Fix doc issue

docs/references.bib

@@ -8,3 +8,42 @@ @article{Virshup_2023
 	title = {The scverse project provides a computational ecosystem for single-cell omics data analysis},
 	journal = {Nature Biotechnology}
 }
+
+@ARTICLE{Moinfar2024-cx,
+  doi = {10.1101/2024.11.06.622266},
+  url = {https://doi.org/10.1101/2024.11.06.622266},
+  title    = "Unsupervised deep disentangled representation of single-cell omics",
+  author   = "Moinfar, Amir Ali and Theis, Fabian J",
+  journal  = "bioRxiv",
+  pages    = "2024.11.06.622266",
+  abstract = "Single-cell genomics allows for the unbiased exploration of
+              cellular heterogeneity. Representation learning methods summarize
+              high-dimensional single-cell data into a manageable latent space
+              in a typically nonlinear fashion, allowing cross-sample
+              integration or generative modeling. However, these methods often
+              produce entangled representations, limiting interpretability and
+              downstream analyses. Existing disentanglement methods instead
+              either require supervised information or impose sparsity and
+              linearity, which may not capture the complexity of biological
+              data. We, therefore, introduce Disentangled Representation
+              Variational Inference (DRVI), an unsupervised deep generative
+              model that learns nonlinear, disentangled representations of
+              single-cell omics. This is achieved by combining recently
+              introduced additive decoders with nonlinear pooling, for which we
+              theoretically prove disentanglement under reasonable assumptions.
+              We validate DRVI's disentanglement capabilities across diverse
+              relevant biological problems, from development to perturbational
+              studies and cell atlases, decomposing, for example, the Human Lung
+              Cell Atlas into meaningful, interpretable latent dimensions.
+              Moreover, we demonstrate that if applied to batch integration,
+              DRVI's integration quality does not suffer from the
+              disentanglement constraints and instead is on par with entangled
+              integration methods. With its disentangled latent space, DRVI is
+              inherently interpretable and facilitates the identification of
+              rare cell types, provides novel insights into cellular
+              heterogeneity beyond traditional cell types, and highlights
+              developmental stages.",
+  month    =  nov,
+  year     =  2024,
+  language = "en"
+}

docs/references.md

@@ -1,6 +1,7 @@
 # References
 
-If DRVI is helpful in your research, please consider citing the following paper:
+If DRVI is helpful in your research, please consider citing {cite:p}`Moinfar2024-cx`.
+For other references used in the documentation see below:
 
 ```{bibliography}
 :cited: