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scCross

A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.

For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:

Overview

Alt text

Single-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.

Key Capabilities

  1. Combine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.

  2. Generate cross-compatible single-cell data between two or more different omics. Trained and tested on independent referenced multi-omics datasets is also feasible.

  3. Augment single-cell omics data through self-improvement techniques.

  4. Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.

  5. Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.

  6. Maintain genomic integrity during omics perturbations and cross-generations effectively.

Installation

You may install scCross by the following command:

pip install sccross

Example workthroughs

Example workthroughs for each dataset in our study can be found in the examples forder.

Codeocean

We employ codeocean reproducible platform to help you get into our codes.

Other repo

You can also access our code at scCross, which is the same as this repo.

About

A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.

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scCross
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:
Overview
Alt text

Single-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.

Key Capabilities
  1. Combine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.
  2. Generate cross-compatible single-cell data between two or more different omics. Trained and tested on independent referenced multi-omics datasets is also feasible.
  3. Augment single-cell omics data through self-improvement techniques.
  4. Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.
  5. Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.
  6. Maintain genomic integrity during omics perturbations and cross-generations effectively.
Installation
You may install scCross by the following command:
pip install sccross

Example workthroughs
Example workthroughs for each dataset in our study can be found in the examples forder.
Codeocean
We employ codeocean reproducible platform to help you get into our codes.
Other repo
You can also access our code at scCross, which is the same as this repo.
About
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
Resources
Readme
MIT license
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19 stars
Watchers
0 watching
Forks
3 forks
Report repository
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scCross
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:
Overview
Alt text

Single-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.

Key Capabilities
  1. Combine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.
  2. Generate cross-compatible single-cell data between two or more different omics. Trained and tested on independent referenced multi-omics datasets is also feasible.
  3. Augment single-cell omics data through self-improvement techniques.
  4. Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.
  5. Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.
  6. Maintain genomic integrity during omics perturbations and cross-generations effectively.
Installation
You may install scCross by the following command:
pip install sccross

Example workthroughs
Example workthroughs for each dataset in our study can be found in the examples forder.
Codeocean
We employ codeocean reproducible platform to help you get into our codes.
Other repo
You can also access our code at scCross, which is the same as this repo.
About
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
Resources
Readme
MIT license
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19 stars
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3 forks
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scCross
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:
Overview
Alt text

Single-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.

Key Capabilities
  1. Combine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.
  2. Generate cross-compatible single-cell data between two or more different omics. Trained and tested on independent referenced multi-omics datasets is also feasible.
  3. Augment single-cell omics data through self-improvement techniques.
  4. Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.
  5. Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.
  6. Maintain genomic integrity during omics perturbations and cross-generations effectively.
Installation
You may install scCross by the following command:
pip install sccross

Example workthroughs
Example workthroughs for each dataset in our study can be found in the examples forder.
Codeocean
We employ codeocean reproducible platform to help you get into our codes.
Other repo
You can also access our code at scCross, which is the same as this repo.
About
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
Resources
Readme
MIT license
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Stars
19 stars
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3 forks
Report repository
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scCross
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:
Overview
Alt text

Single-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.

Key Capabilities
  1. Combine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.
  2. Generate cross-compatible single-cell data between two or more different omics. Trained and tested on independent referenced multi-omics datasets is also feasible.
  3. Augment single-cell omics data through self-improvement techniques.
  4. Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.
  5. Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.
  6. Maintain genomic integrity during omics perturbations and cross-generations effectively.
Installation
You may install scCross by the following command:
pip install sccross

Example workthroughs
Example workthroughs for each dataset in our study can be found in the examples forder.
Codeocean
We employ codeocean reproducible platform to help you get into our codes.
Other repo
You can also access our code at scCross, which is the same as this repo.
About
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
Resources
Readme
MIT license
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Stars
19 stars
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3 forks
Report repository
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scCross
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:
Overview
Alt text

Single-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.

Key Capabilities
  1. Combine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.
  2. Generate cross-compatible single-cell data between two or more different omics. Trained and tested on independent referenced multi-omics datasets is also feasible.
  3. Augment single-cell omics data through self-improvement techniques.
  4. Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.
  5. Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.
  6. Maintain genomic integrity during omics perturbations and cross-generations effectively.
Installation
You may install scCross by the following command:
pip install sccross

Example workthroughs
Example workthroughs for each dataset in our study can be found in the examples forder.
Codeocean
We employ codeocean reproducible platform to help you get into our codes.
Other repo
You can also access our code at scCross, which is the same as this repo.
About
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
Resources
Readme
MIT license
Activity
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Stars
19 stars
Watchers
0 watching
Forks
3 forks
Report repository
Releases
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Used by
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scCross
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:
Overview
Alt text

Single-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.

Key Capabilities
  1. Combine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.
  2. Generate cross-compatible single-cell data between two or more different omics. Trained and tested on independent referenced multi-omics datasets is also feasible.
  3. Augment single-cell omics data through self-improvement techniques.
  4. Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.
  5. Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.
  6. Maintain genomic integrity during omics perturbations and cross-generations effectively.
Installation
You may install scCross by the following command:
pip install sccross

Example workthroughs
Example workthroughs for each dataset in our study can be found in the examples forder.
Codeocean
We employ codeocean reproducible platform to help you get into our codes.
Other repo
You can also access our code at scCross, which is the same as this repo.
About
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
Resources
Readme
MIT license
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19 stars
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3 forks
Report repository
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scCross
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:
Overview
Alt text

Single-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.

Key Capabilities
  1. Combine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.
  2. Generate cross-compatible single-cell data between two or more different omics. Trained and tested on independent referenced multi-omics datasets is also feasible.
  3. Augment single-cell omics data through self-improvement techniques.
  4. Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.
  5. Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.
  6. Maintain genomic integrity during omics perturbations and cross-generations effectively.
Installation
You may install scCross by the following command:
pip install sccross

Example workthroughs
Example workthroughs for each dataset in our study can be found in the examples forder.
Codeocean
We employ codeocean reproducible platform to help you get into our codes.
Other repo
You can also access our code at scCross, which is the same as this repo.
About
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.
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