{"id":4206,"date":"2026-08-20T10:34:45","date_gmt":"2026-08-20T03:34:45","guid":{"rendered":"https:\/\/bioturing.com\/blog\/?p=4206"},"modified":"2026-08-20T10:42:57","modified_gmt":"2026-08-20T03:42:57","slug":"five-common-mistakes-in-spatial-transcriptomics-analysis-part-2","status":"publish","type":"post","link":"https:\/\/bioturing.com\/blog\/five-common-mistakes-in-spatial-transcriptomics-analysis-part-2\/","title":{"rendered":"Five Common Mistakes in Spatial Transcriptomics Analysis \u2013 Part II"},"content":{"rendered":"\n

In Part I of this series<\/a>, we discussed how early analytical decisions\u2014including quality control, cell segmentation, and cell-type annotation\u2014establish the foundation for reliable spatial transcriptomics analysis. Even with these steps carefully optimized, important challenges remain during biological interpretation. In this article, we focus on two major pitfalls in downstream analysis: treating differential expression results as biological conclusions, and treating spatial associations or computational predictions as biological proof.<\/p>\n\n\n\n

Mistake 4: Treating Gene Lists as Biological Conclusions<\/strong><\/h2>\n\n\n\n

Differential expression (DE) analysis is one of the most common ways to interpret spatial transcriptomics data. However, not all DE analyses ask the same biological question.<\/p>\n\n\n\n

One common use is marker identification<\/strong>: finding genes that distinguish one cell population or cluster from others. This can help characterize clusters or support cell-type annotation.<\/p>\n\n\n\n

A different question is condition-level differential expression: asking whether the same cell population differs between biological conditions\u2014for example, whether fibroblasts in tumor tissue differ from fibroblasts in normal tissue, or whether a cell population changes after treatment. Here, the goal is to determine whether expression differences are consistent across biological samples.<\/p>\n\n\n\n

These two analyses should not be interpreted in the same way. A ranked list of marker genes can help describe a cell population, but it does not by itself demonstrate that a gene is consistently different between biological conditions.<\/p>\n\n\n\n

Why It Matters<\/strong><\/h3>\n\n\n\n

For marker identification, genes are typically ranked according to their enrichment in one cell population or cluster relative to others. This can reveal characteristic markers and help define cellular populations.<\/p>\n\n\n\n

However, when comparing biological conditions, treating individual cells as independent replicates can lead to pseudoreplication, because many cells originate from the same biological sample. Thousands of cells from one patient, for example, do not represent thousands of independent biological replicates.<\/p>\n\n\n\n

For multi-sample comparisons, one common approach is pseudobulk analysis, in which expression counts are aggregated for a defined cell population within each biological sample before comparing conditions. Other statistical models can also account for the hierarchical structure of cells nested within biological samples.<\/p>\n\n\n\n

The key question is therefore not simply: \u201cWhich genes are different?<\/strong>\u201d but \u201cWhat biological question am I asking, and what is the appropriate unit of comparison?<\/strong>\u201d<\/p>\n\n\n\n

From Marker Genes to Biological Interpretation<\/strong><\/h3>\n\n\n\n

Even when a DE analysis is statistically appropriate, interpretation should not stop at the resulting gene list.<\/p>\n\n\n\n

A single highly ranked gene may highlight one aspect of a cellular state without explaining the broader biological process. Examining the spatial distribution of important genes and evaluating coordinated changes across pathways or gene sets can provide additional context.<\/p>\n\n\n\n

For example, several genes involved in extracellular matrix organization, inflammatory signaling, or cell\u2013cell interactions may change together. Pathway-level analysis can therefore help move from individual gene differences toward a broader interpretation of the biological processes associated with the observed changes.<\/p>\n\n\n\n

Real-World Example<\/strong><\/h3>\n\n\n\n

A useful workflow is to move through several levels of evidence:<\/p>\n\n\n\n

Cell population \u2192 Differential expression \u2192 Spatial localization \u2192 Pathway interpretation<\/strong><\/p>\n\n\n\n

For example, in the colon Visium HD dataset introduced in Part I, two CD4\u207a T-cell populations were selected from distinct spatial regions of the tissue (Figure 1A). CD4 and other T-cell markers supported their cell-type identity, while their spatial locations indicated that they occupied different tissue environments. <\/p>\n\n\n\n

Differential expression analysis between the two T-cell populations identified a number of significantly different genes (Figure 1B). However, stopping at the gene level can make biological interpretation difficult. For example, CD48, one of the highly ranked genes, showed higher expression in one region than the other. CD48 is expressed across multiple hematopoietic cell types and has been implicated in immune-cell activation and tumor-associated immune regulation 1,2<\/sup>. Its higher expression therefore does not, by itself, establish a specific functional state of these CD4\u207a T cells.<\/p>\n\n\n\n

Rather than interpreting individual genes in isolation, pathway-level analysis provides a broader view of the biological programs that distinguish the two spatial populations<\/strong> (Figure 1C). The enrichment profiles revealed differences in pathways associated with epithelial\u2013mesenchymal transition, IL6\u2013JAK\u2013STAT3 signaling, TNF-\u03b1 signaling, hypoxia, and other cellular processes<\/em>. Together with their distinct spatial locations, these patterns suggest that the two CD4\u207a T-cell populations are associated with different tissue microenvironments and may exhibit differences in their molecular states.<\/p>\n\n\n\n

This illustrates an important principle: a DEG list tells you which genes differ, but pathway analysis can help explain what those differences may represent biologically<\/strong>. Spatial localization adds another layer of evidence by showing where those molecular programs occur within the tissue.<\/p>\n\n\n\n

The goal is therefore not to replace gene-level analysis with pathway analysis, but to integrate both levels of evidence<\/strong>. Individual genes can highlight interesting candidates, while coordinated changes across gene sets and their spatial distribution provide stronger support for biological interpretation.<\/p>\n\n\n\n

How to Catch It<\/strong><\/h3>\n\n\n\n