OSCR

Dual platform spatial transcriptomics reveals parvalbumin interneuron subtype vulnerability in mouse models of Alzheimer's disease.

Code ↔ Paper

13 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 13 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › GeoMx DSP data preprocessing ↔ 01_GeoMx_data_preprocessing.R, lines 355–414 · score 0.83 · variance stabilizing transformation, upper quartile, DESeq2, VST, LOQ, preprocessing
  2. [2] § Methods › Cell type inference using the NMF basis matrices as a reference ↔ 29_xeniumDeconvolution.R, the whole file · a weak match · score 0.79 · insitutypeML, control probes, selected ranks, deconvolution, rows, NMF
  3. [3] § Methods › Nanostring GeoMx DSP data deconvolution ↔ R/utils.R, lines 121–156 · score 0.76 · EnvStats, variable genes, CV, expression profile, variation, SD
  4. [4] § Results › Biological modules showed mutual exclusivity between TG and WT ↔ 00_settings.R, lines 1–71 · score 0.72 · WT1F, TG3F, WT2F, TG4F, coefficient, nodes
  5. [5] § Results › Biological modules showed mutual exclusivity between TG and WT ↔ vignettes/SOTK.Rmd, lines 161–228 · score 0.70 · WT1F, TG3F, WT2F, TG4F, correlation network, nodes
  6. [6] § Methods › Xenium in situ data preprocessing and ROI selection ↔ 21_subsettingROI.R, lines 55–110 · score 0.67 · SingleCellExperiment, cell IDs, subsetted, ROI, selection
  7. [7] § Results › Deconvolution and correlation networks revealed meaningful biological modules ↔ 00_settings.R, lines 1–71 · score 0.65 · WT2F, TG4F, correlation coefficients, NeuN, edges, weighted
  8. [8] § Methods › Select the optimal rank from the deconvolution ↔ R/class-SOTK.R, lines 24–101 · score 0.64 · fast greedy, correlation network, SOTK, igraph, coefficient, nodes
  9. [9] § Methods › Unsupervised clustering and nonlinear dimensionality reduction ↔ 27_MDR_RSC.R, the whole file · a weak match · score 0.64 · distributed Stochastic Neighbor, Rtsne, distance, Embedding, clustering, Xenium
  10. [10] § Methods › Unsupervised clustering and nonlinear dimensionality reduction ↔ 28_MDR_all.R, the whole file · a weak match · score 0.62 · distributed Stochastic Neighbor, Rtsne, distance, Embedding, Xenium
  11. [11] § Results › Deconvolution and correlation networks revealed meaningful biological modules ↔ R/class-SOSet.R, lines 17–155 · score 0.59 · correlation coefficients, gene expression, concatenated, deconvolve, vector, Spearman
  12. [12] § Methods › Comparison of the level of expression between two spatial transcriptomics platforms ↔ R/class-SOSet.R, lines 17–155 · score 0.58 · DSP WTA, correlation coefficients, vectors, Spearman, seq, Transcriptomics
  13. [13] § Methods › Xenium in situ data preprocessing and ROI selection ↔ 02_Xenium_data_preprocessing.R, lines 42–107 · score 0.58 · HDF5Array, unassigned, arrow, parquet, Tx, preprocessing

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 172 lines · 5.3 KB · CC-BY-4.0 · 2 matches

  1. ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
  2. # GeoMx parameters
  3. corMet = "spearman"
  4. corrCoefThre <- 0.5 # correlation coefficient threshold values to include
  5. seed <- 1118
  6. niter <- 1000 # graph layout iteration
  7. commWeight <- 100 # graph layout weights on community info
  8. cohortWeight <- 1 # graph layout weights on cohort info
  10. samLblSize <- "1" # sample (ROIs) label size
  11. metageneNodeSize <- "10"
  12. metageneLblSize <- "1.5" # metagene label size
  14. figSize <- 10 # width and height
  15. vertexSize <- 5
  16. vertexLabelCex <- 1
  17. edgeAlpha <- 0.9
  19. ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
  20. # Xenium parameters
  21. wilcoxonDegFcThre <- 0.5
  22. wilcoxonDegPvalThre <- 0.05
  24. ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
  25. # Color key
  26. groupCols <- c(
  27. "TG" = "#7FFF00", # transgenic mice
  28. "WT" = "#FF931E" # wild-type
  29. )
  31. segmentCols <- c(
  32. "PV" = "#7570B3",
  33. "NeuN" = "#1B9E77"
  34. # "Amyloid" = "#E7298A",
  35. # "TN" = "#D4AF37"
  36. )
  37. segments <- c("PV", "NeuN")
  39. modelCols <- list(
  40. "GeoMx" = c(
  41. "TG1F" = "#084594",
  42. "TG2F" = "#2171B5",
  43. "TG3F" = "#4292C6",
  44. "TG4F" = "#6BAED6",
  45. "WT1F" = "#99000D",
  46. "WT2F" = "#CB181D",
  47. "WT3F" = "#EF3B2C"
  48. ),
  49. "Xenium" = c(
  50. "TG2F" = "#2171B5",
  51. "TG3F" = "#4292C6",
  52. "TG4F" = "#6BAED6",
  53. "WT1F" = "#99000D",
  54. "WT2F" = "#CB181D",
  55. "WT3F" = "#EF3B2C"
  56. )
  57. )
  59. sectionCols <- c(
  60. "RSC" = "#1B9E77",
  61. "SUB" = "#D95F02",
  62. "VIS" = "#7570B3",
  63. "ENT" = "#E7298A",
  64. "CA1" = "#FFBF00"
  65. )
  67. kmeansCols <- c("#7FC97F", "#BEAED4", "#FDC086", "#FFFF99", "#386CB0", "#F0027F", "#BF5B17")
  68. names(kmeansCols) <- c(1:7)
  70. metagenesCols <- c("#7570b3", "#9c98c8", "#c3c1de", "#1b9e77", "#5cb99d", "#7cc7b1", "#bde3d8")
  71. names(metagenesCols) <- c("PV_M1", "PV_M2", "PV_M3", "NeuN_M1", "NeuN_M2", "NeuN_M3", "NeuN_M4")
  74. ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
  75. # User defined functions
  76. .geoMean <- function(x) exp(mean(log(x)))
  78. .listIntersection <- function(listInput, sort = TRUE) {
  79. listInputmat <- fromList(listInput) == 1
  80. listInputunique <- unique(listInputmat)
  81. grouplist <- list()
  82. for (i in 1:nrow(listInputunique)) {
  83. currentRow <- listInputunique[i, ]
  84. myelements <- which(apply(listInputmat, 1, function(x) all(x == currentRow)))
  85. attr(myelements, "groups") <- currentRow
  86. grouplist[[paste(colnames(listInputunique)[currentRow], collapse = ":")]] <- myelements
  87. myelements
  88. }
  89. if (sort) {
  90. grouplist <- grouplist[order(sapply(grouplist, function(x) length(x)), decreasing = TRUE)]
  91. }
  92. attr(grouplist, "elements") <- unique(unlist(listInput))
  93. return(grouplist)
  94. }
  96. .ttest <- function(dat, idx1, idx2){
  97. obj <- try(t.test(dat[idx1], dat[idx2], var.equal=FALSE), silent=TRUE)
  98. if(is(obj, "try-error")) {
  99. value <- NA
  100. }else{
  101. value <- obj$p.value
  102. }
  103. return(value)
  104. }
  106. .log2fc <- function(dat, idx1, idx2){
  107. fc <- log2(mean(dat[idx1]) / mean(dat[idx2]))
  108. return(fc)
  109. }
  111. .reformP <- function(p) {
  112. if(!is.na(p)) {
  113. if (p < 0.0001) {
  114. pString <- formatC(p, format = "e", digits = 2)
  115. } else {
  116. pString <- format(round(p, 4), nsmall = 4)
  117. }
  118. } else {
  119. pString <- "NA"
  120. }
  121. return(pString)
  122. }
  124. .getModelStats <- function(x) {
  125. TG1F <- 0; TG2F <- 0; TG3F <- 0; TG4F <- 0;
  126. WT1F <- 0; WT2F <- 0; WT3F <- 0
  128. if (!is.null(x)) {
  129. for (i in x) {
  130. buff <- unlist(stringr::str_split(i, "-"))
  131. if (buff[1] == "TG1F") {
  132. TG1F <- TG1F + 1
  133. } else if (buff[1] == "TG2F") {
  134. TG2F <- TG2F + 1
  135. } else if (buff[1] == "TG3F") {
  136. TG3F <- TG3F + 1
  137. } else if (buff[1] == "TG4F") {
  138. TG4F <- TG4F + 1
  139. } else if (buff[1] == "WT1F") {
  140. WT1F <- WT1F + 1
  141. } else if (buff[1] == "WT2F") {
  142. WT2F <- WT2F + 1
  143. } else if (buff[1] == "WT3F") {
  144. WT3F <- WT3F + 1
  145. } else {
  146. message(paste0("WARNING::", buff[1], " is uncategorized."))
  147. }
  148. }
  149. }
  150. return(c(TG1F, TG2F, TG3F, TG4F,
  151. WT1F, WT2F, WT3F))
  152. }
  154. .assignShape <- function(x) {
  155. group <- substring(x, 1, 1)
  156. if (group == "T") {
  157. shape <- "triangle" # transgenic
  158. } else if (group == "W") {
  159. shape <- "circle" # wild-type
  160. }
  161. return(shape)
  162. }
  164. .assignShapeSize <- function(x) {
  165. group <- substring(x, 1, 1)
  166. if (group == "T") {
  167. size <- "9" # triangle
  168. } else if (group == "W") {
  169. size <- "7" # circle
  170. }
  171. return(size)
  172. }

00_settings.R, under CC-BY-4.0 · at the source

Overview

Authors: Heewon Seo1, Dylan J. Terstege2,3, Yi Ren2,3, Shiying Liu1, Kimberly-Ann Ruth Goring1, Bo Young Ahn1, Jonathan R. Epp2,3
ORCID iDs: Heewon Seo, Dylan J. Terstege, Yi Ren, Jonathan R. Epp
  1. Applied Spatial Omics Centre, Cumming School of Medicine, University of Calgary,Calgary, AB Canada
  2. Department of Cell Biology and Anatomy, Cumming School of Medicine, University of Calgary,Calgary, AB Canada
  3. Hotchkiss Brain Institute, University of Calgary,Calgary, AB Canada
Institutions: University of Calgary (Canada)
Journal: Nature communications, volume 17, issue 1, article 6668
Dates: received 13 March 2025; accepted 13 May 2026; published online 20 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73474-6 · PMID 42161970 · PMCID PMC13381950 · OpenAlex W7161791098
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging
Keywords: Molecular neuroscience, Transcriptomics, Alzheimer's disease
MeSH: Alzheimer Disease*, Interneurons*, Parvalbumins*, Animals, Disease Models, Animal, Female, Gene Expression Profiling, Glutamate Decarboxylase, Mice, Mice, Transgenic, Spatial Transcriptomics, Transcriptome (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: Canadian Institutes of Health Research (190215); Natural Sciences and Engineering Research Council of Canada; Société Alzheimer | Alzheimer Society Research Program (ASRP); Alzheimer's Society of Canada Research program Fellowship
Citations: cited by 3 papers (Europe PMC); 68 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 13 matches between paragraphs and lines of code.

bioinformatics.ucalgary.ca/publications/mm-in-pvin

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “GeoMx DSP data preprocessing”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: bioinformatics.ucalgary.ca/publications/MM-in-PVIN/GeoMx/

UC-ASOC/SOTK

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 947c7274fd5330a50031df5406476de2dbe5612f, 18 December 2025
Languages: R (7)
Size: 27 files, 7 scripts
Software Heritage: not archived
Found in: the text, “Select the optimal rank from the deconvolution”
Holds: README, license file, environment (DESCRIPTION), tests, documentation, 1 notebook
Not found: CITATION.cff, continuous integration
Tools: tidyverse (4 files), igraph (3 files), ggplot2 (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
9 files
At the source: github.com/UC-ASOC/SOTK

Zenodo 17834664

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (25)
Size: 30 files, 25 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (17 files), ggplot2 (9 files), SingleCellExperiment (7 files), igraph (3 files), reshape2 (3 files), UMAP (2 files), circlize (1 file), ComplexHeatmap (1 file), DESeq2 (1 file), ggpubr (1 file), Plotly (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
25 files
At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: Zenodo 17834664

Read it in the paper: doi.org/10.1038/s41467-026-73474-6.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 32 scripts, each with its path and the digest of its content;
  • 13 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-73474-6.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 12 MeSH terms, 4 funders, 62 references.

Cite

This paper

Seo, H., Terstege, D. J., Ren, Y., Liu, S., Goring, K.-A. R., Ahn, B. Y., & Epp, J. R. (2026). Dual platform spatial transcriptomics reveals parvalbumin interneuron subtype vulnerability in mouse models of Alzheimer's disease. Nature communications, 17(1), 6668. https://doi.org/10.1038/s41467-026-73474-6

BibTeX

@article{seo2026dual,
author = {Seo, Heewon and Terstege, Dylan J. and Ren, Yi and Liu, Shiying and Goring, Kimberly-Ann Ruth and Ahn, Bo Young and Epp, Jonathan R.},
title = {{Dual platform spatial transcriptomics reveals parvalbumin interneuron subtype vulnerability in mouse models of Alzheimer's disease}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6668},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73474-6},
url = {https://doi.org/10.1038/s41467-026-73474-6},
pmid = {42161970},
pmcid = {PMC13381950}
}

RIS

TY - JOUR
AU - Seo, Heewon
AU - Terstege, Dylan J.
AU - Ren, Yi
AU - Liu, Shiying
AU - Goring, Kimberly-Ann Ruth
AU - Ahn, Bo Young
AU - Epp, Jonathan R.
TI - Dual platform spatial transcriptomics reveals parvalbumin interneuron subtype vulnerability in mouse models of Alzheimer's disease
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/20
VL - 17
IS - 1
SP - 6668
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73474-6
UR - https://doi.org/10.1038/s41467-026-73474-6
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-73474-6",
"type": "article-journal",
"title": "Dual platform spatial transcriptomics reveals parvalbumin interneuron subtype vulnerability in mouse models of Alzheimer's disease",
"container-title": "Nature communications",
"author": [
{
"family": "Seo",
"given": "Heewon"
},
{
"family": "Terstege",
"given": "Dylan J."
},
{
"family": "Ren",
"given": "Yi"
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{
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{
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}
],
"container-title-short": "Nat Commun",
"volume": "17",
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"page": "6668",
"DOI": "10.1038/s41467-026-73474-6",
"PMID": "42161970",
"PMCID": "PMC13381950",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73474-6",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
20
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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