From c293f75017173b05f7d28eb4c8f2eae02f4b2cc6 Mon Sep 17 00:00:00 2001 From: moinfar Date: Mon, 11 Nov 2024 12:32:41 +0100 Subject: [PATCH] Fix doc issue --- docs/references.bib | 27 --------------------------- 1 file changed, 27 deletions(-) diff --git a/docs/references.bib b/docs/references.bib index a19f4ed..7387b1b 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -16,33 +16,6 @@ @ARTICLE{Moinfar2024-cx 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"