From Raw Data to Biological Insights: A Practical Guide for Spatial Transcriptomics Analysis in R and Python.

Ahmed M Elhossiny, Reva Kulkarni, Arvind Rao, Timothy L Frankel

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ahmed M ElhossinyDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Reva KulkarniDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Arvind RaoDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Timothy L FrankelDepartment of Surgery, University of Michigan, Ann Arbor, MI, USA. timofran@med.umich.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spatial transcriptomics has revolutionized molecular biology by enabling the capture of gene expression within intact tissue architecture, thereby preserving crucial spatial context often lost in traditional single-cell RNA-sequencing. This breakthrough allows for the study of cellular organization, neighborhood interactions, and functional niches, offering critical insights into complex biological processes. To effectively analyze this complex data, specialized data containers are employed, integrating spatial coordinates with molecular expression. Before extracting biological insights, rigorous preprocessing steps are essential to characterize the tissue accurately. These foundational steps are followed by downstream analyses, including deconvolution, spatial-domain identification, and advanced methods for cell-type-specific differential expression, cell-cell communication, and neighborhood analysis. This chapter provides detailed descriptions of major R and Python object classes tailored for spatial transcriptomics analysis, supporting comprehensive workflows from data management to the derivation of profound biological insights.

Indexed as

Computational BiologyGene Expression ProfilingSoftwareTranscriptomeAnimalsSingle-Cell Gene Expression AnalysisSpatial TranscriptomicsClusteringData analysisData objectsDeconvolutionGiottoPythonRSeuratSpatial feature experimentSpatial transcriptomicsSquidpy

Identifiers

PMID42062686

DOI10.1038/s41592-024-02215-8

What Socratic holds

Textmetadata

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.