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 time efficient methods of storage and computation. Numerous analyses...

Accelerating Cluster Assignment for SeqClu

Clustering is a group of (unsupervised) machine learning algorithms used to categorize data into clusters. The most popular clustering algorithm is k-means clustering. K-means clustering clusters the data into k clusters where a cluster is represented by the mean of the data points called a centroid. Instead of using the mean as a centroid, a...

CBA: Cluster-Guided Batch Alignment for Single Cell RNA-seq

The power of single-cell RNA sequencing (scRNA-seq) in detecting cell heterogeneity or developmental process is becoming more and more evident every day. The granularity of this knowledge is further propelled when combining two batches of scRNA-seq into a single large dataset. This strategy is however hampered by technical differences between...

A data-driven methodology reveals novel myofiber clusters in older human muscles

Skeletal muscles control posture, mobility and strength, and influence whole-body metabolism. Muscles are built of different types of myofibers, each having specific metabolic, molecular, and contractile properties. Fiber classification is, therefore, regarded the key for understanding muscle biology, (patho-) physiology. The expression of...