Table 2 Computational tools and associated methods for spatial transcriptomics data analysis and visualisation.

RNAscope²⁹

Proprietary software

No

N/A

N/A

N/A

N/A

DistMap⁵⁰

Distributed mapping scores

Yes

Count matrix, reference in situ coordinates

Expression patterns

R

https://github.com/rajewsky-lab/distmap

NMFreg⁶¹

Non-negative matrix factorization regression

Yes

Count matrix, 2D spatial coordinates

Expression patterns

Python

https://github.com/tudaga/NMFreg_tutorial

LIGER⁶⁴

Integrative non-negative matrix factorization

Yes

Count matrix, scMethylation, scATAC-seq

Expression patterns, cell clusters

R

https://github.com/MacoskoLab/liger

Harmony⁶⁵

Maximum diversity clustering, linear batch correction

Yes

Count matrix, MERFISH coordinates

Expression patterns

R

https://github.com/immunogenomics/harmony

SpaOTsc⁶⁷

Structured optimal transport

Yes

Count matrix, spatial coordinates, dissimilarity matrices

Expression patterns

Python

https://github.com/zcang/SpaOTsc

NovoSpaRc⁶⁹

Generalised optimal-transport

Yes

Count matrix, target space image

Expression patterns

Python

https://github.com/rajewsky-lab/novosparc

ScoMAP⁷⁰

Axial information extraction via pseudotime ordering

Yes

Count matrix, virtual spatial template

Expression patterns

R

https://github.com/aertslab/ScoMAP

CSOmap⁷¹

Cellular Spatial Organization mapper

Yes

Count matrix, label, ligand-receptor

Expression patterns

MatLab

https://codeocean.com/capsule/2860903/tree/v1

Giotto⁶⁸

Binary Spatial extraction, hidden Markov random field (HMRF) model

Yes

Count matrix, spatial coordinates

Expression patterns

R, Python

https://rubd.github.io/Giotto/

SPOTlight⁹⁸

Seeded non-negative matrix factorization (NMF) regression

Yes

Count matrix, spatial coordinates

Expression patterns

R

https://github.com/MarcElosua/SPOTlight