GB

G.A. Bouland

10 records found

Authored

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic pro ...

With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression g ...

Background and Objectives - With age, somatic mutations accumulated in human brain cells can lead to various neurologic disorders and brain tumors. Because the incidence rate of Alzheimer disease (AD) increases exponentially with age, investigating the association between AD and ...

Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological ...

Contributed

Understanding the role of genes and genetic variants is a key challenge in unraveling the driving mechanisms of Alzheimer's disease (AD). Single-cell RNA sequencing is a technique that quantifies gene expression at the cell (type) level enabling investigation of the roles of diff ...

Binarized single cell RNA sequencing data clustering

The impact of binarized scRNA-seq data on clustering through community detection algorithms

Single-cell RNA sequencing data clustering is a valuable technique for demonstrating cell-to-cell heterogeneity and revealing cell dynamics within and amongst groups. Large up-scaling of scRNA-seq datasets in recent years pose computational challenges for existing state-of-the-ar ...

Memory usage analysis of binary clustering algorithm

What is the gain in peak memory usage of the binary clustering algorithm compared to current state-of-the-art clustering methods?

The rapid increase in the size of single-cell RNAseq datasets presents significant performance challenges when conducting evaluations and extracting information. We research an alternative input data format that utilizes binarization. Our main focus is an analysis of peak memory ...

Similarity metrics for binary cell clustering

How close can we get to state-of-the-art ?

Analysing single-cell RNA sequencing data is becoming an increasingly tedious task as the size of data sets grows. As a proposed solution, recent discoveries suggest that these data sets can be binarized without losing much information. This in turn should allow for memory and ti ...
As single-cell RNA sequencing techniques improve and more cells are measured in individual experiments, cell clustering procedures become increasingly more computationally intensive. This paper studies the runtime performance impact of a specialized clustering algorithm for data ...
Identifying key genes in Alzheimer’s Disease (AD) is important in increasing understanding about its pathogenesis, and discovering potential therapeutic targets. Recent advances in single-cell RNA sequencing (scRNAseq) technology have provided unprecedented opportunities to study ...