Schiebinger et al. (2019) — iPSC reprogramming with stimulus schedule¶

Dataset: Schiebinger G, Shu J, Tabaka M, Cleary B, Subramanian V, Solomon A, et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 2019; 176:928–43.
Organism / tissue: Mouse embryonic fibroblasts (MEF) reprogrammed toward iPSC.
Time points: 18 time points over 18 days (days 0–18) with varying cell densities.
Genes analysed: 108 genes selected by select_DEgenes_and_split, using a list of 60 reference genes identified in the original Schiebinger et al. (2019) article as a seed for differential expression selection.

Biological context¶

iPSC reprogramming is induced by the transient expression of four transcription factors (Oct4, Sox2, Klf4, c-Myc — the Yamanaka factors). The reprogramming stimulus is therefore not constant over time: it is active during an early induction window and then fades or is withdrawn, creating a non-trivial temporal structure that most GRN inference tools ignore.

The non-trivial stimulus schedule¶

CardamomOT explicitly models the stimulus as a time-varying external signal. The --stimulus parameter controls how strongly the stimulus edges are penalised in the network inference objective.

The time schedule of reprogramming factor expression must be provided alongside the data. CardamomOT reads this schedule from the time field in adata.obs and accounts for it when computing the optimal-transport coupling between consecutive time points.

CardamomOT configuration¶

cardamomot pipeline \
    -i experimental_datasets/Schiebinger \
    -s train \
    -r 0.3 \
    -c 0 \
    --mean-forcing 0.5 \
    --stimulus 1.0 \
    --prior 1.0 \
    --force-basins 0.0 \
    --temporal-basins 0 \
    --test

Key differences from the other datasets:

-s train splits cells into train/test sets, enabling held-out evaluation.
-r 0.3 uses a lower rate parameter suited to the longer time range (18 days).
--force-basins 0.0 --temporal-basins 0 disables NB mode forcing, appropriate for the continuous reprogramming dynamics.
--test activates the test-set inference steps (infer_test + check_test_to_train).

Pre-computed outputs for both --prior 0.5 and --prior 1.0 are stored in experimental_datasets/Schiebinger/cardamomOT/.

Post-analysis notebooks¶

Pre-computed outputs are visualised with the notebooks in utils/:

Notebook	Content
`plot_networks.ipynb`	Inferred GRN (stimulus-driven vs autonomous edges)
`plot_data_to_sim.ipynb`	Data vs simulation comparison across 18 time points
`plot_data_to_sim_KOV.ipynb`	`KO_none_OV_Obox6-Zfp42` and other perturbations
`compare_cell_types.ipynb`	MEF/iPSC proportions in data vs simulation

In-silico KO experiments¶

Silencing reprogramming factors in-silico tests whether the system can reach the iPSC attractor autonomously. Pre-computed outputs (e.g. KO_none_OV_Obox6-Zfp42, KO_none_OV_Zfp42) are stored in experimental_datasets/Schiebinger/cardamomOT/ and compared to the wild-type simulation using utils/plot_data_to_sim_KOV.ipynb.

Key methodological point¶

The non-trivial stimulus schedule in this dataset illustrates a unique feature of CardamomOT: the optimal-transport coupling adapts to time-varying external signals, so the inferred regulatory network distinguishes between stimulus-driven and autonomous regulatory interactions. This is not possible with snapshot-based or time-aggregated GRN inference methods.