Consequences and opportunities arising due to sparser single-cell RNA-seq datasets.
Gerard A BoulandAhmed MahfouzMarcel J T ReindersPublished in: Genome biology (2023)
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 give similar results as count-based analyses. Moreover, a binary representation scales up to ~ 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.
Keyphrases
- single cell
- rna seq
- induced apoptosis
- gene expression
- high throughput
- cell cycle arrest
- dna methylation
- endoplasmic reticulum stress
- oxidative stress
- palliative care
- air pollution
- peripheral blood
- cell death
- deep learning
- electronic health record
- locally advanced
- liquid chromatography
- rectal cancer
- high throughput sequencing