Cross-species imputation and comparison of single-cell transcriptomic profiles.
Ran ZhangMu YangJacob SchreiberDiana R O'DayJames M A TurnerJay ShendureChristine M DistecheXinxian DengWilliam Stafford NoblePublished in: bioRxiv : the preprint server for biology (2023)
Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.
Keyphrases
- single cell
- rna seq
- high throughput
- genome wide
- high resolution
- copy number
- healthcare
- genome wide identification
- stem cells
- induced apoptosis
- mesenchymal stem cells
- cell proliferation
- signaling pathway
- poor prognosis
- mass spectrometry
- big data
- artificial intelligence
- electronic health record
- cell death
- oxidative stress
- deep learning
- gene expression
- multidrug resistant
- endoplasmic reticulum stress
- high intensity
- data analysis
- liquid chromatography
- cell cycle arrest