Congratulations to Kaspar Martens on having his paper accepted in Bioinformatics.
Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori, while unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that is magnified when they are defined by differentially expressing a small number of genes.
Congratulations to Joel Nulsen on having his paper accepted in BMC Bioinformatics.
Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges. We previously developed the DigiPico sequencing platform to accurately identify somatic mutations from such samples.
We continue to contribute to the MUM-PREDICT and OPTIMAL projects over the last six months including:
Congratulations to Franziska Gunther on her collaborative work with Breaking Free Online on Identifying factors associated with user retention and outcomes of a digital intervention for substance use disorder: a retrospective analysis of real-world data which has been published in JAMIA Open.
Congratulations to Kaspar Martens on having his paper accepted at MLCB 2023.
Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity. Common or shared factors could be important for explaining variation across modalities whereas other factors may be private and important only for the explanation of a single modality. Multimodal Variational Autoencoders, such as MVAE and MMVAE, are a natural choice for inferring those underlying latent factors and separating shared variation from private. In this work, we investigate their capability to reliably perform this disentanglement. In particular, wehighlight a challenging problem setting where modality-specific variation dominates the shared signal. Taking a cross-modal prediction perspective, we demonstrate limitations of existing models, and propose a modification how to make them more robust to modalityspecific variation. Our findings are supported by experiments on synthetic as well as various real-world multi-omics data sets. The paper is available on PMLR.
Congratulations to Franziska Gunther on her collaborative work with Breaking Free Online which was presented at MIE 2023: On the difficulty of predicting engagement with digital interventions for substance use disorders.
We are proud to have been part of a glittering array of publications arising from our contribution to the MUM-PREDICT projects over the last 12 months. The team also won the Health Data Research UK Team of the Year 2022 award: