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The EDC mixture "NeuroMix" alters neuroendocrine physiology, fatty food preference, and the central reward transcriptome.

Code ↔ Paper

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  1. [1] § Materials and methods › Statistical analyses › Eigengene generation and trait prediction ↔ NMXseq_EigengeneAnalysis_2026.R, lines 212–299 · score 0.62 · Module membership, matrix, gene expression, Pearson, prcomp, Eigengenes
  2. [2] § Materials and methods › Statistical analyses › Enrichment analysis ↔ NMXseq_EigengeneAnalysis_2026.R, lines 2–43 · score 0.54 · Gene Ontology, FDR, gene expression, matching, GO term, clustering

Paper

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The authors' code

R · 300 lines · 9.9 KB · no license · 2 matches

  2. ################################################################################
  3. # Gene Ontology Eigengene Scores
  4. ################################################################################
  6. # Emily N. Hilz, Phd.
  7. # The University of Texas at Austin
  8. # [email hidden]
  9. # please reference https://doi.org/10.1210/endocr/bqag084 when using this code
  11. ################################################################################
  12. # PCA by sample - loop using linear mixed model - individual GO terms
  13. ################################################################################
  14. # The following code provides example flow for one sex and brain region.
  16. library(dplyr)
  17. library(tidyr)
  18. library(lme4)
  19. library(lmerTest)
  20. library(ggplot2)
  22. # Step 1: Load normalized gene expression and GO data files
  23. IDs <- read.csv("NMXseq_IDs.csv")
  25. norm <- read.csv("NMXseq_NormalizedGenes.csv")
  27. geneGOs <- read.csv("GeneGOs.csv") # Derived from Msibdbr package version 7.1
  28. GOresults <- read.csv("GOall_Fem_OFC.csv") %>% filter(fdr < 0.05)
  30. # Step 2: Match genes to GO terms and clusters
  31. matched_genes <- GOresults %>%
  32. inner_join(geneGOs, by = c("id" = "GO")) %>%
  33. select(name, gene) # Keep only relevant columns
  35. matched_summary <- matched_genes %>%
  36. group_by(name) %>%
  37. summarise(Matching_Genes = list(unique(gene)), # Store genes as a list
  38. Gene_Count = n()) # Count matching genes per summary
  40. # Step 3: Initialize lists for results
  41. lmm_results_list <- list()
  42. pca_plots <- list()
  43. pc_results_list <- list() # Stores PC1 & PC2 results for all
  45. # Step 4: Loop through each unique name (i.e., GO term)
  46. for (summary_category in unique(matched_genes$name)) {
  48. # Filter genes for the current summary category
  49. summary_genes <- matched_genes %>%
  50. filter(name == summary_category) %>%
  51. distinct(gene) %>%
  52. pull(gene) # Extract gene names as a vector
  54. # Filter expression data for relevant genes; mind the filter
  55. filtered_norm_expr <- norm %>%
  56. filter(gene %in% summary_genes, Sex == "Female", Brain_Region == "OFC")
  58. # Aggregate duplicate genes per Sample.ID
  59. filtered_genes <- filtered_norm_expr %>%
  60. group_by(gene, Sample.ID) %>%
  61. summarize(Expression = mean(Expression, na.rm = TRUE), .groups = "drop")
  63. # Transform to wide format
  64. filtered_wide <- filtered_genes %>%
  65. pivot_wider(names_from = gene, values_from = Expression) %>%
  66. column_to_rownames(var = "Sample.ID")
  68. # Skip if not enough genes for PCA
  69. if (ncol(filtered_wide) < 2) next
  71. # Perform PCA
  72. pca_results <- prcomp(filtered_wide, scale. = F, center = TRUE)
  74. # Convert PCA results to dataframe
  75. pca_df <- as.data.frame(pca_results$x) %>%
  76. rownames_to_column(var = "Sample.ID") %>%
  77. left_join(IDs, by = "Sample.ID") # Add Treatment info
  79. # Skip if fewer than 2 PCs
  80. if (ncol(pca_df) < 3) next
  82. # Fit Linear Model for PC1
  83. lm_PC1 <- lm(PC1 ~ Treatment, data = pca_df)
  85. # Fit Linear Model for PC2
  86. lm_PC2 <- lm(PC2 ~ Treatment, data = pca_df)
  88. # Store LMM results
  89. lmm_results_list[[summary_category]] <- list(
  90. PC1_LM = summary(lm_PC1),
  91. PC2_LM = summary(lm_PC2)
  92. )
  94. # Store PCA results with name
  95. pc_results_list[[summary_category]] <- pca_df %>%
  96. select(ID, Brain_Region, Sex, Treatment, PC1, PC2) %>%
  97. mutate(name = summary_category, # Add cluster name
  98. ID = paste0("MDR ", ID)) # Modify ID
  100. # Generate PCA plot
  101. p <- ggplot(pca_df, aes(x = PC1, y = PC2, label = Sample.ID, color = Treatment)) +
  102. geom_point(size = 4, alpha = 0.8, show.legend = TRUE) +
  103. geom_text(vjust = 1.5, size = 3, check_overlap = TRUE, color = "black") +
  104. labs(title = paste("PCA of", summary_category, "cluster"),
  105. x = "PC1",
  106. y = "PC2",
  107. color = "GO Term") +
  108. theme_classic()
  110. # Store the plot in a list
  111. pca_plots[[summary_category]] <- p
  112. }
  114. # Optional: Print LMM results to look at treatment effects on PC scores per term
  115. lmm_results_list
  117. # Optional: Check for outliers by viewing PCA plots, perform exclusion as needed
  118. pca_plots
  121. # Step 5: Combine all PC1 & PC2 results into one dataframe
  122. PCS <- bind_rows(pc_results_list)
  124. # Step 6: Save PCA results as a CSV file
  125. write.csv(PCS, "FemOFC_PCA_results_IndvGO.csv", row.names = FALSE)
  128. #Optional: Filter and view significant models and plots
  129. extract_significant_models <- function(lmm_results, threshold = 0.05) {
  130. significant_results <- list()
  132. for (cluster in names(lmm_results)) {
  133. model_PC1 <- lmm_results[[cluster]]$PC1_LM
  134. model_PC2 <- lmm_results[[cluster]]$PC2_LM
  136. # Extract p-values from coefficients
  137. pvals_PC1 <- coef(model_PC1)[, "Pr(>|t|)"]
  138. pvals_PC2 <- coef(model_PC2)[, "Pr(>|t|)"]
  140. # Check if any term in PC1 or PC2 is below threshold
  141. if (any(pvals_PC1 < threshold, na.rm = TRUE) || any(pvals_PC2 < threshold, na.rm = TRUE)) {
  142. significant_results[[cluster]] <- lmm_results[[cluster]]
  143. }
  144. }
  146. return(significant_results)
  147. }
  148. significant_lmm_results <- extract_significant_models(lmm_results_list, threshold = 0.055)
  149. print(significant_lmm_results)
  152. # Create an empty list to store plots
  153. significant_lm_plots <- list()
  155. # Extract only significant clusters
  156. significant_clusters <- names(significant_lmm_results)
  158. # Loop through each significant cluster and generate regression plots
  159. for (cluster in significant_clusters) {
  161. # Retrieve PCA scores for this cluster
  162. cluster_data <- data %>% filter(name == cluster)
  164. # Skip if not enough data
  165. if (nrow(cluster_data) < 5) next
  167. # Generate regression plot for PC1
  168. p1 <- ggplot(pca_df, aes(x = PC1, y = PC2, label = Sample.ID, color = Treatment)) +
  169. geom_point(size = 4, alpha = 0.8, show.legend = TRUE) +
  170. geom_text(vjust = 1.5, size = 3, check_overlap = TRUE, color = "black") +
  171. labs(title = paste("PCA of", summary_category, "cluster"),
  172. x = "PC1",
  173. y = "PC2",
  174. color = "GO Term") +
  175. theme_classic() +
  176. scale_color_manual(values = c("NMX" = "orange",
  177. "Veh" = "cornflowerblue"))
  179. # Store only the plots from significant models
  180. significant_lm_plots[[paste(cluster, "PC1")]] <- p1
  181. }
  183. # View only significant model plots
  184. significant_lm_plots
  186. ################################################################################
  187. # PCA by GO term for module membership
  188. ################################################################################
  190. library(dplyr)
  191. library(tidyr)
  192. library(tibble)
  195. # Step 1: Load data files
  196. IDs <- read.csv("NMXseq_IDs.csv")
  197. norm <- read.csv("NMXseq_NormalizedGenes.csv")
  198. geneGOs <- read.csv("GeneGOs.csv")
  199. GOresults <- read.csv("GOall_Fem_NAC.csv") %>% na.omit()
  201. # Step 2: Match genes to GO terms and clusters
  202. matched_genes <- GOresults %>%
  203. inner_join(geneGOs, by = c("id" = "GO")) %>%
  204. select(name, gene) # Keep only relevant columns
  206. matched_summary <- matched_genes %>%
  207. group_by(name) %>%
  208. summarise(Matching_Genes = list(unique(gene)), # Store genes as a list
  209. Gene_Count = n()) # Count matching genes per summary
  211. run_go_pca <- function(go_name,
  212. sex = "Female",
  213. brain_region = "NAC",
  214. pc_keep = 1:2,
  215. scale = FALSE,
  216. center = TRUE,
  217. membership_pc = 1, # <-- which PC defines "eigengene"
  218. membership_method = "pearson",
  219. membership_use = "pairwise.complete.obs") {
  221. summary_genes <- matched_genes %>%
  222. filter(name == go_name) %>%
  223. distinct(gene) %>%
  224. pull(gene)
  226. filtered_norm_expr <- norm %>%
  227. filter(gene %in% summary_genes,
  228. Sex == sex,
  229. Brain_Region == brain_region)
  231. filtered_genes <- filtered_norm_expr %>%
  232. group_by(gene, Sample.ID) %>%
  233. summarize(Expression = mean(Expression, na.rm = TRUE), .groups = "drop")
  235. filtered_wide <- filtered_genes %>%
  236. pivot_wider(names_from = gene, values_from = Expression) %>%
  237. column_to_rownames("Sample.ID")
  239. if (ncol(filtered_wide) < 2) return(NULL)
  240. if (nrow(filtered_wide) < 3) return(NULL)
  242. pca <- prcomp(filtered_wide, scale. = scale, center = center)
  244. # ---- Loadings ----
  245. loadings <- as.data.frame(pca$rotation) %>%
  246. rownames_to_column("gene")
  248. pcs_exist <- paste0("PC", pc_keep)
  249. pcs_exist <- pcs_exist[pcs_exist %in% colnames(loadings)]
  250. loadings <- loadings %>%
  251. select(gene, all_of(pcs_exist)) %>%
  252. mutate(go_name = go_name,
  253. sex = sex,
  254. brain_region = brain_region)
  256. # ---- Variance explained ----
  257. var_explained <- (pca$sdev^2 / sum(pca$sdev^2)) * 100
  258. var_explained <- tibble(
  259. go_name = go_name,
  260. sex = sex,
  261. brain_region = brain_region,
  262. PC = paste0("PC", seq_along(var_explained)),
  263. pct = var_explained
  264. )
  266. # ---- Module membership (kME-like): cor(gene expression, eigengene) ----
  267. eigengene <- pca$x[, paste0("PC", membership_pc)]
  269. # Make sure columns are genes
  270. expr_mat <- as.matrix(filtered_wide) # rows=samples, cols=genes
  272. kME <- apply(expr_mat, 2, function(gene_vec) {
  273. cor(gene_vec, eigengene,
  274. method = membership_method,
  275. use = membership_use)
  276. })
  278. membership <- tibble(
  279. gene = names(kME),
  280. kME = unname(kME),
  281. abs_kME = abs(kME),
  282. go_name = go_name,
  283. sex = sex,
  284. brain_region = brain_region
  285. ) %>%
  286. arrange(desc(abs_kME))
  288. list(
  289. pca = pca,
  290. loadings = loadings,
  291. var_explained = var_explained,
  292. membership = membership,
  293. eigengene = tibble(Sample.ID = rownames(filtered_wide),
  294. eigengene = as.numeric(eigengene),
  295. go_name = go_name,
  296. sex = sex,
  297. brain_region = brain_region)
  298. )
  299. }

NMXseq_EigengeneAnalysis_2026.R at commit fe24150, no license · at the source

Overview

Authors: Emily N Hilz1, Nicholas R Gonzalez1, Elena Morales-Grahl1, Jahnabi Deka1, Dana L Sheinhaus1, Lindsay M Thompson1, Christopher D Kassotis2, Andrea C Gore1
ORCID iDs: Emily N Hilz, Elena Morales-Grahl, Christopher D Kassotis, Andrea C Gore
  1. Department of Pharmacy, Division of Pharmacology and Toxicology, The University of Texas at Austin, Austin, TX 78712, USA
  2. Institute of Environmental Health Sciences and Department of Pharmacology, Wayne State University, Detroit, MI 48202, USA
Institutions: The University of Texas at Austin (United States); Wayne State University (United States)
Journal: Endocrinology, volume 167, issue 9, article bqag084
Dates: received 22 April 2026; accepted 24 July 2026; published online 1 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1210/endocr/bqag084 · PMID 42538733 · PMCID PMC13490871 · OpenAlex W7172112851
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), rat (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity
Keywords: endocrine-disrupting chemicals, obesogens, nucleus accumbens, appetitive behavior, thyroid, transcriptome
MeSH: Endocrine Disruptors*, Food Preferences*, Neurosecretory Systems*, Reward*, Transcriptome*, Animals, Female, Male, Obesity, Pregnancy, Rats, Rats, Sprague-Dawley, Thyroid Hormone Receptors beta, Thyroid Hormones (* major topic)
Topic: Effects and risks of endocrine disrupting chemicals (Health, Toxicology and Mutagenesis, Environmental Science), according to OpenAlex
Funding: NIH (K99 ES037720, R35 ES035024); ACG
Citations: not cited yet (Europe PMC); 102 references in the paper
Research resources: RRID:AB_2868475

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 2 matches between paragraphs and lines of code.

emilyhilz/NeuroMix-2026

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: fe24150f45328c13f0ebface0a30a763a7cebc5c, 13 August 2026
Languages: R (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Eigengene generation and trait prediction”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), tidyverse (2 files), car (1 file), lme4 (1 file), lmerTest (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
3 files
At the source: github.com/emilyhilz/NeuroMix-2026

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 2 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

No dataset and no data link were found in the paper.

Data availability statement

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1210/endocr/bqag084.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 6 keywords, 14 MeSH terms, 2 funders, 96 references, 1 RRID.

Cite

This paper

Hilz, E. N., Gonzalez, N. R., Morales-Grahl, E., Deka, J., Sheinhaus, D. L., Thompson, L. M., Kassotis, C. D., & Gore, A. C. (2026). The EDC mixture "NeuroMix" alters neuroendocrine physiology, fatty food preference, and the central reward transcriptome. Endocrinology, 167(9), bqag084. https://doi.org/10.1210/endocr/bqag084

BibTeX

@article{hilz2026edc,
author = {Hilz, Emily N and Gonzalez, Nicholas R and Morales-Grahl, Elena and Deka, Jahnabi and Sheinhaus, Dana L and Thompson, Lindsay M and Kassotis, Christopher D and Gore, Andrea C},
title = {{The EDC mixture "NeuroMix" alters neuroendocrine physiology, fatty food preference, and the central reward transcriptome}},
journal = {Endocrinology},
year = {2026},
month = aug,
volume = {167},
number = {9},
pages = {bqag084},
publisher = {The Endocrine Society},
issn = {0013-7227},
doi = {10.1210/endocr/bqag084},
url = {https://doi.org/10.1210/endocr/bqag084},
pmid = {42538733},
pmcid = {PMC13490871}
}

RIS

TY - JOUR
AU - Hilz, Emily N
AU - Gonzalez, Nicholas R
AU - Morales-Grahl, Elena
AU - Deka, Jahnabi
AU - Sheinhaus, Dana L
AU - Thompson, Lindsay M
AU - Kassotis, Christopher D
AU - Gore, Andrea C
TI - The EDC mixture "NeuroMix" alters neuroendocrine physiology, fatty food preference, and the central reward transcriptome
T2 - Endocrinology
J2 - Endocrinology
PY - 2026
DA - 2026/08/01
VL - 167
IS - 9
SP - bqag084
SN - 0013-7227
PB - The Endocrine Society
DO - 10.1210/endocr/bqag084
UR - https://doi.org/10.1210/endocr/bqag084
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "The EDC mixture \"NeuroMix\" alters neuroendocrine physiology, fatty food preference, and the central reward transcriptome",
"container-title": "Endocrinology",
"author": [
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"family": "Hilz",
"given": "Emily N"
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{
"family": "Gonzalez",
"given": "Nicholas R"
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{
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"given": "Elena"
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{
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{
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"volume": "167",
"issue": "9",
"page": "bqag084",
"DOI": "10.1210/endocr/bqag084",
"PMID": "42538733",
"PMCID": "PMC13490871",
"ISSN": "0013-7227",
"publisher": "The Endocrine Society",
"URL": "https://doi.org/10.1210/endocr/bqag084",
"language": "en",
"issued": {
"date-parts": [
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2026,
8,
1
]
]
}
}

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