OSCR

Cellular transcriptomic signatures underpinning the heterogeneity of depression in Alzheimer's disease.

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 › Comparative analysis and cell–cell communication analysis ↔ 17.cellchat.Normal.DEP.R, lines 265–310 · score 0.62 · interaction strengths, signaling pathways, CellChat, networks, cells
  2. [2] § METHODS › Comparative analysis and cell–cell communication analysis ↔ cellchat.MDD.Normal.R, lines 217–262 · score 0.62 · interaction strengths, signaling pathways, CellChat, networks, cells
  3. [3] § METHODS › Differential expression analysis ↔ 13.Normal.DEP.cell_type_DE_analysis.R, lines 89–135 · score 0.59 · post mortem interval, PMI, age, sex, diagnosis, filtered
  4. [4] § METHODS › Differential expression analysis ↔ 12.AD.DEP.cell_type_DE_analysis_adjusted.R, lines 196–234 · score 0.59 · post mortem interval, PMI, age, sex, diagnosis, filtered
  5. [5] § METHODS › Single‐cell RNA sequencing data processing and analysis ↔ 13.Normal.DEP.cell_type_DE_analysis.R, lines 1–87 · score 0.58 · DoubletFinder, removing cells, variables, scores, covariates, diagnosis
  6. [6] § METHODS › Single‐cell RNA sequencing data processing and analysis ↔ 12.AD.DEP.cell_type_DE_analysis_adjusted.R, lines 65–106 · score 0.56 · DoubletFinder, removing cells, variables, scores, covariates, diagnosis
  7. [7] § METHODS › Comparative analysis and cell–cell communication analysis ↔ 15.AD_DEP_vs_Normal_DEP.intersection.mayo.R, lines 84–148 · score 0.53 · Venn diagrams, Common DEGs, intersection, genes, MDD, cell
  8. [8] § METHODS › Comparative analysis and cell–cell communication analysis ↔ 15.AD_DEP_vs_MDD.new.intersection.mayo.R, lines 84–148 · score 0.53 · Venn diagrams, Common DEGs, intersection, genes, MDD, cell

Paper

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The authors' code

R · 285 lines · 10 KB · no license · 2 matches

  1. #srun --mem=200GB --pty bash -i
  2. #conda activate /hpc/group/adrc/zm77/software/zmnebula
  3. #R
  5. .libPaths(c(.libPaths(), '/hpc/group/adrc/zm77/r_packages'))
  6. .libPaths(c(.libPaths(), '/hpc/group/adrc/zm77/software/conda_envs'))
  8. library(Matrix, lib.loc = '/hpc/group/adrc/zm77/r_packages')
  9. library(data.table)
  10. library(ggplot2)
  11. library(Seurat, lib.loc = '/hpc/group/adrc/zm77/r_packages')
  12. library(plyr)
  13. library(dplyr)
  14. library(stringr)
  15. library(nebula)
  16. library(missForest)
  17. library(parallel)
  19. # This can be run as an array job
  20. i <- Sys.getenv('SLURM_ARRAY_TASK_ID') %>% as.numeric()
  21. # For testing, if not an array job, you can set i manually
  22. if (is.na(i)) i <- 1
  24. # Define directories
  25. base_dir <- '/hpc/group/adrc/zm77/LUTZ.MDD/ROSMAP.BIG.10032024/new.analysis.03112025/objects'
  26. out_dir <- file.path(base_dir, 'nebula_results_Normal_DEPR')
  27. dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)
  29. # Load the integrated object with Mayo annotations
  30. setwd(base_dir)
  31. seu <- readRDS('9.integrated_with_mayo_annotations.rds')
  33. # Make sure we have the raw RNA counts
  34. if (!"RNA_raw" %in% names(seu@assays)) {
  35. stop("RNA_raw assay not found in the Seurat object. Please check that the raw counts are available.")
  36. }
  38. clinic <- read.csv("/hpc/group/adrc/zm77/LUTZ.MDD/ROSMAP.BIG.10032024/ROSMAP_depr_clinical.csv")
  39. # Get the metadata from Seurat object
  40. metadata <- [email hidden]
  41. clinic <- clinic %>%
  42. mutate(depression = case_when(
  43. r_depres == 4 ~ "Normal",
  44. r_depres == 1 | r_depres == 2 | r_depres == 3 ~ "DEPR",
  45. TRUE ~ ""
  46. ))
  48. clinic <- clinic %>%
  49. mutate(ad = case_when(
  50. cogdx == 1 ~ "Normal",
  51. cogdx == 4 | cogdx == 5 ~ "LOAD",
  52. TRUE ~ ""
  53. ))
  55. clinic <- clinic [clinic$ad == 'Normal', ]
  56. clinic$diagnosis <- paste0(clinic$ad, '_', clinic$depression)
  57. clinic <- clinic [clinic$diagnosis == 'Normal_DEPR' | clinic$diagnosis == 'Normal_Normal', ]
  59. print ('number of subjects with Normal but without depression:')
  60. print (nrow (clinic[clinic$diagnosis == 'Normal_Normal', ]))
  61. print ('number of subjects with Normal and depression:')
  62. print (nrow (clinic[clinic$diagnosis == 'Normal_DEPR', ]))
  64. metadata <- metadata[metadata$DoubletFinder.score < 0.75, ]
  65. metadata$rowname <- rownames(metadata)
  66. metadata <- left_join(metadata, clinic, by='individualID')
  67. metadata <- as.data.frame(metadata)
  68. rownames(metadata) <- metadata$rowname
  69. metadata <- metadata [complete.cases(metadata$diagnosis), ]
  71. # Remove cells with missing covariates
  72. metadata <- metadata[!is.na(metadata$msex), ]
  73. message("After removing cells with missing covariates: ", nrow(metadata), " cells")
  75. # Create diagnosis variable (0 for Normal without DEP(reference), 1 for Normal with DEP)
  76. metadata$diagnosis <- ifelse(metadata$diagnosis == 'Normal_DEPR', 1, 0)
  78. # Update the Seurat object metadata (keep only filtered cells)
  79. seu <- seu[, rownames(metadata)]
  80. [email hidden] <- metadata
  82. # Get Mayo cell types and count how many cells in each
  83. cell_types <- table(metadata$mayo.cell.type)
  84. cell_types <- names(cell_types[cell_types >= 50]) # Only analyze cell types with at least 50 cells
  86. message("Found ", length(cell_types), " Mayo cell types with at least 50 cells")
  87. print(cell_types)
  89. # Function to run NEBULA analysis for a given cell type
  90. run_nebula_analysis <- function(cell_type) {
  91. message("Processing cell type: ", cell_type)
  93. # Subset metadata for this cell type
  94. meta <- metadata[metadata$mayo.cell.type == cell_type, ]
  96. # If not enough cells of this type, skip
  97. if (nrow(meta) < 50) {
  98. message("Not enough cells for cell type: ", cell_type, " (", nrow(meta), " cells). Skipping.")
  99. return(NULL)
  100. }
  102. # Check if we have both groups
  103. if (length(unique(meta$diagnosis)) < 2) {
  104. message("Only one diagnosis group found for cell type: ", cell_type, ". Skipping.")
  105. return(NULL)
  106. }
  108. # Get raw count data for these cells
  109. data.subset <- seu[["RNA_raw"]]$counts[, rownames(meta)]
  111. # Filter genes with zero expression
  112. genes.use <- rowSums(data.subset) > 0
  113. genes.use <- names(genes.use[genes.use])
  115. message(" - Using ", length(genes.use), " genes with non-zero expression")
  117. # Subset the data to these genes
  118. data.subset <- data.subset[genes.use, ]
  120. # Prepare data for NEBULA
  121. allgenes <- t(as.data.frame(data.subset))
  122. allgenes <- as.data.frame(allgenes)
  124. # Add metadata
  125. ngenes <- ncol(allgenes)
  126. gene_names <- colnames(allgenes) # Store original gene names
  128. allgenes$diagnosis <- meta$diagnosis
  129. allgenes$sampID <- meta$individualID # Using individualID for subject grouping
  130. allgenes$sex <- as.numeric(meta$msex) # msex: 0 = female, 1 = male
  131. allgenes$agec <- meta$agec # Age at death
  132. allgenes$pmi <- meta$pmi # Post-mortem interval
  133. allgenes$nCount_RNA <- meta$nCount_RNA_raw
  134. allgenes$wellKey <- rownames(meta)
  135. rownames(allgenes) <- allgenes$wellKey
  137. # Reorder columns: metadata first, then genes
  138. metadata_cols <- c("diagnosis", "sampID", "sex", "agec", "pmi", "nCount_RNA", "wellKey")
  139. allgenes <- allgenes[, c(metadata_cols, gene_names)]
  141. # Handle missing values in covariates if any
  142. coldata <- allgenes[, 1:7]
  144. # Make sure sample_id is a factor
  145. coldata$sampID <- as.factor(coldata$sampID)
  147. # Filter for genes expressed in at least 10% of cells in either group
  148. message(" - Filtering genes by expression prevalence...")
  150. # Get cells for each condition
  151. load_cells <- as.matrix(t(allgenes[allgenes$diagnosis == 1, 8:ncol(allgenes)]))
  152. normal_cells <- as.matrix(t(allgenes[allgenes$diagnosis == 0, 8:ncol(allgenes)]))
  154. # Skip if one group has no cells
  155. if (ncol(load_cells) == 0 || ncol(normal_cells) == 0) {
  156. message(" - Not enough cells in one of the groups. Skipping.")
  157. return(NULL)
  158. }
  160. # Calculate percent of cells expressing each gene
  161. PercentAbove <- function(x, threshold) {
  162. return(length(x = x[x > threshold]) / length(x = x))
  163. }
  165. pct.exp.load <- apply(X = load_cells, MARGIN = 1, FUN = PercentAbove, threshold = 0)
  166. pct.exp.normal <- apply(X = normal_cells, MARGIN = 1, FUN = PercentAbove, threshold = 0)
  168. # Filter genes expressed in at least 10% of cells in either group
  169. alpha.min <- pmax(pct.exp.load, pct.exp.normal)
  170. genes.to.keep <- names(which(alpha.min >= 0.1))
  172. message(" - Keeping ", length(genes.to.keep), " genes expressed in at least 10% of cells")
  174. # Subset the data to these genes
  175. genedata <- data.subset[rownames(data.subset) %in% genes.to.keep, ]
  177. # Make sure gene data and cell data are in the same order
  178. genedata <- genedata[, rownames(coldata)]
  180. # Order the data by sample ID
  181. coldata <- coldata[order(coldata$sampID), ]
  183. # Prepare for NEBULA
  184. count <- as.matrix(genedata)
  185. count <- count[, rownames(coldata)]
  187. # Set offset (library size normalization)
  188. offsets <- coldata$nCount_RNA
  189. offsets <- unname(offsets)
  191. # Create subject ID vector
  192. sid <- as.character(coldata$sampID)
  194. # Create design matrix with sex, age, and PMI as covariates
  195. df <- model.matrix(~ sex + diagnosis, data = coldata)
  197. message(" - Running NEBULA with ", nrow(count), " genes and ", ncol(count), " cells")
  199. # Run NEBULA
  200. re <- nebula(count, sid, pred = df, offset = offsets, method = 'HL')
  202. # Process results
  203. result <- re$summary
  205. # Calculate log2FC (LOAD vs Normal)
  206. result$log2FC <- log2(exp(result$logFC_diagnosis))
  208. # Calculate FDR
  209. result$fdr <- p.adjust(result$p_diagnosis, method = 'fdr', n = nrow(result))
  211. # Rename columns
  212. result <- result[, c('gene', 'log2FC', 'p_diagnosis', 'fdr')]
  213. colnames(result) <- c('gene', 'log2FC', 'p_val', 'fdr')
  215. # Sort by FDR
  216. result <- result[order(result$fdr), ]
  218. # Extract significant genes
  219. result_sig <- result[which(result$fdr < 0.05), ]
  221. # Save results
  222. setwd(out_dir)
  223. write.csv(result, paste0('Normal_DEPR_', gsub(" ", "_", cell_type), '_all_genes_NEBULA.csv'), row.names = FALSE)
  225. if (nrow(result_sig) > 0) {
  226. write.csv(result_sig, paste0('Normal_DEPR_', gsub(" ", "_", cell_type), '_sig_genes_NEBULA.csv'), row.names = FALSE)
  227. message(" - Found ", nrow(result_sig), " differentially expressed genes at FDR < 0.05")
  228. } else {
  229. message(" - No significant differentially expressed genes found at FDR < 0.05")
  230. }
  232. # Create volcano plot
  233. if (nrow(result) > 10) { # Only create plot if we have enough genes
  234. p <- ggplot(result, aes(x = log2FC, y = -log10(p_val))) +
  235. geom_point(aes(color = fdr < 0.05), alpha = 0.6) +
  236. scale_color_manual(values = c("grey", "red")) +
  237. labs(title = paste0("Volcano Plot: Normal_wt_DEP vs Normal_w_DEP ", cell_type),
  238. x = "log2(Fold Change)",
  239. y = "-log10(p-value)") +
  240. theme_minimal() +
  241. theme(legend.position = "none") +
  242. geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
  243. geom_vline(xintercept = c(-1, 1), linetype = "dashed")
  245. ggsave(paste0('LOAD_DEPR_Normal_', gsub(" ", "_", cell_type), '_volcano.png'), p, width = 8, height = 6)
  246. }
  248. # Return success
  249. return(TRUE)
  250. }
  252. # If running as array job, process the i-th cell type
  253. if (!is.na(i) && i <= length(cell_types)) {
  254. result <- run_nebula_analysis(cell_types[i])
  255. if (is.null(result)) {
  256. message("Analysis failed or was skipped for cell type: ", cell_types[i])
  257. }
  258. } else {
  259. # If not an array job or for testing, process all cell types sequentially
  260. message("Processing all cell types sequentially...")
  261. for (cell_type in cell_types) {
  262. result <- run_nebula_analysis(cell_type)
  263. gc() # Clean up memory between runs
  264. }
  265. }
  267. # Summary of all analyses
  268. summary_file <- file.path(out_dir, "nebula_analysis_Normal_DEPR_summary.txt")
  269. analyzed_cell_types <- list.files(out_dir, pattern = "all_genes_NEBULA.csv")
  270. sig_cell_types <- list.files(out_dir, pattern = "sig_genes_NEBULA.csv")
  272. writeLines(c(
  273. paste("NEBULA Differential Expression Analysis Summary - AD Study"),
  274. paste("Date:", Sys.Date()),
  275. paste("Comparison: Normal_wt_DEP vs Normal_w_DEP"),
  276. paste("Covariates: sex"),
  277. paste("Total cell types analyzed:", length(analyzed_cell_types)),
  278. paste("Cell types with significant genes:", length(sig_cell_types)),
  279. paste("Cell types analyzed:"),
  280. paste(" -", gsub("LOAD_vs_Normal_|\\_all_genes_NEBULA.csv", "", analyzed_cell_types))
  281. ), summary_file)
  283. message("Analysis complete. Results saved to ", out_dir)
  285. print(sessionInfo())

13.Normal.DEP.cell_type_DE_analysis.R at commit 470c908, no license · at the source

Overview

Authors: Michael W. Lutz1, Zhaohui Man1, Ornit Chiba‐Falek1,2
  1. Division of Translational Brain Sciences, Department of Neurology Duke University School of Medicine Durham North Carolina USA
  2. Center for Genomic and Computational Biology Duke University School of Medicine Durham North Carolina USA
Institutions: Duke University (United States); Duke Medical Center (United States)
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association, volume 22, issue 9, article e71823
Dates: received 28 April 2026; accepted 11 August 2026; published online 11 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/alz.71823 · PMID 42728740 · PMCID PMC13569991 · OpenAlex W7212383219
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), depression (population), cellular / molecular (subfield)
Methods: Statistics
Keywords: Alzheimer's disease, co‐pathologies, disease heterogeneity, disease subtypes, major depressive disorder, multiomics, neuropsychiatric symptoms, single‐cell sequencing, transcriptomics, translational science
MeSH: Alzheimer Disease*, Brain*, Major Depressive Disorder*, Transcriptome*, Aged, Female, Gene Expression Profiling, Humans, Male, Proteomics (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institutes of Health/National Institute on Aging (NIH/NIA) (RF1 AG077695, R01 AG057522); Alzheimer's Association (22‐AAIIA‐953269)
Citations: not cited yet (Europe PMC); 82 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.

NCTrailRunner/NPS_manuscript

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 470c908d9fe0002040dceaed395d859486e283a3, 15 September 2026
Languages: R (21), Shell (1)
Size: 23 files, 22 scripts
Software Heritage: not archived
Found in: “DATE AVAILABILITY STATEMENT”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (21 files), Seurat (19 files), ggplot2 (10 files), data.table (7 files), patchwork (4 files), reshape2 (4 files), Harmony (3 files), reticulate (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
23 files
At the source: github.com/NCTrailRunner/NPS_manuscript

The paper's code and data availability statement is in the Data section.

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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;
  • 22 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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Data

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Code and data availability statement

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Read it in the paper: doi.org/10.1002/alz.71823.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 10 keywords, 10 MeSH terms, 2 funders, 81 references.

Cite

This paper

Lutz, M. W., Man, Z., & Chiba‐Falek, O. (2026). Cellular transcriptomic signatures underpinning the heterogeneity of depression in Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(9), e71823. https://doi.org/10.1002/alz.71823

BibTeX

@article{lutz2026cellular,
author = {Lutz, Michael W. and Man, Zhaohui and Chiba‐Falek, Ornit},
title = {{Cellular transcriptomic signatures underpinning the heterogeneity of depression in Alzheimer's disease}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
month = sep,
volume = {22},
number = {9},
pages = {e71823},
publisher = {Wiley},
issn = {1552-5260},
doi = {10.1002/alz.71823},
url = {https://doi.org/10.1002/alz.71823},
pmid = {42728740},
pmcid = {PMC13569991}
}

RIS

TY - JOUR
AU - Lutz, Michael W.
AU - Man, Zhaohui
AU - Chiba‐Falek, Ornit
TI - Cellular transcriptomic signatures underpinning the heterogeneity of depression in Alzheimer's disease
T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association
J2 - Alzheimers Dement
PY - 2026
DA - 2026/09/01
VL - 22
IS - 9
SP - e71823
SN - 1552-5260
PB - Wiley
DO - 10.1002/alz.71823
UR - https://doi.org/10.1002/alz.71823
LA - en
ER -

CSL-JSON

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"id": "10.1002/alz.71823",
"type": "article-journal",
"title": "Cellular transcriptomic signatures underpinning the heterogeneity of depression in Alzheimer's disease",
"container-title": "Alzheimer's & dementia : the journal of the Alzheimer's Association",
"author": [
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"family": "Lutz",
"given": "Michael W."
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"family": "Man",
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},
{
"family": "Chiba‐Falek",
"given": "Ornit"
}
],
"container-title-short": "Alzheimers Dement",
"volume": "22",
"issue": "9",
"page": "e71823",
"DOI": "10.1002/alz.71823",
"PMID": "42728740",
"PMCID": "PMC13569991",
"ISSN": "1552-5260",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/alz.71823",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
1
]
]
}
}

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