OSCR

Cross-species transcriptomic analysis of rodent model fidelity to human mesial temporal lobe epilepsy.

Code ↔ Paper

34 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 34 matches · 9 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Neuroinflammatory and synaptic remodeling signatures characterize the two representative rodent models ↔ RNA_Human/08.Enrichment/GSEA_04.Output_GOBP.R, lines 1–83 · score 0.95 · ionotropic glutamate receptor, NMDA Selective Glutamate, GABAergic, synaptic transmission, Receptor Complex, channel activity
  2. [2] § Results › Neuroinflammatory and synaptic remodeling signatures characterize the two representative rodent models ↔ RNA_Human/08.Enrichment/GSEA_05.Output_GOBP_combined.R, lines 1–81 · score 0.95 · ionotropic glutamate receptor, NMDA Selective Glutamate, GABAergic, synaptic transmission, Receptor Complex, channel activity
  3. [3] § Methods › Human samples ↔ RNA_Human/11.Sample_variables/00.sankey_plot.R, the whole file · a weak match · score 0.90 · tonic clonic postictal, generalized febrile seizures, head trauma, confusion, meningitis, event
  4. [4] § Methods › Human samples ↔ RNA_Human/11.Sample_variables/01.matrix.R, the whole file · a weak match · score 0.90 · tonic clonic postictal, generalized febrile seizures, head trauma, confusion, meningitis, event
  5. [5] § Methods › Expression matrix for cell-type signature generation ↔ scRNA_Animal/01.GSE185862_10X_Deconv.ipynb, lines 320–349 · score 0.85 · Wilcoxon Rank Sum, FindAllMarkers, rank genes, Seurat, Scanpy, union
  6. [6] § Results › Synaptic dysfunction with concurrent neuroinflammation and gliogenesis characterizes human MTLE ↔ RNA_Animal/07.Enrichment/GSEA_04.NES_NG.R, lines 1–63 · score 0.83 · bright orange yellow, deep indigo, vibrant green, rich blue, deep green, GO terms
  7. [7] § Results › Synaptic dysfunction with concurrent neuroinflammation and gliogenesis characterizes human MTLE ↔ RNA_Animal/07.Enrichment/GSEA_04.NES_NT_IC.R, lines 1–61 · score 0.83 · bright orange yellow, deep indigo, vibrant green, rich blue, deep green, GO terms
  8. [8] § Methods › Cell type signature extraction and deconvolution ↔ RNA_Human/09.Deconvolution/03.CIBER_01234Docker.sh, the whole file · a weak match · score 0.81 · CIBERSORTxFractions, HiRes, scRNA, mixture, deconvolution, signatures
  9. [9] § Methods › Epileptogenesis-related gene sets ↔ RNA_Human/10.Correlation/05.GO_Corr_Matrix.R, lines 42–129 · score 0.80 · mossy fiber sprouting, TNF signaling, neuronal death, ion channel, synaptic plasticity, GO terms
  10. [10] § Methods › Epileptogenesis-related gene sets ↔ RNA_Human/10.Correlation/04.Dotplot_Celltype_Epileptogenesis.R, lines 1–49 · score 0.79 · mossy fiber sprouting, TNF signaling, neuronal death, ion channel, synaptic plasticity, neurotransmission
  11. [11] § Results › Large-scale integration establishes a robust cross-species reference ↔ RNA_Human/10.Correlation/05.GO_Dotplot.R, lines 88–153 · score 0.76 · mossy fiber sprouting, TNF signaling, neuronal death, ion channels, synaptic plasticity, neurotransmission
  12. [12] § Results › Large-scale integration establishes a robust cross-species reference ↔ RNA_Human/10.Correlation/05.GO_Corr_Matrix.R, lines 42–129 · score 0.76 · mossy fiber sprouting, TNF signaling, neuronal death, ion channels, synaptic plasticity, neurotransmission
  13. [13] § Results › Hippocampal sclerosis-specific transcriptomic fidelity of the intrahippocampal kainate model ↔ RNA_Human/11.Sample_variables/01.matrix.R, the whole file · a weak match · score 0.72 · initial precipitating injury, head trauma, febrile seizures, events, HS, MTLE
  14. [14] § Results › Hippocampal sclerosis-specific transcriptomic fidelity of the intrahippocampal kainate model ↔ RNA_Human/11.Sample_variables/00.sankey_plot.R, the whole file · a weak match · score 0.72 · initial precipitating injury, head trauma, febrile seizures, events, MTLE, human
  15. [15] § Methods › Cross-species transcriptomic correlation analysis ↔ RNA_Human/10.Correlation/04.Dotplot_Celltype_Epileptogenesis.R, lines 96–154 · score 0.71 · inhibitory neurons, excitatory neurons, endothelial cells, oligodendrocytes, microglia, astrocytes
  16. [16] § Methods › snRNA-seq data processing and cell type annotation ↔ RNA_Human/10.Correlation/04.Dotplot_Celltype_WholeGene.R, lines 92–149 · score 0.70 · inhibitory neurons, excitatory neurons, endothelial cells, oligodendrocytes, microglia, astrocytes
  17. [17] § Methods › snRNA-seq data processing and cell type annotation ↔ RNA_Human/10.Correlation/04.Dotplot_Celltype_Epileptogenesis.R, lines 96–154 · score 0.70 · inhibitory neurons, excitatory neurons, endothelial cells, oligodendrocytes, microglia, astrocytes
  18. [18] § Results › Neuroinflammatory and synaptic remodeling signatures characterize the two representative rodent models ↔ RNA_Animal/07.Enrichment/GSEA_04.NES_NI.R, lines 59–125 · score 0.64 · normalized enrichment score, inflammatory response, Connected dot, NES, GO terms, DOFS
  19. [19] § Results › Neuroinflammatory and synaptic remodeling signatures characterize the two representative rodent models ↔ RNA_Animal/08.Cross_species_gene_comparison/02.Dotplot_DEGs.R, lines 1–53 · score 0.60 · IL1B, KAI IH IPSI, EGR2, OSM, SERPINE1, SHANK3
  20. [20] § Results › Synaptic dysfunction with concurrent neuroinflammation and gliogenesis characterizes human MTLE ↔ RNA_Human/08.Enrichment/GSEA_04.Output_GOBP.R, lines 1–83 · score 0.59 · negative regulation, synaptic plasticity, subacute, pathways, receptor, inflammatory
  21. [21] § Results › Synaptic dysfunction with concurrent neuroinflammation and gliogenesis characterizes human MTLE ↔ RNA_Human/08.Enrichment/GSEA_05.Output_GOBP_combined.R, lines 1–81 · score 0.59 · negative regulation, synaptic plasticity, subacute, pathways, receptor, inflammatory
  22. [22] § Methods › Cell type signature extraction and deconvolution ↔ RNA_Animal/06.Deconvolution/03.CIBER_01234Docker.sh, lines 65–118 · score 0.59 · CIBERSORTxHiRes, mixture, deconvolution, GO, RNA
  23. [23] § Methods › Rodent samples ↔ RNA_Animal/02.Quantification/02.gtf_table.R, lines 1–12 · score 0.59 · Mus musculus, Rattus norvegicus, quantification, mice
  24. [24] § Methods › SIALAP classification for rodent epilepsy models ↔ Scratch_settings.sh, lines 1–42 · score 0.58 · Mus musculus, Rattus norvegicus, epilepsy
  25. [25] § Methods › Pseudobulk differential expression analysis ↔ scRNA_Human/05.Integ_2_Validation_Pseudobulk.R, lines 156–206 · score 0.58 · DESeq2, major cell, pseudobulk, validate, gene, human
  26. [26] § Methods › SIALAP classification for rodent epilepsy models ↔ RNA_Animal/02.Quantification/02.gtf_table.R, lines 1–12 · score 0.57 · Mus musculus, Rattus norvegicus, mouse
  27. [27] § Methods › Rodent samples ↔ Scratch_settings.sh, lines 1–42 · score 0.57 · Mus musculus, Rattus norvegicus, RNA, epilepsy
  28. [28] § Results › Neuroinflammatory and synaptic remodeling signatures characterize the two representative rodent models ↔ RNA_Animal/07.Enrichment/GSEA_04.NES_NT_IC.R, lines 64–131 · score 0.54 · normalized enrichment score, Connected dot, NES, GO terms, DOFS, axis
  29. [29] § Methods › Overrepresentation analysis ↔ RNA_Animal/07.Enrichment/ORA_02.ToppFun.R, the whole file · a weak match · score 0.54 · ToppFun, biological process, ORA, GO, Genes
  30. [30] § Results › Intracranial kainate and perforant path stimulation models best recapitulate human MTLE transcriptome ↔ RNA_Human/10.Correlation/02.Dotplot_correlation_Epileptogenesis_phase.R, lines 119–171 · score 0.53 · PILO IP, KAI IP, KAI IH, hyperacute, HA, intermediate
  31. [31] § Results › Intracranial kainate and perforant path stimulation models best recapitulate human MTLE transcriptome ↔ RNA_Human/10.Correlation/02.Dotplot_correlation_WholeGene_phase.R, lines 101–156 · score 0.53 · PILO IP, KAI IP, KAI IH, hyperacute, HA, intermediate
  32. [32] § Methods › Overrepresentation analysis ↔ RNA_Human/08.Enrichment/ORA_02.ToppFun.R, the whole file · a weak match · score 0.53 · ToppFun, biological process, ORA, GO, Genes
  33. [33] § Methods › RNA trimming and alignment ↔ RNA_Human/01.Pre-processing/03.fastp_paired.sh, the whole file · a weak match · score 0.53 · low_complexity_filter, adapter, FASTP, overrepresented, FASTQ, human
  34. [34] § Methods › RNA trimming and alignment ↔ RNA_Animal/01.Pre-processing/03.fastp_paired.sh, the whole file · a weak match · score 0.53 · low_complexity_filter, adapter, FASTP, overrepresented, FASTQ

Paper

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The authors' code

R · 154 lines · 7.9 KB · no license · 3 matches

  1. library(dplyr)
  2. library(pheatmap)
  3. library(Cairo)
  4. library(ComplexHeatmap)
  5. library(ggplot2)
  6. library(dplyr)
  7. library(tidyr)
  9. ########### Correlation coefficient ##########
  10. # MOUSE
  11. input_file <- paste0("/home/joonho345/1_Epilepsy_RNA/RNA_Human/10.Correlation/04.CellType_Matrix/01.Correlation_Epileptogenesis_Allcelltype_1.txt")
  12. correlation_matrix_CELLTYPE <- read.table(file = input_file, sep = "\t", header = TRUE, row.names = 1, stringsAsFactors = FALSE)
  13. df_list <- list(correlation_matrix_CELLTYPE)
  14. combined_df_M <- do.call(rbind, df_list)
  16. rownames(combined_df_M) <- c("Excitatory Neuron", "Inhibitory Neuron", "Astrocyte", "Microglia", "Oligodendrocyte", "OPC", "Endothelial Cell")
  17. colnames(combined_df_M) <- gsub("\\.", "_", colnames(combined_df_M))
  20. # RAT
  21. input_file <- paste0("/home/joonho345/1_Epilepsy_RNA/RNA_Human/10.Correlation/05.GO_Matrix/01.Correlation_WholeGO_upper_Rat_FILTERED.txt")
  22. correlation_matrix_GO <- read.table(file = input_file, sep = "\t", header = TRUE, row.names = 1, stringsAsFactors = FALSE)
  23. df_list <- list(correlation_matrix_GO)
  24. combined_df_R <- do.call(rbind, df_list)
  26. rownames(combined_df_R) <- c("Neurotransmission", "Ion Channel", "Neuroinflammation", "Neuronal Death", "TNF Signaling",
  27. "Neurogenesis", "Gliogenesis", "Mossy Fiber Sprouting", "Synaptic Plasticity")
  28. colnames(combined_df_R) <- gsub("\\.", "_", colnames(combined_df_R))
  30. additional_rows <- c("Excitatory Neuron", "Inhibitory Neuron", "Astrocyte", "Microglia", "Oligodendrocyte", "OPC", "Endothelial Cell")
  31. empty_rows <- data.frame(matrix(NA, nrow = length(additional_rows), ncol = ncol(combined_df_R)))
  32. rownames(empty_rows) <- additional_rows
  33. colnames(empty_rows) <- colnames(combined_df_R)
  34. combined_df_R <- rbind(empty_rows)
  37. # combine
  38. correlation_matrix_W <- cbind(combined_df_M, combined_df_R)
  39. Group_order <- c(
  40. "M_KAI_IH_IPSI_A_HA", "M_KAI_IH_IPSI_A_AC", "M_KAI_IH_IPSI_A_IM", "M_KAI_IH_IPSI_A_CR",
  41. "M_KAI_IH_CON_A_AC", "M_KAI_IH_CON_A_IM", "M_KAI_IH_CON_A_CR",
  42. "M_KAI_IA_IPSI_A_AC", "M_KAI_IA_IPSI_A_IM", "M_KAI_IA_IPSI_A_CR",
  43. "M_KAI_IP_A_HA", "R_KAI_IP_A_CR", "R_KAI_SUB_I_IM",
  44. "M_PILO_IP_A_HA", "M_PILO_IP_A_AC", "M_PILO_IP_A_IM", "M_PILO_IP_A_CR",
  45. "R_PILO_IP_A_CR", "R_PPS_A_AC", "R_PPS_A_DOFS", "R_PPS_A_IM", "R_PPS_A_CR",
  46. "R_AMG_IPSI_A_CR", "R_TBI_IPSI_A_CR"
  47. )
  48. Group_order <- Group_order[Group_order %in% colnames(correlation_matrix_W)]
  49. correlation_matrix_W <- correlation_matrix_W[, Group_order, drop = FALSE]
  52. ################# p value #################
  53. # MOUSE
  54. #input_file <- paste0("/home/joonho345/1_Epilepsy_RNA/RNA_Human_old/10.Correlation/00.Correlation_Matrix_Mouse_FILTERED.txt")
  55. #correlation_matrix_SAMPLE <- read.table(file = input_file, sep = "\t", header = TRUE, row.names = 1, stringsAsFactors = FALSE)
  56. #input_file <- paste0("/home/joonho345/1_Epilepsy_RNA/RNA_Human_old/12.Correlation_Matrix/04.Pvalue_WholeGO_upper_Mouse_FILTERED.txt")
  57. #correlation_matrix_GO <- read.table(file = input_file, sep = "\t", header = TRUE, row.names = 1, stringsAsFactors = FALSE)
  58. input_file <- paste0("/home/joonho345/1_Epilepsy_RNA/RNA_Human/10.Correlation/04.CellType_Matrix/02.Pvalue_Epileptogenesis_Allcelltype_1.txt")
  59. correlation_matrix_CELLTYPE <- read.table(file = input_file, sep = "\t", header = TRUE, row.names = 1, stringsAsFactors = FALSE)
  60. df_list <- list(correlation_matrix_CELLTYPE)
  61. combined_df_M <- do.call(rbind, df_list)
  63. rownames(combined_df_M) <- c("Excitatory Neuron", "Inhibitory Neuron", "Astrocyte", "Microglia", "Oligodendrocyte", "OPC", "Endothelial Cell")
  64. colnames(combined_df_M) <- gsub("\\.", "_", colnames(combined_df_M))
  66. # RAT
  67. input_file <- paste0("/home/joonho345/1_Epilepsy_RNA/RNA_Human/10.Correlation/05.GO_Matrix/02.Pvalue_WholeGO_upper_Rat_FILTERED.txt")
  68. correlation_matrix_GO <- read.table(file = input_file, sep = "\t", header = TRUE, row.names = 1, stringsAsFactors = FALSE)
  69. df_list <- list(correlation_matrix_GO)
  70. combined_df_R <- do.call(rbind, df_list)
  72. rownames(combined_df_R) <- c("Neurotransmission", "Ion Channel", "Neuroinflammation", "Neuronal Death", "TNF Signaling",
  73. "Neurogenesis", "Gliogenesis", "Mossy Fiber Sprouting", "Synaptic Plasticity")
  74. colnames(combined_df_R) <- gsub("\\.", "_", colnames(combined_df_R))
  76. additional_rows <- c("Excitatory Neuron", "Inhibitory Neuron", "Astrocyte", "Microglia", "Oligodendrocyte", "OPC", "Endothelial Cell")
  77. empty_rows <- data.frame(matrix(NA, nrow = length(additional_rows), ncol = ncol(combined_df_R)))
  78. rownames(empty_rows) <- additional_rows
  79. colnames(empty_rows) <- colnames(combined_df_R)
  80. combined_df_R <- rbind(empty_rows)
  82. # combine
  83. pvalue_matrix_W <- cbind(combined_df_M, combined_df_R)
  84. Group_order <- c(
  85. "M_KAI_IH_IPSI_A_HA", "M_KAI_IH_IPSI_A_AC", "M_KAI_IH_IPSI_A_IM", "M_KAI_IH_IPSI_A_CR",
  86. "M_KAI_IH_CON_A_AC", "M_KAI_IH_CON_A_IM", "M_KAI_IH_CON_A_CR",
  87. "M_KAI_IA_IPSI_A_AC", "M_KAI_IA_IPSI_A_IM", "M_KAI_IA_IPSI_A_CR",
  88. "M_KAI_IP_A_HA", "R_KAI_IP_A_CR", "R_KAI_SUB_I_IM",
  89. "M_PILO_IP_A_HA", "M_PILO_IP_A_AC", "M_PILO_IP_A_IM", "M_PILO_IP_A_CR",
  90. "R_PILO_IP_A_CR", "R_PPS_A_AC", "R_PPS_A_DOFS", "R_PPS_A_IM", "R_PPS_A_CR",
  91. "R_AMG_IPSI_A_CR", "R_TBI_IPSI_A_CR"
  92. )
  93. Group_order <- Group_order[Group_order %in% colnames(pvalue_matrix_W)]
  94. pvalue_matrix_W <- pvalue_matrix_W[, Group_order, drop = FALSE]
  96. ################ total ############
  97. correlation_matrix_W
  98. pvalue_matrix_W
  102. # Ensure both matrices are properly formatted
  103. correlation_matrix_W <- as.matrix(correlation_matrix_W)
  104. pvalue_matrix_W <- as.matrix(pvalue_matrix_W)
  105. cor_long <- as.data.frame(as.table(correlation_matrix_W))
  106. pval_long <- as.data.frame(as.table(pvalue_matrix_W))
  107. colnames(cor_long) <- c("Process", "Animal_Model", "Correlation")
  108. colnames(pval_long) <- c("Process", "Animal_Model", "PValue")
  109. plot_df <- left_join(cor_long, pval_long, by = c("Process", "Animal_Model"))
  111. plot_df <- plot_df %>%
  112. mutate(LogP = -log10(PValue))
  113. process_order <- c("Excitatory Neuron", "Inhibitory Neuron", "Astrocyte", "Microglia", "Oligodendrocyte", "OPC", "Endothelial Cell")
  114. plot_df$Process <- factor(plot_df$Process, levels = rev(process_order))
  116. max_logp <- max(plot_df$LogP, na.rm = TRUE)
  117. max_correlation <- max(plot_df$Correlation, na.rm = TRUE)
  118. min_logp <- min(plot_df$LogP, na.rm = TRUE)
  119. min_correlation <- min(plot_df$Correlation, na.rm = TRUE) #
  122. # Define dot plot
  123. axis_text_size <- 8
  124. legend_text_size <- 8
  125. title_text_size <- 12
  126. font_family <- "Arial"
  127. plot_target <- ggplot(plot_df, aes(x = Animal_Model, y = Process, size = Correlation, color = LogP)) +
  128. geom_hline(aes(yintercept = as.numeric(Process)), linetype = "dotted", color = "gray70") +
  129. geom_vline(aes(xintercept = as.numeric(Animal_Model)), linetype = "dotted", color = "gray70") +
  130. geom_point() +
  131. scale_color_gradient(low = "gray95", high = "firebrick", limits = c(0, max_logp)) + # Dynamic max
  132. scale_size_continuous(range = c(1, 10), limits = c(min_correlation, max_correlation)) + # Dynamic max
  133. theme_minimal() +
  134. theme(
  135. plot.title = element_blank(),
  136. axis.title = element_blank(),
  137. axis.text.x = element_blank(),
  138. axis.text.y = element_text(size = 10),
  139. legend.title = element_text(size = legend_text_size, family = font_family),
  140. legend.text = element_text(size = legend_text_size, family = font_family),
  141. legend.position = "right",
  142. panel.border = element_rect(color = "black", fill = NA, size = 1),
  143. panel.grid = element_blank(),
  144. panel.background = element_rect(fill = "white", color = NA),
  145. plot.background = element_rect(fill = "white", color = NA),
  146. plot.margin = margin(t = 48, r = 20, b = 47, l = 20, unit = "pt")
  147. ) +
  148. labs(x = "Animal Model", y = "Biological Process", color = "-log10(PValue)", size = "Correlation")
  149. plot_width <- 15
  150. plot_height <- 3.5
  151. plot_unit <- 'in'
  152. plot_dpi <- 300
  153. file_name <- paste0("/home/joonho345/1_Epilepsy_RNA/RNA_Human/10.Correlation/04.CellType_Matrix/03.Dotplot_Celltype_Epileptogenesis.png")
  154. ggsave(file_name, plot = plot_target, width = plot_width, height = plot_height, dpi = plot_dpi, units = plot_unit)

04.Dotplot_Celltype_Epileptogenesis.R at commit fbb6f9a, no license · at the source

Overview

Authors: Joonho Kim1,2, Sangbo Lee1,3, Bora Kim2, Kyoung Hoon Jeong4, Soojin Park5, Soomi Cho2, Hoon-Chul Kang3, Won-Joo Kim2,4, Sangwoo Kim1
ORCID iDs: Joonho Kim, Bora Kim, Won-Joo Kim, Sangwoo Kim
  1. Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, South Korea
  2. Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
  3. Division of Pediatric Neurology, Epilepsy Research Institute, Severance Hospital, Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea
  4. Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
  5. Department of Neurology, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
Institutions: Yonsei University (South Korea); Yonsei University Health System (South Korea); Severance Hospital (South Korea)
Journal: Nature communications, volume 17, issue 1, article 7104
Dates: received 13 November 2025; accepted 14 May 2026; published online 2 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73796-5 · PMID 42230582 · PMCID PMC13392440 · OpenAlex W7163189459
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), rat (organism), epilepsy (population), cellular / molecular (subfield)
Methods: Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions
Keywords: Computational biology and bioinformatics, Computational neuroscience
MeSH: Epilepsy, Temporal Lobe*, Transcriptome*, Adult, Animals, Disease Models, Animal, Drug Resistant Epilepsy, Female, Gene Expression Profiling, Hippocampal Sclerosis, Hippocampus, Humans, Kainic Acid, Male, Mice, Middle Aged, Rats, Species Specificity (* major topic)
Topic: Epilepsy research and treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Ministry of Health and Welfare (Ministry of Health, Welfare and Family Affairs) (RS-2024-00405260); National Research Foundation of Korea (RS-2025-23323132); Korean Neurological Assocation is the most representative association in clinical neurology field in South Korea.
Citations: cited by 1 paper (Europe PMC); 74 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 34 matches between paragraphs and lines of code.

joonho345/Epilepsy_Rodent_Model_RNA

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: fbb6f9a1d61f605ed3b2e05cec743d17d59cf244, 17 February 2026
Languages: R (98), Shell (28), Jupyter (3)
Size: 133 files, 129 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (58 files), ggplot2 (48 files), STAR (18 files), DESeq2 (15 files), SAMtools (15 files), ComplexHeatmap (9 files), pheatmap (8 files), circlize (6 files), Seurat (6 files), anndata (3 files), NumPy (3 files), pandas (3 files), Scanpy (3 files), Matplotlib (2 files), reshape2 (2 files), ggpubr (1 file), h5py (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
131 files
At the source: github.com/joonho345/Epilepsy_Rodent_Model_RNA

Zenodo 19640367

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (58 files), ggplot2 (48 files), STAR (18 files), DESeq2 (15 files), SAMtools (15 files), ComplexHeatmap (9 files), pheatmap (8 files), circlize (6 files), Seurat (6 files), anndata (3 files), NumPy (3 files), pandas (3 files), Scanpy (3 files), Matplotlib (2 files), reshape2 (2 files), ggpubr (1 file), h5py (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
131 files
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Read it in the paper: doi.org/10.1038/s41467-026-73796-5.

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Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 17 MeSH terms, 3 funders, 73 references.

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Kim, J., Lee, S., Kim, B., Jeong, K. H., Park, S., Cho, S., Kang, H.-C., Kim, W.-J., & Kim, S. (2026). Cross-species transcriptomic analysis of rodent model fidelity to human mesial temporal lobe epilepsy. Nature communications, 17(1), 7104. https://doi.org/10.1038/s41467-026-73796-5

BibTeX

@article{kim2026cross,
author = {Kim, Joonho and Lee, Sangbo and Kim, Bora and Jeong, Kyoung Hoon and Park, Soojin and Cho, Soomi and Kang, Hoon-Chul and Kim, Won-Joo and Kim, Sangwoo},
title = {{Cross-species transcriptomic analysis of rodent model fidelity to human mesial temporal lobe epilepsy}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7104},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73796-5},
url = {https://doi.org/10.1038/s41467-026-73796-5},
pmid = {42230582},
pmcid = {PMC13392440}
}

RIS

TY - JOUR
AU - Kim, Joonho
AU - Lee, Sangbo
AU - Kim, Bora
AU - Jeong, Kyoung Hoon
AU - Park, Soojin
AU - Cho, Soomi
AU - Kang, Hoon-Chul
AU - Kim, Won-Joo
AU - Kim, Sangwoo
TI - Cross-species transcriptomic analysis of rodent model fidelity to human mesial temporal lobe epilepsy
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/02
VL - 17
IS - 1
SP - 7104
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73796-5
UR - https://doi.org/10.1038/s41467-026-73796-5
LA - en
ER -

CSL-JSON

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"page": "7104",
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"language": "en",
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