# Case study: Il1rn knockout bone marrow

This tutorial reproduces the results of [*IntraTalker – Modelling
Intracellular Signalling in Cellular Crosstalk*](https://www.biorxiv.org/content/10.64898/2026.07.17.739174v1) on the murine bone marrow scRNA-seq data of
Villatoro et al. (Il1rn KO vs. WT).

The tutorial walks the full pipeline: ligand–receptor inference, transcription factor
activities, the receptome, communication communities, and *in silico* receptor knockout
simulations that recover **Il1r1** as a driver of the Adipo-CAR → osteolineage transition in
mesenchymal stromal cells.

:::{admonition} This pipeline uses both Python and R
:class: important

IntraTalkerPy covers the transcription factor and perturbation modules. The
**IntraTalker/CrossTalkeR integration** runs in R
([LR2TF](https://github.com/CostaLab/LR2TF) +
[CrossTalkeR](https://github.com/CostaLab/CrossTalkeR)).

The two sides never share objects in memory — they exchange **CSV / RDS files on disk**. Each
crossing is documented in a *Handoff* box listing exactly which files pass, so you can run
either half on its own as long as those files exist.
:::

```{toctree}
:maxdepth: 1
:hidden:

01_setup
```

## Workflow Overview

| # | Step | Language | Produces |
|---|------|----------|----------|
| 1 | [Introduction and setup](01_setup.md) | Python + **R** | environment, input data |
| 2 | [Ligand–receptor inference](02_ligand_receptor.md) | Python | `{KO,WT}_lr_ready.csv` |
| 3 | [Regulon + TF activity](03_tf_activity.md) | Python | `mouse_dorothea_AB.csv`, TF activity scores |
| 4 | [IntraTalker + CrossTalkeR2](04_intratalker_crosstalker.md) | **R** | `result_TF_object.RDS`, `LR_data_final.Rds`, edge table |
| 5 | [Sankey figures](05_sankey_plots.md) | **R** | Plotting |
| 6 | [Communication communities](06_communities.md) | Python | Plotting |
| 7 | [Receptome construction](07_receptome.md) | Python | `MSCs_receptome.csv` |
| 8 | [The MSC trajectory object](08_trajectory.md) | Python | `MSCs_diffmap_{WT,KO}_subset.h5ad` |
| 9 | [Ridge models + KO simulations](09_perturbation.md) | Python | coefficient matrices, delta embeddings |
| 10 | [Differential pseudotime + ranking](10_receptor_ranking.md) | Python | `receptor_scores.csv`, Plotting |

After the setup, the analysis falls into two halves that can be run on their own. Steps 2–6
alternate between Python and R; steps 7–10 are all Python. Each language switch is called out on
the page where it happens. The package versions every step was run with are listed under
[Session info](11_session_info.md).

## Inter- and Intracellular Communication

**Steps 2–6.** Infer the ligand–receptor interactions and the transcription factor activities,
combine both into the intra- and intercellular network with IntraTalker and CrossTalkeR2, and
read that network out as Sankey figures and communication communities.

This half is self-contained: if the cell–cell communication analysis is all you are after, you
can stop after [step 6](06_communities.md).

```{toctree}
:maxdepth: 1
:hidden:

02_ligand_receptor
03_tf_activity
04_intratalker_crosstalker
05_sankey_plots
06_communities
```

## Gene Expression Perturbations

**Steps 7–10.** Build the receptome, learn receptor to target gene models on the MSC
trajectory, knock the receptors out *in silico*, and rank them by how far they move the cells
along pseudotime.

Everything here runs in Python, but it starts from the network of the first half: the receptome
is built from the intracellular networks and the differential ligand–receptor table exported
from [step 4](04_intratalker_crosstalker.md). If you already have `MSCs_receptome.csv` and the
MSC trajectory object from Zenodo, you can jump straight to [step 8](08_trajectory.md).

```{toctree}
:maxdepth: 1
:hidden:

07_receptome
08_trajectory
09_perturbation
10_receptor_ranking
11_session_info
```

## Citation

If you use IntraTalkerPy, please cite:

> Klöker V, Nagai JS, Feng Z, Mavrommatis L, Hermanns L, Moscoso JMJ, Ruiz M, Kuppe C, Costa IG.
> *IntraTalker – Modelling Intracellular Signalling in Cellular Crosstalk.* bioRxiv (2026).
> doi: [10.64898/2026.07.17.739174](https://www.biorxiv.org/content/10.64898/2026.07.17.739174v1)

The data reanalysed in this case study is from
[Villatoro et al. 2023](https://doi.org/10.1038/s41467-022-35700-9).