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rcannood merged 4 commits into from
Aug 24, 2026
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Add authors Benjamin Frey and Vladimir Shitov #65

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Add authors Benjamin Frey and Vladimir Shitov
VladimirShitov Aug 14, 2026
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VladimirShitov Aug 20, 2026
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212 changes: 111 additions & 101 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -35,12 +35,14 @@ data shows that this is not trivial.
| Alejandro Granados | author | agranado | | |
| Alex Tong | author | atong01 | | |
| Bastian Rieck | author | Pseudomanifold | | |
| Benjamin Frey | author | benjaminfreyuu | 0009-0004-7649-8340 | |
| Christopher Lance | author | xlancelottx | 0000-0002-1275-9802 | |
| Daniel Burkhardt | author | dburkhardt | | |
| Kai Waldrant | contributor | KaiWaldrant | 0009-0003-8555-1361 | |
| Kaiwen Deng | contributor | nonztalk | | dengkw@umich.edu |
| Louise Deconinck | author | LouiseDck | | |
| Robrecht Cannoodt | author, maintainer | rcannood | 0000-0003-3641-729X | |
| Robrecht Cannoodt | author | rcannood | 0000-0003-3641-729X | |
| Vladimir Shitov | author, maintainer | VladimirShitov | 0000-0002-1960-8812 | |
| Xueer Chen | contributor | xuerchen | | xc2579@columbia.edu |
| Jiwei Liu | contributor | daxiongshu | 0000-0002-8799-9763 | jiweil@nvidia.com |
| Marius Lange | contributor | marius1311 | 0000-0002-4846-1266 | |
Expand All @@ -50,47 +52,47 @@ data shows that this is not trivial.
``` mermaid
flowchart TB
file_common_dataset_mod1("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-raw-dataset-rna'>Raw dataset RNA</a>")
file_common_dataset_mod2("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-raw-dataset-mod2'>Raw dataset mod2</a>")
comp_process_datasets[/"<a href='https://github.com/openproblems-bio/task_predict_modality#component-type-process-dataset'>Process Dataset</a>"/]
file_train_mod1("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-train-mod1'>Train mod1</a>")
file_train_mod2("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-train-mod2'>Train mod2</a>")
file_test_mod1("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-test-mod1'>Test mod1</a>")
file_test_mod2("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-solution'>Solution</a>")
comp_control_method[/"<a href='https://github.com/openproblems-bio/task_predict_modality#component-type-control-method'>Control method</a>"/]
comp_method[/"<a href='https://github.com/openproblems-bio/task_predict_modality#component-type-method'>Method</a>"/]
comp_method_predict[/"<a href='https://github.com/openproblems-bio/task_predict_modality#component-type-predict'>Predict</a>"/]
comp_method_train[/"<a href='https://github.com/openproblems-bio/task_predict_modality#component-type-train'>Train</a>"/]
comp_metric[/"<a href='https://github.com/openproblems-bio/task_predict_modality#component-type-metric'>Metric</a>"/]
file_prediction("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-prediction'>Prediction</a>")
comp_control_method[/"<a href='https://github.com/openproblems-bio/task_predict_modality#component-type-control-method'>Control method</a>"/]
file_pretrained_model("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-pretrained-model'>Pretrained model</a>")
comp_method_predict[/"<a href='https://github.com/openproblems-bio/task_predict_modality#component-type-predict'>Predict</a>"/]
file_prediction("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-prediction'>Prediction</a>")
comp_metric[/"<a href='https://github.com/openproblems-bio/task_predict_modality#component-type-metric'>Metric</a>"/]
file_score("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-score'>Score</a>")
file_common_dataset_mod2("<a href='https://github.com/openproblems-bio/task_predict_modality#file-format-raw-dataset-mod2'>Raw dataset mod2</a>")
file_common_dataset_mod1---comp_process_datasets
file_common_dataset_mod2---comp_process_datasets
comp_process_datasets-->file_train_mod1
comp_process_datasets-->file_train_mod2
comp_process_datasets-->file_test_mod1
comp_process_datasets-->file_test_mod2
file_train_mod1---comp_control_method
file_train_mod1---comp_method
file_train_mod1-.-comp_method_predict
file_train_mod1---comp_method_train
file_train_mod2---comp_control_method
file_train_mod1---comp_control_method
file_train_mod1-.-comp_method_predict
file_train_mod2---comp_method
file_train_mod2-.-comp_method_predict
file_train_mod2---comp_method_train
file_test_mod1---comp_control_method
file_train_mod2---comp_control_method
file_train_mod2-.-comp_method_predict
file_test_mod1---comp_method
file_test_mod1---comp_method_predict
file_test_mod1-.-comp_method_train
file_test_mod1---comp_control_method
file_test_mod1---comp_method_predict
file_test_mod2---comp_control_method
file_test_mod2---comp_metric
comp_control_method-->file_prediction
comp_method-->file_prediction
comp_method_predict-->file_prediction
comp_method_train-->file_pretrained_model
comp_metric-->file_score
file_prediction---comp_metric
comp_control_method-->file_prediction
file_pretrained_model---comp_method_predict
file_common_dataset_mod2---comp_process_datasets
comp_method_predict-->file_prediction
file_prediction---comp_metric
comp_metric-->file_score
```

## File format: Raw dataset RNA
Expand All @@ -105,7 +107,57 @@ Format:
<div class="small">

AnnData object
obs: 'batch', 'size_factors'
obs: 'batch', 'cell_type', 'is_train', 'size_factors'
var: 'feature_id', 'feature_name', 'hvg', 'hvg_score'
obsm: 'gene_activity'
layers: 'counts', 'normalized'
uns: 'dataset_id', 'dataset_name', 'dataset_url', 'dataset_reference', 'dataset_summary', 'dataset_description', 'dataset_organism', 'normalization_id', 'gene_activity_var_names'

</div>

Data structure:

<div class="small">

| Slot | Type | Description |
|:---|:---|:---|
| `obs["batch"]` | `string` | Batch information. |
| `obs["cell_type"]` | `string` | Cell type annotation. Used to balance the subsample of test cells. |
| `obs["is_train"]` | `string` | (*Optional*) Which split the cell belongs to. Cells labelled ‘train’ become the training set, all other cells (e.g. ‘test’, ‘iid_holdout’) become the test set. Optional: when absent, `process_dataset` holds out a quarter of the batches instead. |
| `obs["size_factors"]` | `double` | (*Optional*) The size factors of the cells prior to normalization. |
| `var["feature_id"]` | `string` | Unique identifier for the feature, usually a ENSEMBL gene id. |
| `var["feature_name"]` | `string` | (*Optional*) A human-readable name for the feature, usually a gene symbol. |
| `var["hvg"]` | `boolean` | Whether or not the feature is considered to be a ‘highly variable gene’. |
| `var["hvg_score"]` | `double` | A score for the feature indicating how highly variable it is. |
| `obsm["gene_activity"]` | `double` | (*Optional*) ATAC gene activity. |
| `layers["counts"]` | `integer` | Raw counts. |
| `layers["normalized"]` | `double` | Normalized expression values. |
| `uns["dataset_id"]` | `string` | A unique identifier for the dataset. |
| `uns["dataset_name"]` | `string` | Nicely formatted name. |
| `uns["dataset_url"]` | `string` | (*Optional*) Link to the original source of the dataset. |
| `uns["dataset_reference"]` | `string` | (*Optional*) Bibtex reference of the paper in which the dataset was published. |
| `uns["dataset_summary"]` | `string` | Short description of the dataset. |
| `uns["dataset_description"]` | `string` | Long description of the dataset. |
| `uns["dataset_organism"]` | `string` | (*Optional*) The organism of the sample in the dataset. |
| `uns["normalization_id"]` | `string` | The unique identifier of the normalization method used. |
| `uns["gene_activity_var_names"]` | `string` | (*Optional*) Names of the gene activity matrix. |

</div>

## File format: Raw dataset mod2

The second modality of the raw dataset. Must be an ADT or an ATAC
dataset

Example file:
`resources_test/common/openproblems_neurips2021/bmmc_cite/dataset_mod2.h5ad`

Format:

<div class="small">

AnnData object
obs: 'batch', 'cell_type', 'is_train', 'size_factors'
var: 'feature_id', 'feature_name', 'hvg', 'hvg_score'
obsm: 'gene_activity'
layers: 'counts', 'normalized'
Expand All @@ -120,6 +172,8 @@ Data structure:
| Slot | Type | Description |
|:---|:---|:---|
| `obs["batch"]` | `string` | Batch information. |
| `obs["cell_type"]` | `string` | Cell type annotation. Used to balance the subsample of test cells. |
| `obs["is_train"]` | `string` | (*Optional*) Which split the cell belongs to. Cells labelled ‘train’ become the training set, all other cells (e.g. ‘test’, ‘iid_holdout’) become the test set. Optional: when absent, `process_dataset` holds out a quarter of the batches instead. |
| `obs["size_factors"]` | `double` | (*Optional*) The size factors of the cells prior to normalization. |
| `var["feature_id"]` | `string` | Unique identifier for the feature, usually a ENSEMBL gene id. |
| `var["feature_name"]` | `string` | (*Optional*) A human-readable name for the feature, usually a gene symbol. |
Expand Down Expand Up @@ -156,6 +210,7 @@ Arguments:
| `--output_train_mod2` | `file` | (*Output*) The mod2 expression values of the train cells. |
| `--output_test_mod1` | `file` | (*Output*) The mod1 expression values of the test cells. |
| `--output_test_mod2` | `file` | (*Output*) The ground-truth mod2 expression values of the test cells. |
| `--seed` | `integer` | (*Optional*) The seed for determining the train/test split. Default: `1`. |

</div>

Expand Down Expand Up @@ -314,7 +369,7 @@ Format:
var: 'gene_ids', 'hvg', 'hvg_score'
obsm: 'gene_activity'
layers: 'counts', 'normalized'
uns: 'dataset_id', 'common_dataset_id', 'modality', 'dataset_name', 'dataset_url', 'dataset_reference', 'dataset_summary', 'dataset_description', 'dataset_organism', 'gene_activity_var_names'
uns: 'dataset_id', 'common_dataset_id', 'modality', 'dataset_name', 'dataset_url', 'dataset_reference', 'dataset_summary', 'dataset_description', 'dataset_organism', 'normalization_id', 'gene_activity_var_names'

</div>

Expand All @@ -341,13 +396,14 @@ Data structure:
| `uns["dataset_summary"]` | `string` | Short description of the dataset. |
| `uns["dataset_description"]` | `string` | Long description of the dataset. |
| `uns["dataset_organism"]` | `string` | (*Optional*) The organism of the sample in the dataset. |
| `uns["normalization_id"]` | `string` | The unique identifier of the normalization method used. |
| `uns["gene_activity_var_names"]` | `string` | (*Optional*) Names of the gene activity matrix. |

</div>

## Component type: Control method
## Component type: Method

Quality control methods for verifying the pipeline.
A regression method.

Arguments:

Expand All @@ -358,14 +414,13 @@ Arguments:
| `--input_train_mod1` | `file` | The mod1 expression values of the train cells. |
| `--input_train_mod2` | `file` | The mod2 expression values of the train cells. |
| `--input_test_mod1` | `file` | The mod1 expression values of the test cells. |
| `--input_test_mod2` | `file` | The ground-truth mod2 expression values of the test cells. |
| `--output` | `file` | (*Output*) A prediction of the mod2 expression values of the test cells. |

</div>

## Component type: Method
## Component type: Train

A regression method.
Train a model to predict the expression of one modality from another.

Arguments:

Expand All @@ -375,59 +430,51 @@ Arguments:
|:---|:---|:---|
| `--input_train_mod1` | `file` | The mod1 expression values of the train cells. |
| `--input_train_mod2` | `file` | The mod2 expression values of the train cells. |
| `--input_test_mod1` | `file` | The mod1 expression values of the test cells. |
| `--output` | `file` | (*Output*) A prediction of the mod2 expression values of the test cells. |
| `--input_test_mod1` | `file` | (*Optional*) The mod1 expression values of the test cells. |
| `--output` | `file` | (*Output*) A pretrained model for predicting the expression of one modality from another. |

</div>

## Component type: Predict
## Component type: Control method

Make predictions using a trained model.
Quality control methods for verifying the pipeline.

Arguments:

<div class="small">

| Name | Type | Description |
|:---|:---|:---|
| `--input_train_mod1` | `file` | (*Optional*) The mod1 expression values of the train cells. |
| `--input_train_mod2` | `file` | (*Optional*) The mod2 expression values of the train cells. |
| `--input_train_mod1` | `file` | The mod1 expression values of the train cells. |
| `--input_train_mod2` | `file` | The mod2 expression values of the train cells. |
| `--input_test_mod1` | `file` | The mod1 expression values of the test cells. |
| `--input_model` | `file` | A pretrained model for predicting the expression of one modality from another. |
| `--input_test_mod2` | `file` | The ground-truth mod2 expression values of the test cells. |
| `--output` | `file` | (*Output*) A prediction of the mod2 expression values of the test cells. |

</div>

## Component type: Train

Train a model to predict the expression of one modality from another.

Arguments:

<div class="small">
## File format: Pretrained model

| Name | Type | Description |
|:---|:---|:---|
| `--input_train_mod1` | `file` | The mod1 expression values of the train cells. |
| `--input_train_mod2` | `file` | The mod2 expression values of the train cells. |
| `--input_test_mod1` | `file` | (*Optional*) The mod1 expression values of the test cells. |
| `--output` | `file` | (*Output*) A pretrained model for predicting the expression of one modality from another. |
A pretrained model for predicting the expression of one modality from
another.

</div>
Example file: `model`

## Component type: Metric
## Component type: Predict

A predict modality metric.
Make predictions using a trained model.

Arguments:

<div class="small">

| Name | Type | Description |
|:---|:---|:---|
| `--input_prediction` | `file` | A prediction of the mod2 expression values of the test cells. |
| `--input_test_mod2` | `file` | The ground-truth mod2 expression values of the test cells. |
| `--output` | `file` | (*Output*) Metric score file. |
| `--input_train_mod1` | `file` | (*Optional*) The mod1 expression values of the train cells. |
| `--input_train_mod2` | `file` | (*Optional*) The mod2 expression values of the train cells. |
| `--input_test_mod1` | `file` | The mod1 expression values of the test cells. |
| `--input_model` | `file` | A pretrained model for predicting the expression of one modality from another. |
| `--output` | `file` | (*Output*) A prediction of the mod2 expression values of the test cells. |

</div>

Expand Down Expand Up @@ -460,58 +507,35 @@ Data structure:

</div>

## File format: Pretrained model

A pretrained model for predicting the expression of one modality from
another.

## File format: Score

Metric score file

Example file:
`resources_test/task_predict_modality/openproblems_neurips2021/bmmc_cite/swap/score.h5ad`

Format:

<div class="small">

AnnData object
uns: 'dataset_id', 'method_id', 'metric_ids', 'metric_values'
## Component type: Metric

</div>
A predict modality metric.

Data structure:
Arguments:

<div class="small">

| Slot | Type | Description |
| Name | Type | Description |
|:---|:---|:---|
| `uns["dataset_id"]` | `string` | A unique identifier for the dataset. |
| `uns["method_id"]` | `string` | A unique identifier for the method. |
| `uns["metric_ids"]` | `string` | One or more unique metric identifiers. |
| `uns["metric_values"]` | `double` | The metric values obtained for the given prediction. Must be of same length as ‘metric_ids’. |
| `--input_prediction` | `file` | A prediction of the mod2 expression values of the test cells. |
| `--input_test_mod2` | `file` | The ground-truth mod2 expression values of the test cells. |
| `--output` | `file` | (*Output*) Metric score file. |

</div>

## File format: Raw dataset mod2
## File format: Score

The second modality of the raw dataset. Must be an ADT or an ATAC
dataset
Metric score file

Example file:
`resources_test/common/openproblems_neurips2021/bmmc_cite/dataset_mod2.h5ad`
`resources_test/task_predict_modality/openproblems_neurips2021/bmmc_cite/swap/score.h5ad`

Format:

<div class="small">

AnnData object
obs: 'batch', 'size_factors'
var: 'feature_id', 'feature_name', 'hvg', 'hvg_score'
obsm: 'gene_activity'
layers: 'counts', 'normalized'
uns: 'dataset_id', 'dataset_name', 'dataset_url', 'dataset_reference', 'dataset_summary', 'dataset_description', 'dataset_organism', 'normalization_id', 'gene_activity_var_names'
uns: 'dataset_id', 'method_id', 'metric_ids', 'metric_values'

</div>

Expand All @@ -521,23 +545,9 @@ Data structure:

| Slot | Type | Description |
|:---|:---|:---|
| `obs["batch"]` | `string` | Batch information. |
| `obs["size_factors"]` | `double` | (*Optional*) The size factors of the cells prior to normalization. |
| `var["feature_id"]` | `string` | Unique identifier for the feature, usually a ENSEMBL gene id. |
| `var["feature_name"]` | `string` | (*Optional*) A human-readable name for the feature, usually a gene symbol. |
| `var["hvg"]` | `boolean` | Whether or not the feature is considered to be a ‘highly variable gene’. |
| `var["hvg_score"]` | `double` | A score for the feature indicating how highly variable it is. |
| `obsm["gene_activity"]` | `double` | (*Optional*) ATAC gene activity. |
| `layers["counts"]` | `integer` | Raw counts. |
| `layers["normalized"]` | `double` | Normalized expression values. |
| `uns["dataset_id"]` | `string` | A unique identifier for the dataset. |
| `uns["dataset_name"]` | `string` | Nicely formatted name. |
| `uns["dataset_url"]` | `string` | (*Optional*) Link to the original source of the dataset. |
| `uns["dataset_reference"]` | `string` | (*Optional*) Bibtex reference of the paper in which the dataset was published. |
| `uns["dataset_summary"]` | `string` | Short description of the dataset. |
| `uns["dataset_description"]` | `string` | Long description of the dataset. |
| `uns["dataset_organism"]` | `string` | (*Optional*) The organism of the sample in the dataset. |
| `uns["normalization_id"]` | `string` | The unique identifier of the normalization method used. |
| `uns["gene_activity_var_names"]` | `string` | (*Optional*) Names of the gene activity matrix. |
| `uns["method_id"]` | `string` | A unique identifier for the method. |
| `uns["metric_ids"]` | `string` | One or more unique metric identifiers. |
| `uns["metric_values"]` | `double` | The metric values obtained for the given prediction. Must be of same length as ‘metric_ids’. |

</div>
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