvalue decomposition for the data and nulls -- this takes a few hours I think -- see parallelize directory

python python/pca_gene_resample.py --neval 1 --nsamp 20 --outdir out/eig

run the script to get gene expression trajectories

python python/gexp_trajs.py

run the notebook

ipynb/neutrophil.ipynb

Fig S10 -- distributional properties of neutrophil trajectory

python python/pca_ncell_sampling.py // ~ 1 hr
python python/pca_bin_width.py // ~ 0.5 hr
python python/neut_cov_distr.py

Fig S11 -- slingshot on neutrophil

Fig 5, S12, S13 -- eigenvector and associated calculations

run the notebook

ipynb/evec_figs.ipynb

outputs:

• Fig 5
• Fig S12 -- projection of gene expression onto bifurcation eigenvectors #==> ipynb/evec_fig3.ipynb
• Fig S13 -- distribution of gene expression in different genes #==> ipynb/eigenvector_fig.ipynb
• Fig S14 -- GMM fraction of cells in cluster #==> ipynb/evec_fig2.ipynb has code, but didn't work