| --- |
| license: cc-by-4.0 |
| tags: |
| - biology |
| - single-cell |
| - causal-discovery |
| - perturb-seq |
| configs: |
| - config_name: k562 |
| data_files: |
| - split: full |
| path: data/prepared/causalbench_k562.tsv.gz |
| sep: "\t" |
| - config_name: rpe1 |
| data_files: |
| - split: full |
| path: data/prepared/causalbench_rpe1.tsv.gz |
| sep: "\t" |
| --- |
| |
| # CausalBench: K562 and RPE1 |
|
|
| Filtered single-cell CRISPRi perturbation data from |
| [Replogle et al. (2022)](https://doi.org/10.25452/figshare.plus.20029387.v1), |
| prepared following [CausalBench](https://www.nature.com/articles/s42003-025-07764-y). |
| This repo packages the original inputs, preparation code, and filtered outputs |
| so they are easier to download and use together. |
|
|
| | Dataset | Cells | Genes | File | |
| | --- | ---: | ---: | --- | |
| | K562 | 162,751 | 622 | `data/prepared/causalbench_k562.tsv.gz` | |
| | RPE1 | 162,733 | 383 | `data/prepared/causalbench_rpe1.tsv.gz` | |
|
|
| Rows are cells. Gene columns use Ensembl IDs and contain normalized, |
| log-transformed expression. Each table also has an `intervention` column: |
|
|
| - A gene ID identifies the targeted gene. |
| - `non-targeting` identifies control cells. |
| - `excluded` identifies unsupported targets. These rows remain in the data |
| and **must not be treated as controls**. |
|
|
| ## Sources and preprocessing |
|
|
| Inputs: [K562 counts](https://ndownloader.figshare.com/files/35773219), |
| [RPE1 counts](https://ndownloader.figshare.com/files/35775606), and the |
| [perturbation summary workbook](https://ars.els-cdn.com/content/image/1-s2.0-S0092867422005979-mmc2.xlsx) |
| (sheets `TabB_K562_day6_summary_stat` and `TabC_RPE1_summary_statistic`). |
|
|
| 1. Keep controls and strong perturbations: >50 differentially expressed genes, |
| knockdown ≤−0.3, and >25 filtered cells in the summary. |
| 2. For measured targets, retain perturbed cells with target expression at or |
| below the control 10th percentile. |
| 3. Remove zero-total cells. Normalize each cell’s total to the retained-cell |
| median using **all original genes**, then apply natural `log1p`. |
| 4. Keep measured genes targeted in >100 retained cells. Preserve gene order |
| and relabel unsupported targets `excluded` without removing their rows. |
|
|
| K562 was prepared with the unmodified |
| [CausalBench preprocessing function at commit 1a2143c](https://github.com/causalbench/causalbench/blob/1a2143cffdc85f835b41ce8d52034be1bf903e71/causalscbench/data_access/utils/preprocessing.py). |
| RPE1 used a memory-saving implementation of the same rules. Reconstructing both |
| datasets from the raw inputs reproduced all saved expression values and ordered |
| labels exactly. |
|
|
| ## Files and reproduction |
|
|
| ```text |
| data/raw/ Original K562/RPE1 count files and summary workbook |
| prepare.py Download inputs and run preprocessing |
| upstream/preprocessing.py Unchanged CausalBench preprocessing function |
| data/prepared/ Filtered NPZ arrays and gzip TSV tables |
| pyproject.toml + uv.lock Python environment used for reproduction |
| ``` |
|
|
| To download the repo, install `huggingface_hub`, then run: |
|
|
| ```bash |
| hf download huggingbahl21/causalbench --repo-type dataset --local-dir causalbench |
| cd causalbench |
| uv sync --locked |
| uv run prepare.py all --output-dir data/rebuilt |
| ``` |
|
|
| The full download includes about 19 GB of raw count data. Rebuilding uses the |
| original CausalBench function, which loads the full matrix and needs substantial |
| RAM. The NPZ outputs contain `expression_matrix`, `var_names`, and `interventions`; |
| the TSVs contain the same data in table form. Only the prepared files are needed |
| for analysis; the example below downloads one of them. |
|
|
| ## Load a table |
|
|
| ```python |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download( |
| repo_id="huggingbahl21/causalbench", |
| repo_type="dataset", |
| filename="data/prepared/causalbench_rpe1.tsv.gz", # or causalbench_k562.tsv.gz |
| ) |
| expression = pd.read_csv(path, sep="\t") |
| interventions = expression.pop("intervention") |
| ``` |
|
|
| ## Notes for downstream use |
|
|
| These are filtered expression values, not raw counts. The target-expression |
| filter selects cells with lower target expression, so knockdown strength in |
| these tables is partly determined by that selection. The tables mix control |
| and perturbed cells; separating the label column does not make them observational. |
| No complete ground-truth causal graph or train/test split is provided. |
| Perturbation effects can include indirect effects and do not establish direct edges. |
|
|
| Data: **CC BY 4.0**, credited to Replogle et al. The copied preprocessing code |
| retains its original attribution and [Apache 2.0 license](upstream/LICENSE.txt). |
|
|
