Single cell computational analysis has emerged as a powerful tool in the field of oncology,
enabling researchers to decipher the complex cellular heterogeneity that characterizes …

Abstract Two-dimensional (2D) embedding methods are crucial for single-cell data
visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) …

Single-cell genomics technologies enable multimodal profiling of millions of cells across
temporal and spatial dimensions. Experimental limitations prevent the measurement of all …

Mammalian cells release a heterogeneous array of extracellular vesicles (EVs) that
contribute to intercellular communication by means of the cargo that they carry. To resolve …

Metabolic dysfunction‐associated steatotic liver disease (MASLD) is a growing cause of
morbidity with limited treatment options. Thus, accurate in vitro systems to test new therapies …

While experimental and informatic techniques around single cell sequencing (scRNA-seq)
are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged …

The fields of Metagenomics and Metatranscriptomics involve the examination of complete
nucleotide sequences, gene identification, and analysis of potential biological functions …

In data science and visualization, dimensionality reduction techniques have been
extensively employed for exploring large datasets. These techniques involve the …

Resting memory B cells can be divided into classical or atypical groups, but the
heterogenous marker expression on activated memory B cells makes similar classification …

Protein language models, trained on millions of biologically observed sequences, generate
feature-rich numerical representations of protein sequences. These representations, called …