Maren Hackenberg
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View article: scSpecies: enhancement of network architecture alignment in comparative single-cell studies
scSpecies: enhancement of network architecture alignment in comparative single-cell studies Open
Animals can provide meaningful context for human single-cell data. To transfer information between species, we propose a deep learning approach that pre-trains a conditional variational autoencoder on animal data and transfers its final en…
View article: Sparse dimensionality reduction for analyzing single-cell-resolved interactions
Sparse dimensionality reduction for analyzing single-cell-resolved interactions Open
Summary Several approaches have been proposed to reconstruct interactions between groups of cells or individual cells from single-cell transcriptomics data, leveraging prior information about known ligand-receptor interactions. To enhance …
View article: Adding layers of information to scRNA-seq data using pre-trained language models
Adding layers of information to scRNA-seq data using pre-trained language models Open
Single-cell technologies generate increasingly complex and multi-layered datasets, increasing the need for analysis workflows that incorporate additional biological information. Pretrained language models, with access to large corpora of b…
View article: Small Data Explainer -- The impact of small data methods in everyday life
Small Data Explainer -- The impact of small data methods in everyday life Open
The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings with limited information, can benefit from such developments. This includes societal issues such as…
View article: Constructed growth charts and nutrition for pontocerebellar hypoplasia type <scp>2A</scp>
Constructed growth charts and nutrition for pontocerebellar hypoplasia type <span>2A</span> Open
Aim To calculate growth charts for pontocerebellar hypoplasia (PCH) type 2A (PCH2A) and compare them to German reference charts, especially with regard to nutritional aspects. Method Data were gathered from a cohort of patients with geneti…
View article: Diagnostic clues and pitfalls in pontocerebellar hypoplasia type 2A
Diagnostic clues and pitfalls in pontocerebellar hypoplasia type 2A Open
Introduction Pontocerebellar hypoplasia type 2A (PCH2A) is a rare autosomal recessive neurodegenerative disease caused by a specific pathogenic variant in the TSEN54 gene (p.A307S). Affected children show early but initially unspecific sym…
View article: Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data
Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data Open
There are various dimensionality reduction techniques for visually inspecting dynamical patterns in time-series single-cell RNA-sequencing (scRNA-seq) data. However, the lack of one-to-one correspondence between cells across time points ma…
View article: Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data
Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data Open
There are various dimensionality reduction techniques for visually inspecting dynamical patterns in time-series single-cell RNA-sequencing (scRNA-seq) data. However, the lack of one-to-one correspondence between cells across time points ma…
View article: Investigating a Domain Adaptation Approach for Integrating Different Measurement Instruments in a Longitudinal Clinical Registry
Investigating a Domain Adaptation Approach for Integrating Different Measurement Instruments in a Longitudinal Clinical Registry Open
In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points. To combine them, we investigate deep learning techniques for obtaining a joint latent represent…
View article: Sparse dimensionality reduction for analyzing single-cell-resolved interactions
Sparse dimensionality reduction for analyzing single-cell-resolved interactions Open
Summary Several approaches have been proposed to reconstruct interactions between groups of cells or individual cells from single-cell transcriptomics data, leveraging prior information about known ligand-receptor interactions. To enhance …
View article: Sparse Dimensionality Reduction for Analyzing Single-Cell-Resolved Interactions
Sparse Dimensionality Reduction for Analyzing Single-Cell-Resolved Interactions Open
Several approaches have been proposed to reconstruct interactions between groups of cells or individual cells from single-cell transcriptomics data, leveraging prior information about known ligand-receptor interactions. To enhance downstre…
View article: Enhancement of Network Architecture Alignment in Comparative Single-Cell Studies
Enhancement of Network Architecture Alignment in Comparative Single-Cell Studies Open
Animal data can provide meaningful context for human gene expression at the single-cell level. This can improve cell-type detection and clarify how well animal models represent human biology. To achieve this, we propose a deep learning app…
View article: Enhancement of network architecture alignment in comparative single-cell studies
Enhancement of network architecture alignment in comparative single-cell studies Open
1 Abstract Animal data can provide meaningful context for human gene expression at the single-cell level. This context can improve cell-type or cell-state detection and clarify how well the animal models human biological processes. To achi…
View article: Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets
Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets Open
Dimensionality reduction greatly facilitates the exploration of cellular heterogeneity in single-cell RNA sequencing data. While most of such approaches are data-driven, it can be useful to incorporate biologically plausible assumptions ab…
View article: Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups
Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups Open
In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such …
View article: Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registry
Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registry Open
In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points. To combine them, we investigate deep learning techniques for obtaining a joint latent represent…
View article: A statistical approach to latent dynamic modeling with differential equations
A statistical approach to latent dynamic modeling with differential equations Open
Ordinary differential equations (ODEs) can provide mechanistic models of temporally local changes of processes, where parameters are often informed by external knowledge. While ODEs are popular in systems modeling, they are less establishe…
View article: The performance of deep generative models for learning joint embeddings of single-cell multi-omics data
The performance of deep generative models for learning joint embeddings of single-cell multi-omics data Open
Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing inform…
View article: Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data" Open
Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data" Code available at https://github.com/MTreppner/multiomics_dgms
View article: Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data" Open
Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data" Code available at https://github.com/MTreppner/multiomics_dgms
View article: Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data" Open
Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data" Code available at https://github.com/MTreppner/multiomics_dgms
View article: The <b>JuliaConnectoR</b>: A Functionally-Oriented Interface for Integrating <i>Julia</i> in <i>R</i>
The <b>JuliaConnectoR</b>: A Functionally-Oriented Interface for Integrating <i>Julia</i> in <i>R</i> Open
Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the Comprehensive R A…
View article: Using Differentiable Programming for Flexible Statistical Modeling
Using Differentiable Programming for Flexible Statistical Modeling Open
Differentiable programming has recently received much interest as a paradigm that facilitates taking gradients of computer programs. While the corresponding flexible gradient-based optimization approaches so far have been used predominantl…
View article: Incorporating structural knowledge into unsupervised deep learning for two-photon imaging data
Incorporating structural knowledge into unsupervised deep learning for two-photon imaging data Open
Live imaging techniques, such as two-photon imaging, promise novel insights into cellular activity patterns at a high spatio-temporal resolution. While current deep learning approaches typically focus on specific supervised tasks in the an…
View article: Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?
Deep dynamic modeling with just two time points: Can we still allow for individual trajectories? Open
Longitudinal biomedical data are often characterized by a sparse time grid and individual-specific development patterns. Specifically, in epidemiological cohort studies and clinical registries we are facing the question of what can be lear…
View article: Exploring generative deep learning for omics data using log-linear models
Exploring generative deep learning for omics data using log-linear models Open
Motivation Following many successful applications to image data, deep learning is now also increasingly considered for omics data. In particular, generative deep learning not only provides competitive prediction performance, but also allow…
View article: The JuliaConnectoR: a functionally oriented interface for integrating Julia in R
The JuliaConnectoR: a functionally oriented interface for integrating Julia in R Open
Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the CRAN repository a…