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Multi-omic cell free DNA profiles of cerebral spinal fluid from lung cancer metastasis to the brain.

Code ↔ Paper

7 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 7 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] § Results › Specific 5mC methylation patterns present in the CSF cfDNA of brain metastasis ↔ CSF_cfDNA_5mC_5hmC_analysis.R, lines 54–104 · score 0.75 · IL1R2, RAD54B, CCNH, CD74, ZKSCAN1, MID1
  2. [2] § Results › Comparing the methylation of brain metastasis CSF cfDNA to NSCLC primary tumor DNA ↔ CSF_cfDNA_5mC_5hmC_analysis.R, lines 54–104 · score 0.68 · ATAD3A, COL6A6, RAD54B, MIF, MMP13, overlapped
  3. [3] § Results › Decreased intragenic hydroxymethylation in the CSF cfDNA of brain metastasis ↔ CSF_cfDNA_5mC_5hmC_analysis.R, lines 106–146 · score 0.68 · IL21R, ADCY9, HDAC9, KCNQ1, MTDH, PBX1
  4. [4] § Results › Decreased intragenic hydroxymethylation in the CSF cfDNA of brain metastasis ↔ cfDNA_enrichr_analysis.R, lines 52–91 · score 0.57 · PPI Hub Proteins, Transcription Factor, hmCG, Enrichr, bar
  5. [5] § Materials and methods › Methylation and hydroxymethylation analysis of CSF cfDNA ↔ CSF_cfDNA_5mC_5hmC_analysis.R, lines 1–52 · score 0.56 · GENCODE v38, intersecting, FDR, hydroxymethylation, protein, promoter
  6. [6] § Results › Features of CSF cfDNA level among patients with brain metastasis ↔ CSF_cfDNA_sequencing_coverage.R, the whole file · a weak match · score 0.54 · sequencing coverage, CSF volumes, CSF samples, DNA, cancer
  7. [7] § Results › CSF cfDNA profiles reveal longitudinal changes from clinical events ↔ CSF_plasma_cfDNA_size_comparision.R, lines 189–229 · score 0.54 · dt_ratio, md_ratio, mt_ratio, nucleosome ratios, dinucleosome, trinucleosome

Paper

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The authors' code

R · 253 lines · 9.5 KB · MIT · 4 matches

  1. # CSF_cfDNA_5mC_5hmC_analysis.R
  2. # this file is meant to be used inside Rstudio
  4. # this script takes in promoter methylation (5mC) & intragenic hydroxymethylation (5hmC) % per CpG site with fdr-corrected q values, groups by gene level
  5. # and plots the promoter 5mC% or intragenic 5hmC% per gene (volcano plot) or per sample (point plot) for both cancer and control
  7. # this file was generated using bedtools intersect with GENCODE v38:
  8. # bedtools intersect -a gencode.v38.genes.sorted.bed -b P1.5mC.sorted.bedgraph -wa -wb -sorted | awk '{print($0 '\tP1')}' > P1.5mC.sorted.genes.intersect.bed
  9. # P1.5mC.sorted.bedgraph is a bedgraph of the modified_bases.5mC.bed from dorado, filtered on column 5 > 0, and extracting only the chrom, start, end, and percent methylation columns
  10. # multiple intersected bed files can be joined using cat, and fed into this script.
  12. library(data.table)
  13. library(dplyr)
  14. library(ggplot2)
  15. library(ggrepl)
  16. library(tidyverse)
  18. # plot differential prmoter 5mCG% and intragenic 5hmCG% between cancer and control CSF of all genes in volcano plot
  19. # read in merged bed files
  20. # promoter 5mCG
  22. hyper_pro_5mc <- fread("/mnt/ix1/Projects/M085_221117_CSF_cfDNA_methylation/03_statistical_test/04_volcano_plot/02_samples/gene_summary/hyper.2kbpromoter_summary_5mCG.txt")
  23. hypo_pro_5mc <- fread("/mnt/ix1/Projects/M085_221117_CSF_cfDNA_methylation/03_statistical_test/04_volcano_plot/02_samples/gene_summary/hypo.2kbpromoter_summary_5mCG.txt")
  25. # intragenic 5hmCG
  27. hyper_5hmc <- fread("/mnt/ix1/Projects/M085_221117_CSF_cfDNA_methylation/03_statistical_test/04_volcano_plot/02_samples/gene_summary/hyper.genebody_summary_5hmCG.txt")
  28. hypo_5hmc <- fread("/mnt/ix1/Projects/M085_221117_CSF_cfDNA_methylation/03_statistical_test/04_volcano_plot/02_samples/gene_summary/hypo.genebody_summary_5hmCG.txt")
  30. # gene analysis
  31. # diff_methyl = 5mCG% (cancer ) - 5mCG% (control) per gene
  33. # promoter 5mCG
  34. # use gencodev38 gene names to filter protein_coding genes
  36. gencode <- fread("/mnt/ix1/Projects/M086_221117_TGP_cfDNA_methylation/annotation/gencode.v38.genes_summary.txt")
  37. gencode_proteincoding <- gencode %>% filter(V5=='protein_coding')
  38. protein_coding_genes <- unique(gencode_proteincoding$V6)
  40. hyper_pro_5mc_mean <- hyper_pro_5mc %>%
  41. select(c('V5', 'V6', 'V10', 'V11')) %>%
  42. rename(gene = V5, gene_id = V6,
  43. diff_methyl = V10, qval = V11) %>%
  44. filter(gene %in% protein_coding_genes)
  46. hypo_pro_5mc_mean <- hypo_pro_5mc %>%
  47. select(c('V5', 'V6', 'V10', 'V11')) %>%
  48. rename(gene = V5, gene_id = V6,
  49. diff_methyl = V10, qval = V11) %>%
  50. filter(gene %in% protein_coding_genes)
  52. df_pro_5mc <- rbind(hyper_pro_5mc_mean, hypo_pro_5mc_mean)
  54. # add legend to the plot
  56. df_pro_5mc <- df_pro_5mc %>%
  57. mutate(group = case_when(
  58. qval < 0.01 & diff_methyl > 0 ~ "hypermethylated in cancer",
  59. qval < 0.01 & diff_methyl < 0 ~ "hypomethylated in cancer",
  60. TRUE ~ "Background"))
  62. # add reported marker labels on the plot
  64. df_marker_pro_5mc <- df_pro_5mc %>%
  65. filter(gene %in% c('ATAD3A','CCNH','CD74','DYRK2','IL1R2',
  66. 'LIPH','MID1','MIF','MMP13','RAD54B',
  67. 'SETDB1','ZKSCAN1','COL6A6',
  68. 'EPB41L1','IL6R','PER3')) %>%
  69. filter(qval<0.01) %>% group_by(gene, diff_methyl,qval) %>%
  70. summarize(n = n())
  72. # plot the volcano plot
  74. p1_2 <- ggplot(data=df_pro_5mc, aes(x=diff_methyl, y=-log10(1e-5+qval))) +
  75. geom_point(aes(color=group),size=6, alpha=0.6) +
  76. scale_color_manual(name ='type',
  77. values = c("hypermethylated in cancer" = "#CC79A7",
  78. "hypomethylated in cancer" = "#009E73")) +
  79. geom_text_repel(data = df_marker_pro_5mc,
  80. aes(x= diff_methyl, y=-log10(1e-5+qval), label = gene),
  81. min.segment.length = 0,
  82. max.overlaps=Inf,
  83. size = 8 ) +
  84. labs(x='CpG promoter 5mCG difference per gene (%)') +
  85. theme_classic(30) +
  86. theme(axis.text = element_text(face = 'bold'),
  87. axis.title = element_text(face = 'bold'))
  88. p1_2
  90. # intragenic 5hmCG
  92. hyper_5hmc_mean <- hyper_5hmc %>%
  93. select(c('V5', 'V6', 'V10', 'V11')) %>%
  94. rename(gene_type = V5, gene = V6,
  95. diff_methyl = V10, qval = V11) %>%
  96. filter(gene_type == 'protein_coding')
  98. hypo_5hmc_mean <- hypo_5hmc %>%
  99. select(c('V5', 'V6', 'V10', 'V11')) %>%
  100. rename(gene_type = V5, gene = V6,
  101. diff_methyl = V10, qval = V11) %>%
  102. filter(gene_type == 'protein_coding')
  104. df_5hmc <- rbind(hyper_5hmc_mean, hypo_5hmc_mean)
  106. # add legend to the plot
  108. df_5hmc <- df_5hmc %>%
  109. mutate(group = case_when(
  110. qval < 0.01 & diff_methyl > 0 ~ "hyper-hydroxymethylated in cancer",
  111. qval < 0.01 & diff_methyl < 0 ~ "hypo-hydroxymethylated in cancer",
  112. TRUE ~ "Background"))
  114. # add reported marker labels on the plot
  116. df_marker_5hmc <- df_5hmc %>%
  117. filter(gene %in% c('ADCY9','HDAC9','IL21R','KCNQ1',
  118. 'MTDH','PBX1','SASH1','TPST1','TWIST2')) %>%
  119. filter(qval<0.01) %>% group_by(gene, diff_methyl,qval) %>%
  120. summarize(n = n())
  122. # plot the volcano plot
  124. p1_2 <- ggplot(data=df_5hmc, aes(x=diff_methyl, y=-log10(1e-6 + qval))) +
  125. geom_point(aes(color=group),size=6, alpha=0.6) +
  126. scale_color_manual(name ='type', values = c("hyper-hydroxymethylated in cancer" = "#CC79A7",
  127. "hypo-hydroxymethylated in cancer" = "#009E73")) +
  128. geom_text_repel(data = df_marker_5hmc,
  129. aes(x = diff_methyl, y = -log10(1e-6 + qval), label = gene),
  130. min.segment.length = 0,
  131. max.overlaps = Inf,
  132. size = 8) +
  133. labs(x='CpG intragenic 5hmCG difference per gene (%)') +
  134. theme_classic(30) +
  135. theme(axis.text = element_text(face = 'bold'),
  136. axis.title = element_text(face = 'bold'))
  138. p1_2
  140. ggsave("5mCG_promoter_volcano.png", plot = p1_1, dpi = 300, units = "in", height = 12, width = 20)
  141. ggsave("5hmCG_intragenic_volcano.png", plot = p1_2, dpi = 300, units = "in", height = 12, width = 20)
  144. # plot differential prmoter 5mCG% and intragenic 5hmCG% of marker genes per sample
  145. # these marker genes were defined as sig genes (q<0.01) between cancer vs. control CSF samples, and reported in the literature as biomarkers for NSCLC
  146. # e.g. a sig gene that was hypomethylated in our data and reported as an upregulated marker/overexpressed in NSCLC patients
  148. # promoter 5mCG
  150. p2_1 <- ggplot(marker_5mc_pro, aes(x= gene, y= mean_methyl, color = type)) +
  151. geom_jitter(size=6, alpha=0.6, width = 0.3, height = 2) +
  152. #scale_y_continuous(limits = c(0,100), breaks = c(0,50,100)) +
  153. geom_vline(xintercept = c(1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,
  154. 10.5,11.5,12.5,13.5,14.5,15.5),
  155. linetype="dotted",size=0.5) +
  156. labs(x = "Gene" ,y ="CpG promoter 5mCG
  157. per sample (%)") +
  158. theme_classic(25) +
  159. theme(axis.text = element_text(face = "bold"),
  160. axis.title = element_text(face = "bold"),
  161. axis.text.x = element_text(angle = -45, hjust = 0.05))
  162. p2_1
  164. # intragenic 5hmCG
  166. p2_2 <- ggplot(marker_5hmc, aes(x= gene, y= mean_methyl, color = type)) +
  167. geom_jitter(size=6, alpha=0.6, width = 0.3, height = 2) +
  168. #scale_y_continuous(limits = c(0,100), breaks = c(0,50,100)) +
  169. geom_vline(xintercept = c(1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5),
  170. linetype="dotted",size=0.5) +
  171. labs(x = "Gene" ,y ="CpG intragenic 5hmCG
  172. per sample (%)") +
  173. theme_classic(30) +
  174. theme(axis.text = element_text(face = "bold"),
  175. axis.title = element_text(face = "bold"),
  176. axis.text.x = element_text(angle = -45, hjust = 0.05))
  177. p2_2
  179. ggsave("5mCG_promoter_marker_plot.png", plot = p2_1, dpi = 300, units = "in", height = 8, width = 20)
  180. ggsave("5hmCG_intragenic_marker_plot.png", plot = p2_2, dpi = 300, units = "in", height = 10, width = 16)
  183. # plot average genome-wide 5hmCG% between cancer and control CSF samples
  184. # read in the merged bedmethyl files for 5hmCG
  185. # these are bedgraphs containing 5hmC% before intersecting with GENCODE
  187. cpg_5hmc <- fread("/mnt/ix1/Projects/M085_221117_CSF_cfDNA_methylation/00_sample/mp0007_sample_summary_sup_methyl/sorted_bedgraph/5hmCG/merged.genomewide.5hmCG.txt")
  188. cpg_5hmc <- cpg_5hmc %>% mutate(type = ifelse(grepl('control',V5),'Healthy','Cancer'))
  190. # calculate average genome-wide 5hmCG%
  192. avg_5hmc <- cpg_5hmc %>% group_by(V5,type) %>%
  193. summarise(mean_5hmCG = mean(V4)) %>%
  194. mutate(m = signif(mean_5hmCG,2))
  196. # plot
  198. p3_1 <- ggplot(avg_5hmc, aes(x= type, y= mean_5hmCG, fill = type)) +
  199. geom_boxplot(width=0.7) +
  200. labs(x = NULL, y ="Average genome-wide
  201. 5hmCG (%)") +
  202. #scale_y_continuous(limits = c(0.8,9), breaks = c(1,3,5,7,9)) +
  203. theme_classic(25) +
  204. theme(axis.text = element_text(face = "bold"),
  205. axis.title = element_text(face = "bold"))
  206. p3_1
  208. p3_2 <- ggplot(avg_5hmc, aes(x= m, fill = type)) +
  209. geom_bar(width=0.1, color ='black') +
  210. labs(x = "Average genome-wide 5hmCG (%)", y ="Number of samples", fill = "Type") +
  211. #scale_x_continuous(limits = c(54,72), breaks = c(54,60,66,72)) +
  212. #scale_y_continuous(limits = c(0,2), breaks = c(0,1,2)) +
  213. facet_wrap(~ type, ncol = 1) +
  214. theme_classic(40) +
  215. theme(strip.text = element_text(size = 40),
  216. axis.text = element_text(face = "bold"),
  217. axis.title = element_text(face = "bold"),
  218. panel.margin = unit(0, "lines"))
  219. p3_2
  221. # caculate p value for the box plot
  223. p_5hmc <- avg_5hmc %>% summarise(p=t.test(mean_5hmCG[type=='Healthy'], mean_5hmCG[type=='Cancer'])$p.value)
  225. ggsave("genomewide_avg_5hmCG_boxplot.png", plot = p3_1, dpi = 300, units = "in", height = 6, width = 8)
  226. ggsave('genomewide_avg_5hmCG_barplot.png', plot = p3_2, dpi = 300, units = 'in', height = 10, width = 20)

CSF_cfDNA_5mC_5hmC_analysis.R at commit e393d61, under MIT · at the source

Overview

Authors: Tianqi Chen1, Xiangqi Bai1, Georgiana Burnside2, Thy Trang Hoang Trinh2, Melanie Hayden Gephart2, Hanlee P Ji1,3, Billy T Lau1
ORCID iDs: Hanlee P Ji
  1. Division of Oncology, Department of Medicine, Stanford University, School of Medicine, Stanford, CA 94305, United States
  2. Department of Neurosurgery, Stanford University, Stanford, CA 94305, United States
  3. Department of Electrical Engineering, Stanford University, Stanford, CA 94305, United States
Institutions: Stanford Medicine (United States); Stanford University (United States)
Journal: NAR cancer, volume 8, issue 3, article zcag021
Dates: received 6 March 2026; accepted 3 August 2026; published online 31 August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/narcan/zcag021 · PMID 42676708 · PMCID PMC13527165 · OpenAlex W7204818232
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population)
Methods: Statistics
MeSH: Biomarkers, Tumor*, Brain Neoplasms*, Carcinoma, Non-Small-Cell Lung*, Cell-Free Nucleic Acids*, Lung Neoplasms*, Aged, Case-Control Studies, DNA Fragmentation, DNA Methylation, DNA, Neoplasm, Female, Humans, Male, Middle Aged, Multiomics (* major topic)
Topic: Cancer Genomics and Diagnostics (Cancer Research, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NCI NIH HHS (U01 CA282212, P30 CA124435, U54 CA261717); NHGRI NIH HHS (R35 HG011292)
Citations: not cited yet (Europe PMC); 85 references in the paper

Abstract

Cerebrospinal fluid (CSF) is a liquid biological medium immersing the brain and spinal canal. For patients with metastatic cancer from the lung, tumor cells release their cell-free DNA (cfDNA) into the CSF. We applied a nanopore single-molecule sequencing approach to analyze the fragmentation, methylation, and hydroxymethylation patterns present in CSF-derived cell-free DNA from patients with metastatic lung cancer to the brain. We compared the cancer cfDNA finding to non-cancer healthy controls and their CSF cell-free DNA. Among cancer patients, we observed enriched mono-nucleosome levels and significantly higher mono-/trinucleosome ratios in cancer patients. Comparison with plasma-derived cfDNA further confirmed the unique fragmentation features of CSF-derived cfDNA. Distinct methylation and hydroxymethylation patterns were observed between cancer and control CSF samples. We observed significantly lower degree of hydroxymethylation in cancer patients compared with healthy controls and the affected genes had different pathway profiles. Overall, CSF cfDNA in patients with non-small cell lung cancer brain metastases had distinct profiles of DNA fragmentation, methylation, and hydroxymethylation.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

Tianqi-Kiki/nanopore_CSF_cfDNA

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e393d614799ee1a94bcbb550de6d920429c50c69, 24 July 2025
Languages: R (5)
Size: 7 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (5 files), data.table (4 files), tidyverse (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/Tianqi-Kiki/nanopore_CSF_cfDNA

Zenodo 20173042

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (5 files), data.table (4 files), tidyverse (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source: doi.org/10.5281/zenodo.20173042

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • 7 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

Sequence-aligned BAM files and associated methylation calls are deposited in NCBI’s dbGAP under accession phs003794.v1.p1 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs003794.v1.p1). Scripts related to the study are available at GitHub: https://github.com/Tianqi-Kiki/nanopore_CSF_cfDNA and Zenodo: DOI https://doi.org/10.5281/zenodo.20173042.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 15 MeSH terms, 2 funders, 85 references.

Cite

This paper

Chen, T., Bai, X., Burnside, G., Trinh, T. T. H., Gephart, M. H., Ji, H. P., & Lau, B. T. (2026). Multi-omic cell free DNA profiles of cerebral spinal fluid from lung cancer metastasis to the brain. NAR cancer, 8(3), zcag021. https://doi.org/10.1093/narcan/zcag021

BibTeX

@article{chen2026multi,
author = {Chen, Tianqi and Bai, Xiangqi and Burnside, Georgiana and Trinh, Thy Trang Hoang and Gephart, Melanie Hayden and Ji, Hanlee P and Lau, Billy T},
title = {{Multi-omic cell free DNA profiles of cerebral spinal fluid from lung cancer metastasis to the brain}},
journal = {NAR cancer},
year = {2026},
month = aug,
volume = {8},
number = {3},
pages = {zcag021},
publisher = {Oxford University Press},
issn = {2632-8674},
doi = {10.1093/narcan/zcag021},
url = {https://doi.org/10.1093/narcan/zcag021},
pmid = {42676708},
pmcid = {PMC13527165}
}

RIS

TY - JOUR
AU - Chen, Tianqi
AU - Bai, Xiangqi
AU - Burnside, Georgiana
AU - Trinh, Thy Trang Hoang
AU - Gephart, Melanie Hayden
AU - Ji, Hanlee P
AU - Lau, Billy T
TI - Multi-omic cell free DNA profiles of cerebral spinal fluid from lung cancer metastasis to the brain
T2 - NAR cancer
J2 - NAR Cancer
PY - 2026
DA - 2026/08/31
VL - 8
IS - 3
SP - zcag021
SN - 2632-8674
PB - Oxford University Press
DO - 10.1093/narcan/zcag021
UR - https://doi.org/10.1093/narcan/zcag021
LA - en
ER -

CSL-JSON

{
"id": "10.1093/narcan/zcag021",
"type": "article-journal",
"title": "Multi-omic cell free DNA profiles of cerebral spinal fluid from lung cancer metastasis to the brain",
"container-title": "NAR cancer",
"author": [
{
"family": "Chen",
"given": "Tianqi"
},
{
"family": "Bai",
"given": "Xiangqi"
},
{
"family": "Burnside",
"given": "Georgiana"
},
{
"family": "Trinh",
"given": "Thy Trang Hoang"
},
{
"family": "Gephart",
"given": "Melanie Hayden"
},
{
"family": "Ji",
"given": "Hanlee P"
},
{
"family": "Lau",
"given": "Billy T"
}
],
"container-title-short": "NAR Cancer",
"volume": "8",
"issue": "3",
"page": "zcag021",
"DOI": "10.1093/narcan/zcag021",
"PMID": "42676708",
"PMCID": "PMC13527165",
"ISSN": "2632-8674",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/narcan/zcag021",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
31
]
]
}
}

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In common: data.table, ggplot2, tidyverse, genetics / omics, other condition, 1 reference
[7] doi:10.1093/nar/gkag454 [code]
RegRegSEA: a web server for regulatory region set enrichment analysis of epigenomic data.
Journal: Nucleic acids research
In common: data.table, ggplot2, tidyverse, genetics / omics, 1 reference
[8] doi:10.3390/ijms27167275 [code]
Integrative Multi-Omics Analysis of Multiple Sclerosis Reveals Cell-Type-Specific Regulatory Landscapes and Discordant Methylation-Expression Coupling.
Journal: International journal of molecular sciences
In common: data.table, ggplot2, tidyverse, genetics / omics, 1 reference
[9] doi:10.1038/s41592-026-03211-w [code]
Spatial isoform sequencing at single-cell resolution reveals cell-type-specific spatial isoform variability in multiple brain cell types.
Journal: Nature methods
In common: data.table, ggplot2, tidyverse, genetics / omics, 1 reference
[10] doi:10.1186/s12916-026-04957-y [code]
Thyroid-stimulating hormone receptor mediates peripheral-central neuroimmune crosstalk in autoimmune thyroid diseases.
Journal: BMC medicine
In common: data.table, ggplot2, tidyverse, genetics / omics, 1 reference

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