Prediction of cell position using single-cell transcriptomic data: an iterative procedure.
Andrés Mariano AlonsoAlejandra CarreaLuis A DiambraPublished in: F1000Research (2019)
Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. With the purpose of developing new algorithms, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). Within this context, we describe here our proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.
Keyphrases
- single cell
- rna seq
- genome wide
- high throughput
- bioinformatics analysis
- genome wide identification
- machine learning
- electronic health record
- minimally invasive
- induced apoptosis
- genome wide analysis
- dna methylation
- big data
- magnetic resonance imaging
- magnetic resonance
- computed tomography
- data analysis
- oxidative stress
- signaling pathway
- contrast enhanced
- artificial intelligence