SPIRAL: Significant Process InfeRence ALgorithm for single cell RNA-sequencing and spatial transcriptomics
Biran, H.; Hashimshony, T.; Mandel-Gutfreund, Y.; Yakhini, Z.
Show abstract
1Gene expression data is complex and may hold information regarding multiple biological processes at once. We present SPIRAL, an algorithm that uses a Gaussian statistical model to produce a comprehensive overview of a plurality of significant processes detected in single cell RNA-seq or spatial transcriptomics data. SPIRAL identifies biological processes by finding sub-matrices that consist of the subset of genes involved and the subset of cells or spots. We describe the algorithmic method, the analysis pipeline and several example results. SPIRAL is available at https://spiral.technion.ac.il/.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks 97%
- Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error 97%
- Application of Modular Response Analysis to Medium- to Large-Size Biological Systems 97%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.