Reduction of high-dimensional datasets to 2D for visualization & interpretation.
Repository: openproblems-bio/task_dimensionality_reduction
Data visualisation is an important part of all stages of single-cell analysis, from initial quality control to interpretation and presentation of final results. For bulk RNA-seq studies, linear dimensionality reduction techniques such as PCA and MDS are commonly used to visualise the variation between samples. While these methods are highly effective they can only be used to show the first few components of variation which cannot fully represent the increased complexity and number of observations in single-cell datasets. For this reason non-linear techniques (most notably t-SNE and UMAP) have become the standard for visualising single-cell studies. These methods attempt to compress a dataset into a two-dimensional space while attempting to capture as much of the variance between observations as possible. Many methods for solving this problem now exist. In general these methods try to preserve distances, while some additionally consider aspects such as density within the embedded space or conservation of continuous trajectories. Despite almost every single-cell study using one of these visualisations there has been debate as to whether they can effectively capture the variation in single-cell datasets [@chari2023speciousart].
The dimensionality reduction task attempts to quantify the ability of methods to embed the information present in complex single-cell studies into a two-dimensional space. Thus, this task is specifically designed for dimensionality reduction for visualisation and does not consider other uses of dimensionality reduction in standard single-cell workflows such as improving the signal-to-noise ratio (and in fact several of the methods use PCA as a pre-processing step for this reason). Unlike most tasks, methods for the dimensionality reduction task must accept a matrix containing expression values normalised to 10,000 counts per cell and log transformed (log-10k) and produce a two-dimensional coordinate for each cell. Pre-normalised matrices are required to enforce consistency between the metric evaluation (which generally requires normalised data) and the method runs. When these are not consistent, methods that use the same normalisation as used in the metric tend to score more highly. For some methods we also evaluate the pre-processing recommended by the method.
| name | roles |
|---|---|
| Luke Zappia | maintainer, author |
| Michal Klein | author |
| Scott Gigante | author |
| Ben DeMeo | author |
| Robrecht Cannoodt | author |
| Kai Waldrant | contributor |
| Sai Nirmayi Yasa | contributor |
| Juan A. Cordero Varela | contributor |
flowchart LR
file_common_dataset("Dataset")
comp_process_dataset[/"Data processor"/]
file_dataset("Dataset")
file_solution("Test data")
comp_control_method[/"Control method"/]
comp_method[/"Method"/]
comp_metric[/"Metric"/]
file_embedding("Embedding")
file_score("Score")
file_common_dataset---comp_process_dataset
comp_process_dataset-->file_dataset
comp_process_dataset-->file_solution
file_dataset---comp_control_method
file_dataset---comp_method
file_solution---comp_control_method
file_solution---comp_metric
comp_control_method-->file_embedding
comp_method-->file_embedding
comp_metric-->file_score
file_embedding---comp_metric
The dataset to pass to a method.
Example file: resources_test/common/pancreas/dataset.h5ad
Format:
AnnData object
obs: 'cell_type'
var: 'hvg_score'
layers: 'counts', 'normalized'
uns: 'dataset_id', 'dataset_name', 'dataset_url', 'dataset_reference', 'dataset_summary', 'dataset_description', 'dataset_organism', 'normalization_id'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["cell_type"] |
string |
Classification of the cell type based on its characteristics and function within the tissue or organism. |
var["hvg_score"] |
double |
High variability gene score (normalized dispersion). The greater, the more variable. |
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 |
Which normalization was used. |
A dimensionality reduction dataset processor.
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
The dataset to pass to a method. |
--output_dataset |
file |
(Output) The dataset to pass to a method. |
--output_solution |
file |
(Output) The data for evaluating a dimensionality reduction. |
The dataset to pass to a method.
Example file:
resources_test/dimensionality_reduction/pancreas/dataset.h5ad
Format:
AnnData object
var: 'hvg_score'
layers: 'counts', 'normalized'
uns: 'dataset_id', 'normalization_id'
Data structure:
| Slot | Type | Description |
|---|---|---|
var["hvg_score"] |
double |
High variability gene score (normalized dispersion). The greater, the more variable. |
layers["counts"] |
integer |
Raw counts. |
layers["normalized"] |
double |
Normalized expression values. |
uns["dataset_id"] |
string |
A unique identifier for the dataset. |
uns["normalization_id"] |
string |
Which normalization was used. |
The data for evaluating a dimensionality reduction.
Example file:
resources_test/dimensionality_reduction/pancreas/solution.h5ad
Format:
AnnData object
obs: 'cell_type'
var: 'hvg_score'
layers: 'counts', 'normalized'
uns: 'dataset_id', 'dataset_name', 'dataset_url', 'dataset_reference', 'dataset_summary', 'dataset_description', 'dataset_organism', 'normalization_id'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["cell_type"] |
string |
Classification of the cell type based on its characteristics and function within the tissue or organism. |
var["hvg_score"] |
double |
High variability gene score (normalized dispersion). The greater, the more variable. |
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 |
Which normalization was used. |
Quality control methods for verifying the pipeline.
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
The dataset to pass to a method. |
--input_solution |
file |
The data for evaluating a dimensionality reduction. |
--output |
file |
(Output) A dataset with dimensionality reduction embedding. |
A dimensionality reduction method.
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
The dataset to pass to a method. |
--output |
file |
(Output) A dataset with dimensionality reduction embedding. |
A dimensionality reduction metric.
Arguments:
| Name | Type | Description |
|---|---|---|
--input_embedding |
file |
A dataset with dimensionality reduction embedding. |
--input_solution |
file |
The data for evaluating a dimensionality reduction. |
--output |
file |
(Output) Metric score file. |
A dataset with dimensionality reduction embedding.
Example file:
resources_test/dimensionality_reduction/pancreas/embedding.h5ad
Format:
AnnData object
obsm: 'X_emb'
uns: 'dataset_id', 'method_id', 'normalization_id'
Data structure:
| Slot | Type | Description |
|---|---|---|
obsm["X_emb"] |
double |
The dimensionally reduced embedding. |
uns["dataset_id"] |
string |
A unique identifier for the dataset. |
uns["method_id"] |
string |
A unique identifier for the method. |
uns["normalization_id"] |
string |
Which normalization was used. |
Metric score file
Example file:
resources_test/dimensionality_reduction/pancreas/score.h5ad
Format:
AnnData object
uns: 'dataset_id', 'normalization_id', 'method_id', 'metric_ids', 'metric_values'
Data structure:
| Slot | Type | Description |
|---|---|---|
uns["dataset_id"] |
string |
A unique identifier for the dataset. |
uns["normalization_id"] |
string |
Which normalization was used. |
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’. |