smiDE post

posts/smiDE/index.qmd

@@ -0,0 +1,43 @@
+---
+title: "smiDE: an open-source package for differential expression analysis with spatially correlated data "
+author:
+  - name: Dan McGuire
+    orcid: 0009-0006-0286-9625
+    affiliations:
+      - ref: nstg
+      - ref: dan11mcguire
+date: "2024-11-05"
+date-modified: "2024-11-05"
+categories: [differential expression, DE]
+draft: false
+#image: figures/insitucor-tile.png
+---
+
+```{r}
+#| echo: false
+
+## xx ## #| eval: true
+## xx ## 
+## xx ## knitr::include_graphics("./figures/insitucor-banner.png")
+```
+
+# Background 
+
+
+Differential expression is a key application of imaging-based spatial transcriptomics analysis. In the preprint ["Differential Expression Analysis for Spatially Correlated Data"](https://www.biorxiv.org/content/10.1101/2024.08.02.606405v1.full){target="_blank"} [@Vasconcelos2024], the authors discuss a number of challenges to differential expression analyses with spatial data, namely:
+
+* segmentation errors may cause bias in fold-change estimates and lead to incorrect inferences, and 
+* correlation among neighboring cells leads standard models to inflate statistical significance.
+
+We find that ignoring these issues can result in considerable false discoveries that greatly outnumber true findings.
+Benchmarking various solutions to address these problems, we motivate a suite of solutions and implement them in the R package [smiDE](https://github.com/Nanostring-Biostats/smiDE){target="_blank"}.
+
+
+
+
+
+
+# Resources
+
+- [Our preprint](https://www.biorxiv.org/content/10.1101/2024.08.02.606405v1.full){target="_blank"}
+- [The smiDE R package](https://github.com/Nanostring-Biostats/smiDE){target="_blank"}

references.bib

@@ -124,3 +124,16 @@ @article {Maher2023
 	eprint = {https://www.biorxiv.org/content/early/2023/07/02/2023.06.30.547258.full.pdf},
 	journal = {bioRxiv}
 }
+@article {Vasconcelos2024,
+	author = {Vasconcelos, Ana Gabriela and McGuire, Daniel and Simon, Noah and Danaher, Patrick and Shojaie, Ali},
+	title = {Differential Expression Analysis for Spatially Correlated Data},
+	elocation-id = {2024.08.02.606405},
+	year = {2024},
+	doi = {10.1101/2024.08.02.606405},
+	publisher = {Cold Spring Harbor Laboratory},
+	abstract = {Differential expression is a key application of imaging spatial transcriptomics, moving analysis beyond cell type localization to examining cell state responses to microenvironments. However, spatial data poses new challenges to differential expression: segmentation errors cause bias in fold-change estimates, and correlation among neighboring cells leads standard models to inflate statistical significance. We find that ignoring these issues can result in considerable false discoveries that greatly outnumber true findings. We present a suite of solutions to these fundamental challenges, and implement them in the R package smiDE.Competing Interest StatementThe authors have declared no competing interest.},
+	URL = {https://www.biorxiv.org/content/early/2024/08/06/2024.08.02.606405},
+	eprint = {https://www.biorxiv.org/content/early/2024/08/06/2024.08.02.606405.full.pdf},
+	journal = {bioRxiv}
+}
+