Crunch 3 – Dec 9 to Apr 30 – Identifying Gene
Overview
In Crunch 3, your task is to design a gene panel that best distinguishes dysplasia regions from noncancerous mucosa regions in colon tissue affected by Inflammatory Bowel Disease (IBD). Using provided H&E images annotated by pathologists and single-cell RNA sequencing (scRNA-Seq) data, you will rank 18,615 protein-coding genes based on their ability to discriminate between these disease states.
If you participated in Crunch 1 or Crunch 2, you may leverage your previously developed models to make gene expression predictions on the annotated regions and design your gene panel based on these predictions. If not, you can design your gene panel from scratch using biological insights or other approaches.
Additionally, you are required to:
Provide a justification for how you constructed your gene panel.
Peer Review three submissions from other participants based on their justifications.
X (Inputs Data)
H&E Images and Annotations
First H&E Image: Includes only noncancerous mucosa (already provided in Crunch 1 and Crunch 2).
Second H&E Image: Entire colon tissue section including both dysplasia and noncancerous mucosa regions (
UC9_I-crunch3-HE.tif).Associated Files:
Nucleus Segmentation Masks.
Tissue Region Masks with Annotations (
UC9_I-crunch3-HE-dysplasia-ROI.tif):0: Other tissue regions.
1: Noncancerous mucosa.
2: Dysplasia.
Single-cell RNA-Seq Data
Dataset:
Crunch3_scRNAseq.h5ad.Content: Gene expression data for 18,615 protein-coding genes from colon tissue samples with and without dysplasia.
Cell Metadata (
adata.obs):Cell Type:
adata.obs["annotation"].Individual:
adata.obs["individual"].Disease Status:
adata.obs["status"](Normal, Unaffected tissue, Polyp, Adenocarcinoma).Dysplasia Status:
adata.obs["dysplasia"](y,n, orND).
Expression Data
Normalized Counts:
adata.X(log1p-normalized).Raw Counts: Available in
adata.layers["counts"]
Y (Targets)
Gene Ranking
Rank all 18,615 protein-coding genes from 1 (best discriminator) to 18,615 (worst), based on their ability to distinguish between dysplasia and noncancerous mucosa regions.
Expected Output
Your submission should include the following.
Gene Ranking DataFrame
Format: A DataFrame returned by your
inferfunction.Structure:
Index (Rank): Unique integers from 1 (best discriminator) to 18,615 (worst).
Column (Gene Name): Gene symbols matching those provided in the dataset.
Justification Report
File:
REPORT.mdLength: Maximum 1 page.
Content:
Method Description: Explain how your method works. (5-10 sentences)
Rationale: Describe the reasoning behind your gene panel design. (5-10 sentences)
Data and Resources Used: Specify the datasets and any other resources utilized. (5-10 sentences)
References: May be included (not counted toward the page limit).
Hard Requirement: A submission will be rejected outright if the file is missing:
If you are submitting via the CLI, just create a
REPORT.mdat the root of your submission.Only non-empty and non-comment lines are considered. This is an example of what is expected:
The report is attached to the submission.
To modify the REPORT.md, you must resubmit with the modified content.
Peer Review
Mandatory Participation: To qualify for prizes, you must review three submissions from other participants.
Purpose: The peer review process is crucial for selecting the most promising gene panels for Experimental Validation. Your evaluations help identify submissions with strong justifications and innovative approaches, contributing to the advancement of dysplasia research.
Evaluation Criteria:
Assign a score on a 1-3 scale
1 - excellent justification
2 - adequate justification
3 - poor justification
Provide a short explanation (200-400 words) covering:
Rationale of design.
Novelty of design.
Compliance with the required format.
Evaluation
We will assess your submissions based on two key criteria:
Classification Accuracy: We'll use your top 50 genes to train a model that distinguishes between dysplasia and noncancerous mucosa. The better your genes help the model correctly identify these regions, the higher your accuracy score will be. This is the main factor in determining your ranking.
Diversity: We'll also consider the variety of biological functions represented in your gene panel. Including genes from different pathways enhances the panel's usefulness and may provide deeper insights into dysplasia. A more diverse panel is favorable and can help differentiate teams with similar accuracy.
Your final ranking will prioritize classification accuracy, with diversity as a supplementary factor to distinguish between submissions with close accuracy scores.
Experimental Validation
To validate the most promising gene panels, we will select up to 500 genes for experimental evaluation. This selection will occur via two routes:
Route 1: the top performers from Crunch 2 who also participate in Crunch 3 will have up to 50 of their highest-ranked genes included.
Route 2: the top performers from Crunch 3, determined by peer review and expert evaluations, will also have up to 50 of their top genes included.
We will order a Xenium gene panel comprising these selected genes, reserving a small number of additional genes to identify important cell types in the colon. This panel will be used to perform spatial transcriptomics measurements on a new colon tissue section diagnosed with dysplasia, enabling experimental validation of your gene panels.
Last updated
