Back

A variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions from spatial transcriptomics

2025-12-11 genetic and genomic medicine Title + abstract only
View on medRxiv
Show abstract

Advanced spatially resolved transcriptomic (SRT) technologies preserve the spatial context of gene expression within tissues, enabling the study of context-dependent transcriptional regulation. Here, we propose VISGP, a variational sparse gaussian-process method for spatial variable genes (SVGs) and cellular interactions analysis from such data. VISGP utilizes variational inference and a sparse Gaussian process approximation, which efficiently models the posterior distribution with a set of indu...

Predicted journal destinations

1
Nature Communications
483 training papers
Top 1% 22.0%
2
Scientific Reports
701 training papers
Top 13% 11.7%
3
The American Journal of Human Genetics
77 training papers
Top 2% 7.7%
4
Proceedings of the National Academy of Sciences
100 training papers
Top 0.6% 6.4%
5
Nature Genetics
72 training papers
Top 3% 5.9%
6
Bioinformatics
24 training papers
Top 0.2% 4.1%
7
Cell Genomics
34 training papers
Top 0.6% 3.8%
8
eLife
262 training papers
Top 12% 3.8%
9
Genome Medicine
56 training papers
Top 3% 3.8%
10
Human Genetics and Genomics Advances
39 training papers
Top 2% 2.8%
11
PLOS Genetics
39 training papers
Top 2% 2.8%
12
Human Molecular Genetics
28 training papers
Top 2% 1.9%
13
PLOS Computational Biology
141 training papers
Top 8% 1.5%
14
PLOS ONE
1737 training papers
Top 95% 1.5%
15
Frontiers in Genetics
32 training papers
Top 4% 1.3%
16
Science Advances
52 training papers
Top 2% 1.3%
17
Nature
58 training papers
Top 5% 1.2%
18
Genetics in Medicine
57 training papers
Top 7% 0.9%
19
iScience
74 training papers
Top 10% 0.9%
20
Communications Biology
36 training papers
Top 5% 0.7%
21
European Journal of Human Genetics
25 training papers
Top 6% 0.7%
22
npj Genomic Medicine
18 training papers
Top 5% 0.7%
23
Statistics in Medicine
17 training papers
Top 0.5% 0.7%
24
Genes
21 training papers
Top 5% 0.7%