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README.md

@@ -12,13 +12,13 @@ pipeline_tag: feature-extraction
 
 # Varix-Dim16-Varix
 
-A [Varix](https://github.com/autoencodix/autoencodix) variational autoencoder with a 16-dimensional latent space, trained on single-cell RNA-seq data. Unlike Ontix, the latent dimensions are not constrained by gene ontologies, providing a compact general-purpose embedding of the transcriptome.
+A [Varix](https://github.com/jan-forest/autoencodix_package/) variational autoencoder with a 16-dimensional latent space, trained on single-cell RNA-seq data. Unlike Ontix, the latent dimensions are not constrained by gene ontologies, providing a compact general-purpose embedding of the transcriptome.
 
 Part of the [autoencodix pretrained model collection](https://huggingface.co/collections/autoencodix/acx-pretrained-models).
 
 ## Usage
 
-Install the [`autoencodix`](https://github.com/autoencodix/autoencodix) package:
+Install the [`autoencodix`](https://github.com/jan-forest/autoencodix_package/) package:
 
 ```bash
 pip install autoencodix
@@ -81,4 +81,4 @@ df_latent
 
 ## Further usage
 
-For a full walkthrough including latent space visualization, marker gene explanation with xAI/LLMs, and synthetic data generation, see the [tutorial notebook](https://github.com/autoencodix/autoencodix/blob/main/Tutorials/DeepDives/UsingPreTrainedModels.ipynb).
+For a full walkthrough including latent space visualization, marker gene explanation with xAI/LLMs, and synthetic data generation, see the [tutorial notebook](https://github.com/jan-forest/autoencodix_package/blob/main/Tutorials/DeepDives/UsingPreTrainedModels.ipynb).