Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA.
Mohammadamin EdrisiXiru HuangHuw A OgilvieLuay K NakhlehPublished in: Nature communications (2023)
Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a method that uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we suggest, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barrett's esophagus progresses to esophageal cancer, reaffirming the findings of the previous studies.
Keyphrases
- single cell
- rna seq
- high throughput
- electronic health record
- single molecule
- circulating tumor
- big data
- cell free
- induced apoptosis
- computed tomography
- copy number
- magnetic resonance
- high resolution
- squamous cell carcinoma
- cell cycle arrest
- gene expression
- genome wide
- magnetic resonance imaging
- artificial intelligence
- peripheral blood
- oxidative stress
- contrast enhanced