novoSpaRc - de novo Spatial Reconstruction of Single-Cell Gene Expression
novoSpaRc predicts locations of single cells in space by solely using
single-cell RNA sequencing data. An existing reference database of marker genes
is not required, but significantly enhances performance if available.
novoSpaRc accompanies the following publications:
and
novoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transportN Moriel*, E Senel*, N Friedman, N Rajewsky, N Karaiskos†, M Nitzan†
Version 0.4.4 11 October 2021
Fixed a bug regarding the distance metric usage in cost calculation.
Version 0.4.3 20 April 2021
Fixed bugs. Added self consistency analysis and updated tutorials.
Version 0.4.2 03 April 2021
Improved package structure, fixed minor performace issues, and fixed bugs. Added two new tutorials (corti & osteosarcoma) and updated the previous tutorials with validation analyses.
Version 0.4.1 24 August 2020
Changed the package structure and run flow of the scripts. Added anndata and scanpy support. Updated tutorials and implemented basic target geometries.
Version 0.3.11 27 April 2020
Moran’s I algorithm for spatially informative genes is implemented and removed pysal dependency.
Version 0.3.10 07 February 2020
Added Moran’s I algorithm to detect spatially informative genes.
Version 0.3.7 29 October 2019
Updated computation of shortest paths that singificantly reduces running time.
Version 0.3.5 13 June 2019
Fixed a bug that was prone to produce infinities during reconstruction. Improved plotting functions and added new ones for plotting mapped cells.
Version 0.3.4 27 February 2019
novoSpaRc reconstructs single-cell gene expression without relying on existing reference markers and makes great use of such information if available.