Back

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Crowell, H. L.; Dong, Y.; Billato, I.; Cai, P.; Emons, M.; Gunz, S.; Guo, B.; Li, M.; Mahmoud, A.; Manukyan, A.; Pages, H.; Panwar, P.; Rao, S.; Sargeant, C. J.; Shepherd Kern, L.; Ramos, M.; Sun, J.; Totty, M.; Carey, V. J.; Chen, Y.; Collado-Torres, L.; Ghazanfar, S.; Hansen, K. D.; Martinowich, K.; Maynard, K. R.; Patrick, E.; Righelli, D.; Risso, D.; Tiberi, S.; Waldron, L.; Gottardo, R.; Robinson, M. D.; Hicks, S. C.; Weber, L. M.

2026-02-09 bioinformatics
10.1101/2025.11.20.688607 bioRxiv
Show abstract

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 1%
21.6%
Similar papers in this journal
2
Nucleic Acids Research
◐ 1281 papers in training set
Top 2%
10.4%
Similar papers in this journal
3
GigaScience
◐ 212 papers in training set
Top 0.2%
9.5%
Similar papers in this journal
4
Briefings in Bioinformatics
◐ 354 papers in training set
Top 1%
6.6%
Similar papers in this journal
5
PLOS Computational Biology
● 1863 papers in training set
Top 7%
5.4%
Similar papers in this journal
50% of probability mass above
6
BMC Bioinformatics
○ 457 papers in training set
Top 2%
5.4%
7
Nature Protocols
○ 33 papers in training set
Top 0.1%
4.8%
8
Biological Imaging
◐ 15 papers in training set
Top 0.1%
4.0%
9
Nature Communications
○ 5641 papers in training set
Top 33%
4.0%
10
Genome Biology
○ 637 papers in training set
Top 3%
4.0%
11
eLife
● 5828 papers in training set
Top 42%
2.4%
12
Nature Methods
○ 385 papers in training set
Top 4%
1.7%
13
Bioinformatics Advances
◐ 203 papers in training set
Top 3%
1.7%
14
Scientific Data
○ 209 papers in training set
Top 1%
1.7%
15
PLOS ONE
● 5266 papers in training set
Top 53%
1.1%
16
Communications Biology
○ 993 papers in training set
Top 23%
1.1%
17
SoftwareX
○ 15 papers in training set
Top 0.2%
1.1%
18
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 4%
1.0%
19
BMC Genomics
○ 406 papers in training set
Top 8%
0.8%
20
Molecular Biology and Evolution
◐ 542 papers in training set
Top 6%
0.6%
21
Nature Biotechnology
○ 172 papers in training set
Top 5%
0.6%

"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.