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Sex and life experience shape locus coeruleus pretangle tau pathology.

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] § RESULTS › snRNA‐seq identifies major hippocampal cell types and regionally distinct neuronal subpopulations ↔ ex_subclsuters.ipynb, lines 31–33 · score 0.90 · Camk2d, Htr2c, Mgat4c, Sema5a, Arhgap12, Cpne4
  2. [2] § RESULTS › snRNA‐seq identifies major hippocampal cell types and regionally distinct neuronal subpopulations ↔ hippo_allcelltypes.ipynb, lines 29–30 · score 0.89 · Inpp5d, Slc17a7, Slc1a2, Slc1a3, Arhgap15, Ctss
  3. [3] § RESULTS › Sex‐ and experience‐dependent transcriptomic remodeling of hippocampal neurons and glia induced by LC pretangle tau › Excitatory neurons ↔ excitatory_go.R, lines 126–188 · score 0.84 · axon ensheathment, cellular stress, synaptic signaling, protein regulation, myelination, neurodevelopmental
  4. [4] § METHODS › Single‐nucleus RNA sequencing › Gene ontology analysis ↔ OligoAstroMicro_go.R, lines 27–72 · score 0.82 · enrichGO, keyType, compareCluster, db, readable, rn
  5. [5] § METHODS › Single‐nucleus RNA sequencing › Gene ontology analysis ↔ excitatory_go.R, lines 26–71 · score 0.82 · enrichGO, keyType, compareCluster, db, readable, rn
  6. [6] § RESULTS › snRNA‐seq identifies major hippocampal cell types and regionally distinct neuronal subpopulations ↔ hippo_allcelltypes.ipynb, lines 71–88 · score 0.64 · low quality cells, inhibitory neurons, excitatory neurons, Endo, OPCs, LQCs
  7. [7] § RESULTS › Sex‐ and experience‐dependent transcriptomic remodeling of hippocampal neurons and glia induced by LC pretangle tau › Inhibitory neurons ↔ excitatory_go.R, lines 73–124 · score 0.61 · electron transport chain, mitochondrial energy, excitatory, clusters
  8. [8] § RESULTS › Sex‐ and experience‐dependent transcriptomic remodeling of hippocampal neurons and glia induced by LC pretangle tau › Inhibitory neurons ↔ excitatory_go.R, lines 126–188 · score 0.55 · synaptic signaling, RNA processing, calcium, mitochondrial, excitatory, regulated

Paper

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The authors' code

R · 275 lines · 9.3 KB · no license · 4 matches

  1. library(clusterProfiler)
  2. library(enrichplot)
  3. library(org.Rn.eg.db)
  4. library(ggnewscale)
  5. library(RColorBrewer)
  7. # ------------------------------ Parameters ------------------------------------
  8. deg_dir <- "~/your_file_path/ex_G0_DEGs_c012"
  9. pval_thr <- 0.05
  10. logfc_thr <- 0.5
  11. topN <- 10
  13. # Faceting (clusters) and x-axis ordering
  14. cluster_order <- c("C0","C1","C2") # facets you want to show
  15. base_order <- c("cE14F","cE14M","eSTRF","eSTRM","eERF","eERM","lSTRF","lSTRM","lERF","lERM")
  17. # ---------------------------- Helper functions --------------------------------
  18. strip_ver <- function(x) gsub("\\.\\d+$", "", x) # remove Ensembl version suffix
  19. norm <- function(x) {
  20. x |>
  21. stringr::str_replace_all("[\u2010-\u2015\u2212]", "-") |> # normalize dashes
  22. stringr::str_squish() |>
  23. stringr::str_to_lower()
  24. }
  26. # ---------------------- 1) Load DEGs and split UP/DOWN ------------------------
  27. deg_files <- list.files(deg_dir, pattern = "\\.csv$", full.names = TRUE)
  28. combined_up <- list()
  29. combined_down <- list()
  31. for (file in deg_files) {
  32. condition <- tools::file_path_sans_ext(basename(file)) # e.g., "cE14F_C0"
  33. df <- read.csv(file, check.names = FALSE)
  35. gene_col <- dplyr::case_when(
  36. "Gene" %in% names(df) ~ "Gene",
  37. "gene_names" %in% names(df) ~ "gene_names",
  38. TRUE ~ NA_character_
  39. )
  40. if (is.na(gene_col)) next
  42. up_genes <- df %>%
  43. filter(p_val_adj < pval_thr, logFC > logfc_thr) %>%
  44. pull(!!sym(gene_col)) %>% unique()
  46. dn_genes <- df %>%
  47. filter(p_val_adj < pval_thr, logFC < -logfc_thr) %>%
  48. pull(!!sym(gene_col)) %>% unique()
  50. combined_up[[condition]] <- up_genes
  51. combined_down[[condition]] <- dn_genes
  52. }
  54. # Strip Ensembl version suffixes (if present)
  55. combined_up <- lapply(combined_up, strip_ver)
  56. combined_down <- lapply(combined_down, strip_ver)
  58. # ------------------- 2) Build compareCluster input & run ----------------------
  59. gene_sets <- c(
  60. setNames(combined_up, paste0(names(combined_up), "_UP")),
  61. setNames(combined_down, paste0(names(combined_down), "_DOWN"))
  62. )
  64. go_all <- compareCluster(
  65. geneCluster = gene_sets,
  66. fun = "enrichGO",
  67. OrgDb = org.Rn.eg.db,
  68. keyType = "ENSEMBL",
  69. ont = "BP",
  70. readable = TRUE
  71. )
  73. # ---------------- 3) Tidy results: parse sample & cluster ---------------------
  74. go_raw <- as_tibble(go_all@compareClusterResult)
  76. # Cluster names look like "<SampleBase>_C#_UP" or "_DOWN"
  77. go_df <- go_raw %>%
  78. mutate(
  79. Regulation = if_else(grepl("_UP$", Cluster), "Up", "Down"),
  80. Sample = sub("_(UP|DOWN)$", "", Cluster), # "<SampleBase>_C#"
  81. Cluster3 = sub("^.*_(C\\d+)$", "\\1", Sample), # allow any C#
  82. SampleBase = sub("_(C\\d+)$", "", Sample), # prefix before _C#
  83. negLog10Padj = -log10(p.adjust + 1e-300)
  84. ) %>%
  85. filter(!is.na(Cluster3), p.adjust < 0.05, Count >= 3) %>%
  86. mutate(
  87. Cluster3 = factor(Cluster3, levels = cluster_order),
  88. SampleBase = factor(SampleBase, levels = base_order),
  89. Regulation = factor(Regulation, levels = c("Up","Down"))
  90. ) %>%
  91. droplevels()
  93. #for topN GO terms run 4A # for your selected terms run 4B # then continue from step 5
  95. # ---------------- 4A) Option: keep topN per panel (default) -------------------sel <- go_df %>%
  96. group_by(Regulation, Cluster3, Sample) %>%
  97. slice_min(order_by = p.adjust, n = topN, with_ties = FALSE) %>%
  98. ungroup()
  100. global_levels <- sel %>%
  101. group_by(Description) %>%
  102. summarise(median_padj = median(p.adjust, na.rm = TRUE), .groups = "drop") %>%
  103. arrange(median_padj) %>%
  104. pull(Description)
  106. plot_df <- go_df %>%
  107. filter(Description %in% global_levels) %>%
  108. mutate(Description = factor(Description, levels = rev(global_levels))) %>%
  109. droplevels()
  112. # ---------------- 4B) Option: selcted GO term from top list for main figure-> GO IDs -----------
  113. keep_terms <- c(
  114. # ——— Mitochondria Energy Production
  115. "oxidative phosphorylation",
  116. "aerobic respiration",
  117. "cellular respiration",
  118. "ATP synthesis coupled electron transport",
  119. "respiratory electron transport chain",
  120. "mitochondrial ATP synthesis coupled electron transport",
  121. "proton transmembrane transport",
  122. "proton motive force-driven mitochondrial ATP synthesis",
  123. "NADH dehydrogenase complex assembly",
  124. "mitochondrial respiratory chain complex I assembly",
  126. # ——— RNA/Protein Regulation
  127. "ribosome biogenesis",
  128. "ribonucleoprotein complex biogenesis",
  129. "protein-RNA complex organization",
  130. "ribosome assembly",
  131. "rRNA processing",
  132. "rRNA metabolic process",
  133. "cytoplasmic translation",
  134. "translational elongation",
  135. "protein folding",
  136. "proteasomal protein catabolic process",
  138. # ——— Synaptic Signaling Plasticity and Learning
  139. "regulation of synaptic vesicle recycling",
  140. "vesicle-mediated transport in synapse",
  141. "presynaptic endocytosis",
  142. "postsynapse organization",
  143. "regulation of postsynaptic membrane neurotransmitter receptor levels",
  144. "chemical synaptic transmission, postsynaptic",
  145. "receptor localization to synapse",
  146. "dendritic spine organization",
  147. "regulation of synaptic plasticity",
  148. "regulation of long-term synaptic potentiation",
  149. "learning or memory",
  150. "long-term memory",
  152. # ——— Cellular Stress Calcium Signaling
  153. "cellular response to reactive oxygen species",
  154. "reactive oxygen species metabolic process",
  155. "response to endoplasmic reticulum stress",
  156. "endoplasmic reticulum calcium ion homeostasis",
  157. "negative regulation of calcium-mediated signaling",
  158. "regulation of intrinsic apoptotic signaling pathway by p53 class mediator",
  159. "mitochondrial outer membrane permeabilization",
  160. "regulation of mitochondrial membrane permeability involved in apoptotic process",
  162. # ——— Neurodevelopment Axonal Transport
  163. "hippocampus development",
  164. "limbic system development",
  165. "midbrain development",
  166. "neural nucleus development",
  167. "axonal transport",
  168. "retrograde axonal transport",
  169. "myelination",
  170. "axon ensheathment",
  171. "axon regeneration",
  172. "axonogenesis"
  173. )
  175. keep_tbl <- tibble(
  176. keep_term_raw = keep_terms,
  177. keep_term_clean = norm(keep_terms),
  178. keep_order = seq_along(keep_terms)
  179. )
  181. all_terms <- as_tibble(go_all@compareClusterResult) %>%
  182. distinct(ID, Description) %>%
  183. mutate(Desc_clean = norm(Description))
  184. join_ids <- keep_tbl %>%
  185. inner_join(all_terms, by = c("keep_term_clean" = "Desc_clean")) %>%
  186. arrange(keep_order)
  188. keep_ids <- join_ids$ID
  190. # Filter go_df by these IDs
  191. plot_df <- go_df %>%
  192. filter(ID %in% keep_ids)
  194. desc_levels <- join_ids$Description # in your keep_terms order
  196. plot_df <- plot_df %>%
  197. mutate(Description = factor(Description, levels = rev(desc_levels))) %>% # rev() puts first at top
  198. droplevels()
  200. # ---------------- 5) Pad empty samples so ticks stay visible ------------------
  201. pad_grid <- tidyr::expand_grid(
  202. Cluster3 = factor(cluster_order, levels = cluster_order),
  203. SampleBase = factor(base_order, levels = base_order)
  204. )
  206. pad_df <- pad_grid %>%
  207. mutate(
  208. Description = factor(NA, levels = levels(plot_df$Description)),
  209. Count = NA_integer_,
  210. Regulation = factor("Up", levels = c("Up","Down")),
  211. negLog10Padj = NA_real_
  212. )
  214. plot_df_pad <- bind_rows(plot_df, pad_df) %>%
  215. mutate(SampleBase = factor(SampleBase, levels = base_order))
  217. # ---------------------------- 6) Plot figure ----------------------------------
  218. down_df <- filter(plot_df_pad, Regulation == "Down")
  219. up_df <- filter(plot_df_pad, Regulation == "Up")
  221. p_go <- ggplot(plot_df_pad) +
  222. # DOWN (Blues)
  223. geom_point(
  224. data = down_df,
  225. aes(x = SampleBase, y = Description, size = Count, fill = negLog10Padj),
  226. shape = 21, color = "#08519C", stroke = 0.6, alpha = 0.95, na.rm = TRUE
  227. ) +
  228. scale_fill_gradientn(
  229. name = "Down: -log10(adj p)",
  230. colours = brewer.pal(9, "Blues"),
  231. na.value = NA
  232. ) +
  233. ggnewscale::new_scale_fill() +
  234. # UP (Reds)
  235. geom_point(
  236. data = up_df,
  237. aes(x = SampleBase, y = Description, size = Count, fill = negLog10Padj),
  238. shape = 21, color = "#A50F15", stroke = 0.6, alpha = 0.95, na.rm = TRUE
  239. ) +
  240. scale_fill_gradientn(
  241. name = "Up: -log10(adj p)",
  242. colours = brewer.pal(9, "Reds"),
  243. na.value = NA
  244. ) +
  245. scale_size(name = "Gene Count", range = c(2, 8)) +
  246. guides(size = guide_legend(order = 1)) +
  247. facet_grid(. ~ Cluster3, scales = "free_x", space = "free_x") +
  248. scale_x_discrete(limits = base_order, drop = FALSE) +
  249. scale_y_discrete(drop = FALSE) +
  250. labs(
  251. #title = "GO BP enrichment per sample (excitatory clusters)",#
  252. x = "Samples", y = "GO terms"
  253. ) +
  254. theme_bw() +
  255. theme(
  256. axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 8),
  257. axis.text.y = element_text(size = 7),
  258. strip.text = element_text(face = "bold"),
  259. panel.grid.major.y = element_line(size = 0.2, linetype = 3)
  260. )
  262. p_go
  264. # ---------------------------- 7) Save outputs ---------------------------------
  265. saveRDS(go_all, file = "excitatory_GO_up_down_compareCluster.rds")
  267. raw_df <- as.data.frame(go_all@compareClusterResult)
  268. write.csv(raw_df, file = "excitatory_GO_BP_results_raw.csv", row.names = FALSE)
  270. write.csv(go_df, file = "excitatory_GO_BP_results_tidy.csv", row.names = FALSE)
  271. write.csv(plot_df, file = "excitatory_GO_BP_results_plotdf.csv", row.names = FALSE)
  273. ggsave("excitatory_GO_BP_selected.pdf", plot = p_go, width = 12, height = 10, units = "in")
  274. ggsave("excitatory_GO_BP_plot.svg", plot = p_go, width = 11, height = 8.5, units = "in", bg = "transparent")
  275. ggsave("excitatory_GO_BP_plot.jpg", plot = p_go, width = 11, height = 8.5, units = "in", dpi = 300)

excitatory_go.R at commit fcb73f0, no license · at the source

Overview

Authors: Zia Hasan1, Sarah E. Torraville1, Tamunotonye Omoluabi1, Aida Maziar1, Onyedikachi N. Belolise1, Lauren A. MacGowan1, Cassandra M. Flynn1, Camila Reinhardt1, Susan G. Walling2, Touati Benoukraf1, Qi Yuan1
ORCID iDs: Touati Benoukraf, Qi Yuan
  1. Biomedical Sciences Faculty of Medicine Memorial University of Newfoundland St. John's Newfoundland and Labrador Canada
  2. Department of Psychology Faculty of Science Memorial University of Newfoundland St. John's Newfoundland and Labrador Canada
Institutions: Memorial University of Newfoundland (Canada)
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association, volume 22, issue 3, article e71285
Dates: received 31 August 2025; accepted 14 February 2026; published online 10 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/alz.71285 · PMID 41804725 · PMCID PMC12973150 · OpenAlex W7134842462
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), rat (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Evoked potentials
Keywords: BDNF, enrichment, hippocampus, locus coeruleus, pretangle tau, sex, stress, transcriptomic
MeSH: Alzheimer Disease*, Locus Coeruleus*, tau Proteins*, Tauopathies*, Animals, Female, Hippocampus, Humans, Male, Neurons, Rats, Rats, Transgenic, Sex Characteristics, Stress, Psychological (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: Canadian Institutes of Health Research Project Grant (#PJT‐169197); Alzheimer Society of Canada New Investigator Grant (#20‐04)
Citations: not cited yet (Europe PMC); 75 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.

ziahasanz/hippocampus_paper

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: fcb73f0d996def9ef20082f919c3238e6fd9f2e5, 14 May 2026
Languages: R (5), Jupyter (3)
Size: 10 files, 8 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), Matplotlib (3 files), NumPy (3 files), pandas (3 files), Scanpy (3 files), seaborn (3 files), clusterProfiler (2 files), patchwork (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
9 files
At the source: github.com/ziahasanz/hippocampus_paper

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

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 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);
  • 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

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Code and data availability statement

The paper has a code and 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/10.1002/alz.71285.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 8 keywords, 14 MeSH terms, 2 funders, 74 references.

Cite

This paper

Hasan, Z., Torraville, S. E., Omoluabi, T., Maziar, A., Belolise, O. N., MacGowan, L. A., Flynn, C. M., Reinhardt, C., Walling, S. G., Benoukraf, T., & Yuan, Q. (2026). Sex and life experience shape locus coeruleus pretangle tau pathology. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(3), e71285. https://doi.org/10.1002/alz.71285

BibTeX

@article{hasan2026sex,
author = {Hasan, Zia and Torraville, Sarah E. and Omoluabi, Tamunotonye and Maziar, Aida and Belolise, Onyedikachi N. and MacGowan, Lauren A. and Flynn, Cassandra M. and Reinhardt, Camila and Walling, Susan G. and Benoukraf, Touati and Yuan, Qi},
title = {{Sex and life experience shape locus coeruleus pretangle tau pathology}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
month = mar,
volume = {22},
number = {3},
pages = {e71285},
publisher = {Wiley},
issn = {1552-5260},
doi = {10.1002/alz.71285},
url = {https://doi.org/10.1002/alz.71285},
pmid = {41804725},
pmcid = {PMC12973150}
}

RIS

TY - JOUR
AU - Hasan, Zia
AU - Torraville, Sarah E.
AU - Omoluabi, Tamunotonye
AU - Maziar, Aida
AU - Belolise, Onyedikachi N.
AU - MacGowan, Lauren A.
AU - Flynn, Cassandra M.
AU - Reinhardt, Camila
AU - Walling, Susan G.
AU - Benoukraf, Touati
AU - Yuan, Qi
TI - Sex and life experience shape locus coeruleus pretangle tau pathology
T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association
J2 - Alzheimers Dement
PY - 2026
DA - 2026/03/01
VL - 22
IS - 3
SP - e71285
SN - 1552-5260
PB - Wiley
DO - 10.1002/alz.71285
UR - https://doi.org/10.1002/alz.71285
LA - en
ER -

CSL-JSON

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"DOI": "10.1002/alz.71285",
"PMID": "41804725",
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