OSCR

Proteomic analysis of human chronic traumatic encephalopathy brain implicates proteasome and ribosome dysfunction in disease severity.

Code ↔ Paper

9 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 9 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Hierarchical clustering ↔ leading_edge_genes.R, lines 41–98 · score 0.87 · Lysosomal Glycan Metabolism, GTPase, Complement System, leading edge genes, PI3K, Rho
  2. [2] § Methods › Hierarchical clustering ↔ fgsea_comparative_plots.R, lines 1–51 · score 0.82 · Lysosomal Glycan Metabolism, GTPase, Complement System, PI3K, Rho, AKT
  3. [3] § Model comparisons ↔ fgsea_comparative_plots.R, lines 1–51 · score 0.74 · Lysosomal Glycan Metabolism, GTPase, complement system, Rho, ribosome, dementia
  4. [4] § Model comparisons ↔ leading_edge_genes.R, lines 41–98 · score 0.74 · Lysosomal Glycan Metabolism, GTPase, complement system, Rho, ribosome, dementia
  5. [5] § Methods › Gene set enrichment analysis ↔ limma_fgsea_functions_CTE_proteomics.R, lines 29–131 · score 0.67 · fold change, Limma model, BH, log, fgsea, Gene
  6. [6] § Methods › Filtering and preprocessing ↔ Creating_SummarizedExperiment_hep.R, lines 54–133 · score 0.65 · Internal, PC1, PCA, outlier, metadata, human
  7. [7] § Methods › Network development ↔ cytoscape_prep.R, lines 33–107 · score 0.59 · hierarchical cluster, network, Cytoscape, nodes, cosine, triangles
  8. [8] § Cognitive difficulty scale and dementia associations are concordant ↔ limma_fgsea_functions_CTE_proteomics.R, lines 230–288 · score 0.58 · Limma models, CDS model, dementia model, cognitive
  9. [9] § Methods › Covariate imputation ↔ SomaScan_Amelia_Imputation.R, the whole file · a weak match · score 0.50 · imputation, Amelia, variables, covariates, AD, PMI

Paper

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The authors' code

R · 210 lines · 8.7 KB · no license · 2 matches

  1. #leading edge genes plots
  2. #Helen Pennington
  4. library(data.table)
  5. library(dplyr)
  6. library(ggplot2)
  7. library(tidyr)
  9. full_grouped_paths <- fread("/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/final_files/clustered_fgsea_using_function_nofaq_withproteins_grouped.csv")
  11. groups <- full_grouped_paths %>%
  12. select(-V1, -cluster, -NES)
  14. # Convert wide format to long format
  15. long_df <- groups %>%
  16. pivot_longer(
  17. cols = -c(group, pathway),
  18. names_to = "Protein",
  19. values_to = "value"
  20. )
  22. # Convert to binary leading edge presence (1 if in leading edge, 0 if not)
  23. long_df <- long_df %>%
  24. mutate(in_leading_edge = ifelse(value != 0, 1, 0))
  26. # Count number of pathways in each group per protein
  27. protein_counts <- long_df %>%
  28. group_by(Protein, group) %>%
  29. summarise(count = sum(in_leading_edge), .groups = "drop")
  31. # Pivot back to wide format with proteins as rows and groups as columns
  32. protein_counts_wide <- protein_counts %>%
  33. pivot_wider(
  34. names_from = group,
  35. values_from = count,
  36. values_fill = 0
  37. )
  39. protein_counts_wide
  41. # Get the group with the maximum count for each protein
  42. protein_max_group <- protein_counts_wide %>%
  43. rowwise() %>%
  44. mutate(
  45. max_group = names(select(cur_data(), -Protein))[which.max(c_across(-Protein))]
  46. ) %>%
  47. ungroup() %>%
  48. select(Protein, max_group)
  50. protein_max_group
  52. group_colors <- c(
  53. "Rho GTPase" = "orange",
  54. "MAPK and PI3K/AKT" = "yellow",
  55. "Complement System" = "green",
  56. "Proteasome" = "lightblue",
  57. "Lysosomal Glycan Metabolism" = "blue",
  58. "Growth Factor Signaling" = "purple",
  59. "Ribosome" = "deeppink", # Reddish Purple
  60. "mRNA Processing" = "cyan",
  61. "Other" = "grey",
  62. "Extracellular Matrix" = "#8B4513", # Brown
  63. "DNA Damage Response" = "pink",
  64. "Immune/ Secretory Trafficking" = "#009E73", # Bluish Green
  65. "Purine Metabolism" = "salmon" # Reddish Purple
  66. )
  68. # prep models
  69. CTE_rl_fgsea <- fread("/restricted/projectnb/cteseq/projects/somascan/results/fgsea/gseafiles/plot_format_files/PMI_imp1/fgsea_compact_CTE_RHIvslow.csv")
  70. CTE_rl_paths <- CTE_rl_fgsea[which(CTE_rl_fgsea$padj < 0.05),]
  71. leading_proteins_rl <- as.matrix(sort(table(unlist(strsplit(gsub("\\s+", "", CTE_rl_paths$leadingEdge), ","))), decreasing = TRUE))
  72. leading_proteins_rl <- data.frame(protein = rownames(leading_proteins_rl),`RHI vs Low CTE Proteins` = leading_proteins_rl[,1])
  74. CTE_rh_fgsea <- fread("/restricted/projectnb/cteseq/projects/somascan/results/fgsea/gseafiles/plot_format_files/PMI_imp1/fgsea_compact_CTE_RHIvsHigh.csv")
  75. CTE_rh_paths <- CTE_rh_fgsea[which(CTE_rh_fgsea$padj < 0.05),]
  76. #CTE_rh_paths$leadingEdge <- as.list(CTE_rh_paths$leadingEdge)
  77. leading_proteins_rh <- as.matrix(sort(table(unlist(strsplit(gsub("\\s+", "", CTE_rh_paths$leadingEdge), ","))), decreasing = TRUE))
  78. leading_proteins_rh <- data.frame(protein = rownames(leading_proteins_rh),`RHI vs High CTE Proteins` = leading_proteins_rh[,1])
  80. AT8_fgsea <- fread("/restricted/projectnb/cteseq/projects/somascan/results/fgsea/gseafiles/plot_format_files/PMI_imp1/fgsea_compact_AT8_total.csv")
  81. AT8_paths <- AT8_fgsea[which(AT8_fgsea$padj < 0.05),]
  82. leading_proteins_AT8 <- as.matrix(sort(table(unlist(strsplit(gsub("\\s+", "", AT8_paths$leadingEdge), ","))), decreasing = TRUE))
  83. leading_proteins_AT8 <- data.frame(protein = rownames(leading_proteins_AT8),`AT8 Total Proteins` = leading_proteins_AT8[,1])
  85. totyrs_fgsea <- fread("/restricted/projectnb/cteseq/projects/somascan/results/fgsea/gseafiles/plot_format_files/PMI_imp1/fgsea_compact_totyrs.csv")
  86. totyrs_paths <- totyrs_fgsea[which(totyrs_fgsea$padj < 0.05),]
  87. leading_proteins_totyrs <- as.matrix(sort(table(unlist(strsplit(gsub("\\s+", "", totyrs_paths$leadingEdge), ","))), decreasing = TRUE))
  88. leading_proteins_totyrs <- data.frame(protein = rownames(leading_proteins_totyrs),`Total Years of Play Proteins` = leading_proteins_totyrs[,1])
  90. cds_fgsea <- fread("/restricted/projectnb/cteseq/projects/somascan/results/fgsea/gseafiles/plot_format_files/PMI_imp1/fgsea_compact_CDStot.csv")
  91. cds_paths <- cds_fgsea[which(cds_fgsea$padj < 0.05),]
  92. leading_proteins_cds <- as.matrix(sort(table(unlist(strsplit(gsub("\\s+", "", cds_paths$leadingEdge), ","))), decreasing = TRUE))
  93. leading_proteins_cds <- data.frame(protein = rownames(leading_proteins_cds),`CDS Total Proteins` = leading_proteins_cds[,1])
  95. dem_fgsea <- fread("/restricted/projectnb/cteseq/projects/somascan/results/fgsea/gseafiles/plot_format_files/PMI_imp1/fgsea_compact_DementiaHx.csv")
  96. dem_paths <- dem_fgsea[which(dem_fgsea$padj < 0.05),]
  97. leading_proteins_dem <- as.matrix(sort(table(unlist(strsplit(gsub("\\s+", "", dem_paths$leadingEdge), ","))), decreasing = TRUE))
  98. leading_proteins_dem <- data.frame(protein = rownames(leading_proteins_dem),`Dementia Proteins` = leading_proteins_dem[,1])
  100. #function
  101. plot_leading_edge_single_model <- function(
  102. leading_df,
  103. protein_col = "protein",
  104. model_col,
  105. protein_max_group,
  106. group_colors,
  107. top_n = 100
  108. ) {
  110. plot_data <- leading_df %>%
  111. select(
  112. protein = all_of(protein_col),
  113. Appearances = all_of(model_col)
  114. ) %>%
  115. arrange(desc(Appearances)) %>%
  116. slice_head(n = top_n) %>%
  117. left_join(protein_max_group, by = c("protein" = "Protein")) %>%
  118. mutate(
  119. protein = factor(protein, levels = protein)
  120. )
  122. ggplot(plot_data,
  123. aes(x = Appearances,
  124. y = protein,
  125. fill = max_group)) +
  126. geom_col() +
  127. coord_flip() +
  128. scale_fill_manual(values = group_colors) +
  129. labs(
  130. x = "Number of Appearances",
  131. y = "Protein",
  132. fill = "Group"
  133. ) +
  134. theme_minimal(base_size = 20) +
  135. theme(
  136. axis.text.y = element_text(size = 15),
  137. axis.text.x = element_text(
  138. angle = 45,
  139. hjust = 1,
  140. vjust = 1,
  141. size = 10)
  142. )
  143. }
  145. p <- plot_leading_edge_single_model(
  146. leading_df = leading_proteins_rl,
  147. model_col = "RHI.vs.Low.CTE.Proteins",
  148. protein_max_group = protein_max_group,
  149. group_colors = group_colors,
  150. top_n = 100
  151. )
  152. p
  153. ggsave("/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/lowCTE_leading_edge_300dpi.png", plot = p, width = 20, height = 6, dpi = 300)
  154. saveRDS(p, "/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/lowCTE_leading_edge.rds")
  156. p <- plot_leading_edge_single_model(
  157. leading_df = leading_proteins_rh,
  158. model_col = "RHI.vs.High.CTE.Proteins",
  159. protein_max_group = protein_max_group,
  160. group_colors = group_colors,
  161. top_n = 100
  162. )
  163. p
  164. ggsave("/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/highCTE_leading_edge_300dpi.png", plot = p, width = 20, height = 6, dpi = 300)
  165. saveRDS(p, "/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/highCTE_leading_edge.rds")
  167. p <- plot_leading_edge_single_model(
  168. leading_df = leading_proteins_AT8,
  169. model_col = "AT8.Total.Proteins",
  170. protein_max_group = protein_max_group,
  171. group_colors = group_colors,
  172. top_n = 100
  173. )
  174. p
  175. ggsave("/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/AT8_total_leading_edge_300dpi.png", plot = p, width = 20, height = 6, dpi = 300)
  176. saveRDS(p, "/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/AT8_total_leading_edge.rds")
  178. p <- plot_leading_edge_single_model(
  179. leading_df = leading_proteins_totyrs,
  180. model_col = "Total.Years.of.Play.Proteins",
  181. protein_max_group = protein_max_group,
  182. group_colors = group_colors,
  183. top_n = 100
  184. )
  185. p
  186. ggsave("/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/totyrs_leading_edge_300dpi.png", plot = p, width = 20, height = 6, dpi = 300)
  187. saveRDS(p, "/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/totyrs_leading_edge.rds")
  189. p <- plot_leading_edge_single_model(
  190. leading_df = leading_proteins_dem,
  191. model_col = "Dementia.Proteins",
  192. protein_max_group = protein_max_group,
  193. group_colors = group_colors,
  194. top_n = 100
  195. )
  196. p
  197. ggsave("/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/Dementia_leading_edge_300dpi.png", plot = p, width = 20, height = 6, dpi = 300)
  198. saveRDS(p, "/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/Dementia_leading_edge.rds")
  200. p <- plot_leading_edge_single_model(
  201. leading_df = leading_proteins_cds,
  202. model_col = "CDS.Total.Proteins",
  203. protein_max_group = protein_max_group,
  204. group_colors = group_colors,
  205. top_n = 100
  206. )
  207. p
  208. ggsave("/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/CDS_leading_edge_300dpi.png", plot = p, width = 20, height = 6, dpi = 300)
  209. saveRDS(p, "/restricted/projectnb/cteseq/projects/somascan/proteomics_paper/publication_ready_leading_edge_plots/CDS_leading_edge.rds")

leading_edge_genes.R at commit 661644a, no license · at the source

Overview

Authors: Helen E Pennington1, Dillon Shapiro2,3, Jenny Empawi2,4, Nurgul Aytan5,6,7, Victor E Alvarez5,6,7, Jessie Mez2,7, Michael L Alosco2,7, Xiaoling Zhang2,4, Ann C McKee6,2,7,3, Thor D Stein5,6,2,3, Jonathan D Cherry5,2,3,4,8, Adam Labadorf1,5,2,7
ORCID iDs: Adam Labadorf
  1. Bioinformatics Program, Boston University, Boston Massachusetts, USA
  2. Department of Neurology, Boston University Alzheimer’s Disease Research Center and CTE Centers, Chobanian & Avedisian School of Medicine, Boston Unviersity, Boston Massachusetts, USA
  3. Department of Pathology and Laboratory Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA USA
  4. Section of Biomedical Genetics, Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA USA
  5. VA Boston Healthcare System, U.S. Department of Veteran Affairs, Boston Massachusetts, USA
  6. VA Bedford Healthcare System, Bedford Massachusetts, USA
  7. Department of Neurology, Chobanian & Avedisian School of Medicine, Boston Unviersity, Boston Massachusetts, USA
  8. Department of Anatomy and Neurobiology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA USA
Institutions: Boston University (United States); VA Boston Healthcare System (United States)
Journal: Molecular neurodegeneration advances, volume 2, issue 1, article 36
Dates: received 20 March 2026; accepted 27 July 2026; published online 14 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s44477-026-00045-w · PMID 42603909 · PMCID PMC13476330 · OpenAlex W7203445358
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), Alzheimer's / dementia (population), traumatic brain injury (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: CTE, Neurodegeneration, Repetitive head impacts, Tauopathy, Proteomics
Topic: Traumatic Brain Injury Research (Epidemiology, Medicine), according to OpenAlex
Funding: NIA NIH HHS (R01 AG090553); NIGMS NIH HHS (T32 GM150533)
Citations: not cited yet (Europe PMC); 62 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 9 matches between paragraphs and lines of code.

BU-Neuromics/cte-proteomics-2026

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 661644a67e69adceb389551dd76cfb4ea031e748, 3 August 2026
Languages: R (9)
Size: 11 files, 9 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), data.table (7 files), ggplot2 (5 files), clusterProfiler (2 files), ComplexHeatmap (2 files), limma (2 files), pheatmap (2 files), broom (1 file), igraph (1 file), patchwork (1 file), reticulate (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/BU-Neuromics/cte-proteomics-2026

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

Tracing map

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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;
  • 9 scripts, each with its path and the digest of its content;
  • 9 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/10.1186/s44477-026-00045-w.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 2 funders, 57 references.

Cite

This paper

Pennington, H. E., Shapiro, D., Empawi, J., Aytan, N., Alvarez, V. E., Mez, J., Alosco, M. L., Zhang, X., McKee, A. C., Stein, T. D., Cherry, J. D., & Labadorf, A. (2026). Proteomic analysis of human chronic traumatic encephalopathy brain implicates proteasome and ribosome dysfunction in disease severity. Molecular neurodegeneration advances, 2(1), 36. https://doi.org/10.1186/s44477-026-00045-w

BibTeX

@article{pennington2026proteomic,
author = {Pennington, Helen E and Shapiro, Dillon and Empawi, Jenny and Aytan, Nurgul and Alvarez, Victor E and Mez, Jessie and Alosco, Michael L and Zhang, Xiaoling and McKee, Ann C and Stein, Thor D and Cherry, Jonathan D and Labadorf, Adam},
title = {{Proteomic analysis of human chronic traumatic encephalopathy brain implicates proteasome and ribosome dysfunction in disease severity}},
journal = {Molecular neurodegeneration advances},
year = {2026},
month = aug,
volume = {2},
number = {1},
pages = {36},
issn = {3059-4944},
doi = {10.1186/s44477-026-00045-w},
url = {https://doi.org/10.1186/s44477-026-00045-w},
pmid = {42603909},
pmcid = {PMC13476330}
}

RIS

TY - JOUR
AU - Pennington, Helen E
AU - Shapiro, Dillon
AU - Empawi, Jenny
AU - Aytan, Nurgul
AU - Alvarez, Victor E
AU - Mez, Jessie
AU - Alosco, Michael L
AU - Zhang, Xiaoling
AU - McKee, Ann C
AU - Stein, Thor D
AU - Cherry, Jonathan D
AU - Labadorf, Adam
TI - Proteomic analysis of human chronic traumatic encephalopathy brain implicates proteasome and ribosome dysfunction in disease severity
T2 - Molecular neurodegeneration advances
J2 - Mol Neurodegener Adv
PY - 2026
DA - 2026/08/14
VL - 2
IS - 1
SP - 36
SN - 3059-4944
DO - 10.1186/s44477-026-00045-w
UR - https://doi.org/10.1186/s44477-026-00045-w
LA - en
ER -

CSL-JSON

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"id": "10.1186/s44477-026-00045-w",
"type": "article-journal",
"title": "Proteomic analysis of human chronic traumatic encephalopathy brain implicates proteasome and ribosome dysfunction in disease severity",
"container-title": "Molecular neurodegeneration advances",
"author": [
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"given": "Helen E"
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{
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{
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{
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{
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"given": "Xiaoling"
},
{
"family": "McKee",
"given": "Ann C"
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"family": "Stein",
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],
"container-title-short": "Mol Neurodegener Adv",
"volume": "2",
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"DOI": "10.1186/s44477-026-00045-w",
"PMID": "42603909",
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}

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In common: reticulate, limma, igraph, 6 other tools, genetics / omics, other condition, cellular / molecular

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