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Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder.

Code ↔ Paper

8 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 8 matches
  1. [1] § Methods › Bulk RNA sequencing quality control and normalisation ↔ GTEx_QC_2.R, lines 1–47 · score 0.99 · amyotrophic lateral sclerosis, positive blood cultures, systemic lupus, unexplained seizures, multiple sclerosis, Parkinson
  2. [2] § Methods › Differential abundance and gene expression testing with miloR ↔ Milo_analysis.R, lines 49–118 · score 0.79 · log fold change, findNhoodGroupMarkers, spatial FDR, log normalised, Milo, clustered
  3. [3] § Methods › Network analysis ↔ Network_analysis.R, lines 19–84 · score 0.76 · Edge thickness, adjacency matrix, correlation matrix, nodes, weight, rows
  4. [4] § Methods › Internal and external validation ↔ CMC_QC.R, lines 1–73 · score 0.72 · PMI_hrs, TMM, genes expressed, CPM, Outliers, connectivity
  5. [5] § Methods › Spatial analysis of module scores ↔ Vizium_analysis.R, lines 1–70 · score 0.68 · SCTransform, AddModuleScore, Br2720, matrix, clusters, genes
  6. [6] § Results › The FADS1positive network can define astrocyte states altered in MDD ↔ Milo_analysis.R, lines 49–118 · score 0.60 · log fold change, logFC, MDD depleted, nhood, slices, Milo
  7. [7] § Results › The FADS1positive network can define astrocyte states altered in MDD ↔ Vizium_analysis.R, lines 73–151 · score 0.55 · excitatory neurons, module scores, endothelial, oligodendrocyte, correlated, cell
  8. [8] § Methods › Sparse partial least squares (sPLS) ↔ FADS1_clustering.R, lines 1–80 · score 0.51 · Brain Frontal Cortex, variable, BA9, FADS1, FDR, network

Paper

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The authors' code

R · 202 lines · 6.7 KB · CC-BY-4.0 · 2 matches

  1. library(Seurat)
  2. library(miloR)
  3. library(SingleCellExperiment)
  4. library(scater)
  5. library(scran)
  6. library(dplyr)
  7. library(patchwork)
  9. load("Astro_clusters.RData")
  11. counts <- Astro_sub@assays$RNA
  12. subset_FADS1@assays$RNA <- counts
  14. subset_fad <- subset_FADS1
  15. DefaultAssay(subset_fad) <- 'RNA'
  17. subset_fad <- as.SingleCellExperiment(subset_fad)
  19. reducedDim(subset_fad, "PCA", withDimnames=TRUE) <- subset_FADS1[['pca']]@cell.embeddings
  20. reducedDim(subset_fad, "UMAP", withDimnames=TRUE) <- subset_FADS1[['umap']]@cell.embeddings
  23. subset_fad <- miloR::Milo(subset_fad)
  26. subset_fad <- buildGraph(subset_fad, k = 20, d = 20)
  27. subset_fad <- makeNhoods(subset_fad, prop = 0.2, k = 20, d=20, refined = TRUE)
  29. plotNhoodSizeHist(subset_fad)
  31. subset_fad <- countCells(subset_fad, meta.data = data.frame(colData(subset_fad)), sample="Sample")
  34. astro_design <- data.frame(colData(subset_fad))[,c("Sample", "Age", "Batch", "Condition")]
  36. astro_design$Batch <- as.factor(astro_design$Batch)
  37. astro_design <- distinct(astro_design)
  38. rownames(astro_design) <- astro_design$Sample
  40. subset_fad <- calcNhoodDistance(subset_fad, d=30)
  42. da_results <- testNhoods(subset_fad, design = ~ Batch + Age + Condition, design.df = astro_design)
  44. subset_fad <- buildNhoodGraph(subset_fad)
  46. plotNhoodGraphDA(subset_fad, da_results, layout="UMAP",alpha=0.1)
  49. #-----------------------------------------------
  50. da_results <- annotateNhoods(subset_fad, da_results, coldata_col = "seurat_clusters")
  51. head(da_results)
  53. Bswarm_nagy <- plotDAbeeswarm(da_results, group.by = "seurat_clusters") +
  54. xlab("") +ylab("Log fold change") +
  55. theme(panel.grid = element_blank())
  59. #----------------------------------------------------
  60. #---------------------- DEGs ------------------------
  61. #-----------------------------------------------------
  62. subset_fad <- logNormCounts(subset_fad)
  64. da_results$NhoodGroup <- as.numeric(da_results$SpatialFDR < 0.1 & da_results$logFC < 0)
  66. keep.rows <- rowSums(logcounts(subset_fad)) != 0
  67. subset_fad <- subset_fad[keep.rows, ]
  69. da_nhood_markers <- findNhoodGroupMarkers(subset_fad, da_results, subset.row = rownames(subset_fad))
  71. options(ggrepel.max.overlaps = 'Inf')
  73. top <- da_nhood_markers[da_nhood_markers$logFC_1 > 0,]
  74. top <- slice_min(top, n=10, adj.P.Val_1)
  76. bottom <- da_nhood_markers[da_nhood_markers$logFC_1 < 0,]
  77. bottom <- slice_min(bottom, n=10, adj.P.Val_1)
  79. label_me <- c(top$GeneID, bottom$GeneID)
  81. library(EnhancedVolcano)
  83. keyvals <- ifelse(
  84. da_nhood_markers$logFC_1 < 0 & da_nhood_markers$adj.P.Val_1 < 0.05, 'firebrick',
  85. ifelse(da_nhood_markers$logFC_1 > 0 & da_nhood_markers$adj.P.Val_1 < 0.05, 'forestgreen',
  86. 'black'))
  87. keyvals[is.na(keyvals)] <- 'black'
  88. names(keyvals)[keyvals == 'firebrick'] <- 'high'
  89. names(keyvals)[keyvals == 'black'] <- 'mid'
  90. names(keyvals)[keyvals == 'forestgreen'] <- 'low'
  92. Volcano <- EnhancedVolcano(da_nhood_markers,
  93. lab = da_nhood_markers$GeneID,
  94. x = 'logFC_1',
  95. y = 'adj.P.Val_1',
  96. colAlpha = 1,
  97. title = '',
  98. subtitle = "",
  99. labSize = 4.0,
  100. selectLab = label_me,
  101. drawConnectors = TRUE,
  102. arrowheads = FALSE,
  103. FCcutoff = 0,
  104. colCustom = keyvals,
  105. ylab = bquote(~-log[10]~ '(Adj P-Value)'),
  106. xlab = bquote(~log[2]~ 'fold change'),
  107. cutoffLineType = 'blank',
  108. cutoffLineCol = 'black',
  109. gridlines.major = FALSE,
  110. gridlines.minor = FALSE) +
  111. scale_x_continuous(limits = c(-2,2)) +
  112. scale_y_continuous(limits = c(0,260)) +
  113. theme(plot.title = element_text(hjust = 0.5),
  114. legend.position = 'none') +
  115. annotate("text", x = -1, y = 250, size = 14/.pt, label = "Enriched in \n other nhoods",
  116. colour = "firebrick")+
  117. annotate("text", x = 1, y = 250, size = 14/.pt,label = "Enriched in \n MDD depleted nhoods",
  118. colour = "forestgreen")
  121. sig <- da_nhood_markers[da_nhood_markers$adj.P.Val_1 < 0.05,]
  124. Upreg <- sig[sig$logFC_1 > 0.25,]%>%
  125. dplyr::select(GeneID)
  127. background <- da_nhood_markers$GeneID
  129. Upreg_GO <- enrichGO(gene = Upreg$GeneID,
  130. OrgDb = "org.Hs.eg.db",
  131. keyType = 'SYMBOL',
  132. ont = "ALL",
  133. pAdjustMethod = "BH",
  134. pvalueCutoff = 0.05,
  135. qvalueCutoff = 1,
  136. universe = background)
  138. Upreg_GO_res <- Upreg_GO@result
  140. Upreg_GO_res <- Upreg_GO_res[with(Upreg_GO_res,order(p.adjust)),]
  141. Upreg_GO_res <- Upreg_GO_res[1:10,]
  142. Upreg_GO_res$Cluster <- 'Enriched in MDD depleted nhoods'
  145. Downreg <- sig[sig$logFC_1 < -0.25,]%>%
  146. dplyr::select(GeneID)
  147. Downreg_GO <- enrichGO(gene = Downreg$GeneID,
  148. OrgDb = "org.Hs.eg.db",
  149. keyType = 'SYMBOL',
  150. ont = "ALL",
  151. pAdjustMethod = "BH",
  152. pvalueCutoff = 0.05,
  153. qvalueCutoff = 1,
  154. universe = background)
  156. Downreg_GO_res <- Downreg_GO@result
  158. Downreg_GO_res <- Downreg_GO_res[with(Downreg_GO_res,order(p.adjust)),]
  159. Downreg_GO_res <- Downreg_GO_res[1:10,]
  160. Downreg_GO_res$Cluster <- 'Enriched in other nhoods'
  163. # Capatilize the first letter of each GO term
  164. Capitalize <- function(x) {
  165. substr(x, 1, 1) <- toupper(substr(x, 1, 1))
  166. x
  167. }
  169. Total <- rbind(Upreg_GO_res, Downreg_GO_res)
  170. Total$Description <- Capitalize(Total$Description)
  171. Total$logPvalue <- -log10(Total$p.adjust)
  174. reorder_within <- function(x, by, within, fun = mean, sep = "___", ...) {
  175. new_x <- paste(x, within, sep = sep)
  176. stats::reorder(new_x, by, FUN = fun)
  177. }
  179. scale_x_reordered <- function(..., sep = "___") {
  180. reg <- paste0(sep, ".+$")
  181. ggplot2::scale_x_discrete(labels = function(x) gsub(reg, "", x), ...)
  182. }
  184. DEG_GO <- ggplot(Total, aes(reorder_within(Description, logPvalue, Cluster), logPvalue, fill = Cluster)) +
  185. geom_segment(aes(xend = reorder_within(Description, logPvalue, Cluster), yend = 0)
  186. ) +
  187. geom_point(size = 5, aes(colour = Cluster)) +
  188. coord_flip() +
  189. theme_bw() +
  190. scale_x_reordered() +
  191. facet_wrap(facets = "Cluster", scales = "free") +
  192. theme(panel.grid = element_blank(),
  193. plot.title = element_text(hjust = 0.5),
  194. strip.background = element_blank(),
  195. legend.position = 'none',
  196. text = element_text(size = 16)) +
  197. scale_color_manual(values=c("forestgreen","firebrick"))+
  198. geom_hline(yintercept=1.3, linetype="dashed", color = "grey44") +
  199. xlab("") +
  200. ylab(expression(paste("-log"[10]," (adj P-value)")))

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

Overview

Authors: Eamon Fitzgerald1,2,3, Nicholas O’Toole1,2, Irina Pokhvisneva1,2, Eric J. Nestler4, Gustavo Turecki1, Corina Nagy1, Michael J. Meaney1,2,5,6,7
ORCID iDs: Eamon Fitzgerald, Eric J. Nestler, Gustavo Turecki, Corina Nagy, Michael J. Meaney
  1. Department of Psychiatry, Douglas Mental Health University Institute, McGill University,Montréal, QC Canada
  2. Ludmer Centre for Neuroinformatics and Mental Health, McGill University,Montréal, QC Canada
  3. Cervolve, Montréal, QC Canada
  4. Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai,New York, NY USA
  5. Translational Neuroscience Program, Singapore Institute for Clinical Sciences,Centros, Singapore
  6. Yong Loo Lin School of Medicine, National University of Singapore,Centros, Singapore
  7. Brain–Body Initiative, Agency for Science, Technology & Research (A*STAR),Centros, Singapore
Institutions: McGill University (Canada); Icahn School of Medicine at Mount Sinai (United States); Singapore Institute for Clinical Sciences (Singapore); National University of Singapore (Singapore); Agency for Science, Technology and Research (Singapore)
Journal: Nature communications, volume 17, issue 1, article 4985
Dates: received 7 August 2024; accepted 24 March 2026; published online 8 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71542-5 · PMID 41951638 · PMCID PMC13237068 · OpenAlex W7151999713
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), depression (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity
Keywords: Neuroscience, Depression, Computational biology and bioinformatics, Drug development, Genetics
MeSH: Astrocytes*, Fatty Acid Desaturases*, Fatty Acids*, Major Depressive Disorder*, Delta-5 Fatty Acid Desaturase, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Oligodendroglia, Polymorphism, Single Nucleotide, PPAR alpha, Prefrontal Cortex (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (R01 MH129306)
Citations: not cited yet (Europe PMC); 114 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

Zenodo 10181579

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (14), Shell (2)
Size: 19 files, 16 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (12 files), edgeR (4 files), Seurat (4 files), data.table (3 files), ggplot2 (3 files), nlme (2 files), patchwork (2 files), cowplot (1 file), igraph (1 file), limma (1 file), lme4 (1 file), reshape2 (1 file), SingleCellExperiment (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
16 files
At the source: zenodo.org/records/10181579

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Read it in the paper: doi.org/10.1038/s41467-026-71542-5.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 16 scripts, each with its path and the digest of its content;
  • 8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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/s41467-026-71542-5.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 13 MeSH terms, 1 funder, 108 references.

Cite

This paper

Fitzgerald, E., O’Toole, N., Pokhvisneva, I., Nestler, E. J., Turecki, G., Nagy, C., & Meaney, M. J. (2026). Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder. Nature communications, 17(1), 4985. https://doi.org/10.1038/s41467-026-71542-5

BibTeX

@article{fitzgerald2026astrocyte,
author = {Fitzgerald, Eamon and O’Toole, Nicholas and Pokhvisneva, Irina and Nestler, Eric J. and Turecki, Gustavo and Nagy, Corina and Meaney, Michael J.},
title = {{Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {4985},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71542-5},
url = {https://doi.org/10.1038/s41467-026-71542-5},
pmid = {41951638},
pmcid = {PMC13237068}
}

RIS

TY - JOUR
AU - Fitzgerald, Eamon
AU - O’Toole, Nicholas
AU - Pokhvisneva, Irina
AU - Nestler, Eric J.
AU - Turecki, Gustavo
AU - Nagy, Corina
AU - Meaney, Michael J.
TI - Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/08
VL - 17
IS - 1
SP - 4985
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71542-5
UR - https://doi.org/10.1038/s41467-026-71542-5
LA - en
ER -

CSL-JSON

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