# Crunch 1 – Oct 28 to Feb 9 – Predict gene expression ## Evaluation Phases In Crunch 1, 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 multiple validation checkpoints: * **Checkpoint 1** - November 30th (Eastern Time 17:59) * **Checkpoint 2** - December 16th (Eastern Time 17:59) * **Checkpoint 3** - December 30th (Eastern Time 17:59) * **Checkpoint 4** - January 13th (Eastern Time 17:59) * ~~**Checkpoint 5** - January 27th (Eastern Time 17:59)~~ * **Continuous Public Leaderboard** - January 20th * **Last submission -** February 9th (Eastern Time 17:59) ## Overview In **Crunch 1**, you will train an algorithm to predict **spatial transcriptomics data** (gene expression in each cell) from matched **H\&E images**. In other words predict the gene expression (Y) in cells from specific tissue patches based on the **H\&E images** (X) and surrounding **spatial transcriptomics** data. * **X (Input)**: * **`HE_original`**: The **original H\&E image** in its native pixel coordinates. Alignment from H\&E native coordinate system to Xenium coordinate system has been handled from our end. If you prefer to handle alignment yourself, you can check **HE\_original** and **DAPI** (provided in crunch1\_max), but it may require additional processing. * **`HE_nuc_original`**: The **nucleus segmentation mask of H\&E image**, in H\&E native coordinate system. The cell\_id in this segmentation mask matches with the nuclei by gene matrix stored in **anucleus**.
* **Y**: * **`anucleus`**: This file contains the **aggregated gene expression data** for each nucleus. It is log1p-normalized and stores the gene expression profiles for 460 genes per nucleus. This is the primary target (**Y**) for your model.

Anucleus – gene expression for each nucleus

## Linking the H\&E image to spatial transcriptomics Steps to align X and Y: * **Step 1: Identify nuclei in the H\&E image** * Use the **nucleus segmentation masks**: * **H\&E nucleus segmentation (`HE_nuc_original`)**: This mask identifies the location of nuclei in the **original H\&E image** **(**i.e. **HE\_original)**. * **Step 2: Link gene expression to H\&E images** * For each nucleus in the **H\&E image**, use the **`anucleus`** file to get the corresponding gene expression profile (Y) for that nucleus. * The **`anucleus`** file provides the gene expression data, where each row corresponds to a nucleus (cell) and each column corresponds to a gene. * The nuclei IDs from the segmentation masks (e.g., from **`HE_nuc_original`**) will match the IDs used in the **`anucleus`** file.

Matched crop H&E image and its corresponding Gene Expression Heatmap

{% hint style="info" %} If you open the image HE\_nuc\_original, e.g. through `mask=sdata['HE_nuc_original'][0].to_numpy()`. You can directly find the location of that cell, with cell\_id, through `mask==cell_id`. {% endhint %} The datasets are store in a **SpatialData object.** Learn more about this format [here](https://spatialdata.scverse.org/en/stable/generated/spatialdata.SpatialData.html). {% code fullWidth="true" %} ```javascript // SpatialData object structure Images // 'DAPI': DAPI image (validation and test tissue patches are removed) 'DAPI_nuc': DAPI nucleus segmentation 'HE_nuc_original': H&E nucleus segmentation on original image 'HE_nuc_registered': H&E nucleus segmentation on registered image (registered to DAPI image) 'HE_original': H&E original image 'HE_registered': H&E registered image 'group': Defining train(0)/validation(1)/test(2), No_transcript-train(4) tissue patches 'group_HEspace': Defining train(0)/validation(1)/test(2), No_transcript-train(4) tissue patches on the H&E image Points 'transcripts': DataFrame for each transcript (containing x,y,tissue patch,z_location, feature_name,transcript_id,qv,cell_id columns) Tables 'anucleus': AnnData contains .X, .layers['counts'], .obsm['spatial'] 'cell_id-group': AnnData only contains .obs DataFrame for mapping of cell_id to region. with coordinate systems: 'global', with elements: DAPI (Images), 'DAPI_nuc' (Images), 'HE_nuc_original' (Images), 'HE_nuc_registered' (Images), 'HE_original' (Images), 'HE_registered' (Images), 'group' (Images), 6 'group_HEspace' (Images), 'transcripts' (Points) 'scale_um_to_px', with elements: transcripts (Points) ``` {% endcode %} In the minimum version of the data provided for crunch1 (in crunch1\_min.tar), only **HE\_original**, **HE\_nuc\_original**, **anucleus** and **cell\_id-group** are provided. ## Expected Output The output consists of four columns: * **cell\_id**: contains the held-out nuclei (both validation and test tissue regions). * **gene**: the gene among the 460 genes to be predicted. * **prediction**: the gene expression value, rounded to two decimal places. * **sample**: the tissue sample among the 8 samples to process. {% hint style="success" %} Make sure your predictions are `log1p-normalized` with a scale factor of 100 as in `anucleus.X` {% endhint %}
### Scoring The scoring metric is a cell-wise Spearman correlation. A Mean Squared Error metric is also computed, the value must be below 0.2. Since the baseline is 0.1, a model with an MSE that is too high is not considered viable and will not be eligible for rewarded. {% hint style="info" %} [The evaluation code is available on GitHub.](https://github.com/crunchdao/competitions/blob/master/competitions/broad-1/scoring/scoring.py) {% endhint %} ## Submit To build a valid submission, your model need to be coded within the infer function, effectively respecting the crunch code submission interface. {% hint style="info" %} [See how to submit through the quickstarter.](https://github.com/crunchdao/quickstarters/blob/master/competitions/broad-1/quickstarters/random-submission/random-submission.ipynb) {% endhint %} {% hint style="info" %} [Learn about crunch code interface.](/competitions/participate/code-interface.md) {% endhint %} ## Data Variants Due to the large size of the datasets, Crunch provides both a small (aka. default) and a large version. Depending on your local setup and goals within Crunch, you can choose either one. By default, the small dataset is downloaded. To access the larger dataset, specify it explicitly with a different CLI command: ```bash # setup with the large data crunch setup --size large broad-1 my-model ... # setup with the small data crunch setup broad-1 my-model ... ``` The larger version contain the Xenium transcriptomic data. It allow you to know both the gene expression and the coordinate (x, y, z) of the position of the gene in the Cells. More details about the gene transcriptomic data in the full documentation. {% hint style="warning" %} The large variant is for local use only. The Cloud Environment will always use the default dataset. {% endhint %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.crunchdao.com/competitions/competitions/broad-institute-autoimmune-disease/crunch-1.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.