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tahoebio/replogle-nadig-de-rhaister

Replogle-Nadig CRISPR Screen Differential Expression Summary Statistics Differential-expression summary statistics for the Replogle-Nadig genome-scale CRISPR screen, which includes 4 cell lines and ~2,000 gene knockdowns. Used to train Rhaister, the data are per-gene fold changes, significance, and pseudobulk deltas summarized from raw single cells. The four cell lines are hepg2, jurkat, k562, and rpe1 and the perturbation is a CRISPR knockdown of a target gene. The repository… See the full description on the dataset page: https://huggingface.co/datasets/tahoebio/replogle-nadig-de-rhaister.

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Replogle-Nadig CRISPR Screen Differential Expression Summary Statistics

Differential-expression summary statistics for the Replogle-Nadig genome-scale CRISPR screen, which includes 4 cell lines and ~2,000 gene knockdowns. Used to train Rhaister, the data are per-gene fold changes, significance, and pseudobulk deltas summarized from raw single cells. The four cell lines are hepg2, jurkat, k562, and rpe1 and the perturbation is a CRISPR knockdown of a target gene.

The repository contains two tables: pdex and cell_eval.

pdex per-gene differential expression (long format)

python
from datasets import load_dataset
pdex = load_dataset("tahoebio/replogle-nadig-de-rhaister", "pdex", streaming=True, split="train")

One row per (cell line, gem_group, target KD, gene). ~1.5 B rows total.

ColumnDescription
cell_lineCell line (hepg2, jurkat, k562, rpe1).
gem_groupSequencing batch (1–56).
targetCRISPR knockdown target gene.
featureMeasured gene symbol.
fold_changelog2 fold change, log2(target_mean / ref_mean). -inf where target_mean = 0.
percent_change(target_mean − ref_mean) / ref_mean.
p_valueMann–Whitney U p-value, perturbed vs. non-targeting control.
statisticMann–Whitney U statistic.
fdrBenjamini–Hochberg adjusted p-value.
target_mean / ref_meanMean expression in the perturbed / control group.
target_membership / ref_membershipCell counts in the perturbed / control group.
n_cells_stratum / n_reference_cellsStratum and reference cell counts.

cell_eval pseudobulk expression deltas (wide format)

python
cell_eval = load_dataset("tahoebio/replogle-nadig-de-rhaister", "cell_eval", streaming=True, split="train")

One row per (cell line, gemgroup, target gene). Columns `cellline, gem_group, gene, then one column per measured gene (~6,546) holding the pseudobulk delta mean(perturbed) − mean(control)` on the linear (normalized) scale.

Definition & filtering criteria

definition/ holds the dataset definition (dataset.toml) and the holdout split TOMLs (split_0–split_3). It records the cell/treatment columns (cell_line, and gene as the treatment), the 2,000 highly-variable genes used for analysis (hvg_2k_genes.json), and the feature_names.json map from pdex's integer feature indices to gene symbols. The compositional split holds out a (cell line, gene) pair only when its cell line is in the holdout set and its target is in that cell line's held-out list, so each test cell line and target is still seen individually in training.

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