Crunch 2 – Nov 18 to Mar 21 – Predicting Unseen Genes

Overview

In Crunch 2, your task is to predict the expression levels of genes that were not measured in a spatial transcriptomics dataset. You will use both spatial data and single-cell RNA sequencing (scRNA-Seq) data from similar colon tissue samples to make these predictions.

X (Inputs Data)

• Spatial Data: The .zarr data provided in Crunch 1.

• scRNA-Seq Data: The Crunch2_scRNAseq.h5ad file contains gene expression data for 18,615 protein-coding genes, including the 460 genes in the Spatial Data object.

Y (Targets)

• Gene Expression Predictions: The expression levels of 2,000 genes.

Example of 2,000 genes expressions with random values.

Evaluation Phases

In Crunch 2, you will have the opportunity to evaluate your model’s predictive performance on a validation dataset, before submission of your test dataset predictions.

There will be checkpoints every:

• Friday — to get your scores before the weekend

• Monday — to see how your weekend work stacks up

The final submission must be submitted by March 21th (Eastern Time 17:59).

The single-cell transcriptomic datasets

Colon tissue samples similar to those profiled by Xenium spatial transcriptomics.

These datasets contain single-cell gene expression data for 18,615 protein-coding genes, including the 460 genes in the Spatial Data.

Datasets Included

We provide datasets (atlases) from multiple studies to represent all the cell types that are found in the colon tissue.

• UC Dataset: An atlas of ulcerative colitis patients, including inflamed, non-inflamed, and healthy colon tissue.
• ENS Dataset: An atlas of the enteric nervous system, including glial cells and neurons innervating the colon.
• Muscle Dataset: An atlas of the colon muscle layer.

Data Format

Provided as an AnnData object stored in an h5ad file: Crunch2_scRNAseq.h5ad.

Cell Metadata scRNA-Seq.obs

• Cell Type: scRNA-Seq.obs["annotation"]

• Study: scRNA-Seq.obs["study"]

• Individual: scRNA-Seq.obs["individual"]

• Disease Status: scRNA-Seq.obs["status"]

Expression DatascRNA-Seq.X — log1p-normalized counts

Original raw counts per cell are divided by the sum of counts per cell, multiplied by 10,000, and then log1p-transformed.

This representation displays the scRNAseq.X matrix in DataFrame format to clarify the structure of the CSR matrix.

The columns in the DataFrame are as follows:

• Row: the row index corresponding to the observation index, accessible via scRNAseq.obs

• Column: the column index corresponding to the gene index, accessible via scRNAseq.var

• Value: normalized, log-transformed gene expression counts

Raw Gene Counts — Available in scRNA-Seq.layers["counts"]

Expected Output

The output must be provided as a DataFrame with the following structure:

Index

• Contains the cell_id values corresponding to the validation and test groups expected in the SpatialData (.zarr file provided by the infer function).

Columns

• Contains 2,000 genes randomly selected from the 18,615 protein-coding genes in the scRNA-Seq data, including the 20 genes already measured by Xenium spatial transcriptomics but excluded from the Spatial Data object.

• You can retrieve this list from the Crunch2_gene_list.csv file included in the competition dataset.

Values

• Gene expression predictions for each cell and gene.

• Predictions must be log1p-normalized and rounded to 2 decimal points.

Refer to the random-submission.ipynb notebook for an example of how to format your submission.

Evaluation

Your predictions are evaluated on the 20 held-out genes using Spearman’s rank correlation for cells with non-zero expression. For cells with zero expression, a separate metric applies. Scores combine predictions across global and local regions for a balanced final score.