OSCR

Dissecting genotype-specific effects of disease-associated genetic variants.

Code ↔ Paper

3 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 3 matches
  1. [1] § STAR★Methods › Method details › ONT long-read DNA sequencing › High molecular weight (HMW) DNA extraction, library preparation and sequencing ↔ python/pod5/src/pod5/pod5_types.py, lines 136–238 · score 0.78 · MinKNOW, PromethION, flow cells, sequencing kit, Nanopore
  2. [2] § STAR★Methods › Method details › RNA extraction, sequencing and data analysis › RNA sequencing data analysis ↔ CRISPR_Tx_plots.R, lines 262–343 · score 0.68 · iPSC, IDO1, UTF1, ZIC1, NANOG, OCT4
  3. [3] § STAR★Methods › Method details › RNA extraction, sequencing and data analysis › RNA sequencing data analysis ↔ CRISPR_Tx_plots.R, lines 891–976 · score 0.60 · log2 fold change, Log2FC, v1, v2, seq, genes

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 976 lines · 41 KB · no license · 2 matches

  1. library("DESeq2")
  2. library(tidyverse)
  3. library("ggpubr")
  4. library(gridExtra)
  5. library("RColorBrewer")
  6. library(Manu)
  7. library("ggbeeswarm")
  8. library("readxl")
  9. library("UpSetR")
  11. # MA plots ----------------------------------------------------------------
  13. # https://rpkgs.datanovia.com/ggpubr/reference/ggmaplot.html
  15. t5_all <- read_tsv("../../../120623/edited_ipsc_vs_other_edited_ipsc/t5_ipsc_vs_other_ipsc_lfcshrk0_p0.05.tsv")
  16. t5_all_ma <- ggmaplot(t5_all, main = expression("rs11610045 vs background"),
  17. fdr = 0.05, fc = 2, size =0.4, #log2FC = 1
  18. palette = c("#B31B21", "#1465AC", "darkgray"),
  19. genenames = as.vector(t5_all$gene),
  20. legend = "none", top = 0,
  21. font.label = c("bold", 11),
  22. font.legend = "bold",
  23. font.main = "bold",
  24. ggtheme = ggplot2::theme_minimal(),
  25. label.select = c("ARHGAP22","CNTNAP2","TXNRD1"))
  26. ggsave("../plots/MA/t5_vs_allipsc.pdf",t5_all_ma, width = 6, height = 4)
  28. t5_all_1 <- t5_all %>%
  29. filter(log2FoldChange > 1 | log2FoldChange < -1)
  31. write.table(t5_all, "~/Library/CloudStorage/OneDrive-TheUniversityofAuckland/myhome (files.auckland.ac.nz)/Liggins_PD/Manuscripts/CRISPR_iPSC_Tx/t5_all_suptab2.txt", row.names = FALSE, sep = "\t")
  33. # Neurons
  35. t5N_WTN <- read_tsv("editN_vs_WTN/t5N_vs_WTN_lfcshrk0_p0.01.tsv")
  37. t5N_WTN_ma <- ggmaplot(t5N_WTN, main = expression("T5 neuron vs WT neuron"),
  38. fdr = 0.01, fc = 2, size =0.4, #log2FC = 1
  39. palette = c("#B31B21", "#1465AC", "darkgray"),
  40. genenames = as.vector(t5N_WTN$gene),
  41. legend = "right", top = 0,
  42. font.label = c("bold", 11),
  43. font.legend = "bold",
  44. font.main = "bold",
  45. ggtheme = ggplot2::theme_minimal())
  47. ggsave("../plots/MA/t5n_WTN_ma.pdf", width = 6, height = 4)
  49. # WT vs WT nuc
  51. WT_WTNF <- read_tsv("nfWT_vs_WT_lfcshrk0_p0.01.tsv")
  53. WT_WTNF_ma <- ggmaplot(WT_WTNF, main = expression("WT nucleofected vs WT"),
  54. fdr = 0.01, fc = 2, size =0.4, #log2FC = 1,
  55. palette = c("#B31B21", "#1465AC", "darkgray"),
  56. genenames = as.vector(WT_WTNF$gene),
  57. legend = "right", top = 0,
  58. font.label = c("bold", 11),
  59. font.legend = "bold",
  60. font.main = "bold",
  61. ggtheme = ggplot2::theme_minimal())
  63. ggsave("../plots/MA/WT_WTNF_ma.pdf", width = 6, height = 4)
  65. # Volcano plots -----------------------------------------------------------
  67. # T5 volcano
  69. t5_iPSC_full <- read_tsv("../../lfcshrink_full_310724/edited_ipsc_vs_others_lfcshrink_full/t5_ipsc_vs_other_ipsc_lfcshrk_full.tsv")
  71. t5_iPSC_full$DE <- "NS"
  72. t5_iPSC_full$DE[t5_iPSC_full$log2FoldChange > 1 & t5_iPSC_full$padj<0.05] <- "Up"
  73. t5_iPSC_full$DE[t5_iPSC_full$log2FoldChange < -1 & t5_iPSC_full$padj<0.05] <- "Down"
  74. t5_top <- t5_iPSC_full %>%
  75. arrange(padj) %>%
  76. filter(!is.na(padj) & padj < 0.05) %>%
  77. filter(log2FoldChange > 1 | log2FoldChange < -1) %>%
  78. top_n(-10, padj)
  80. t5_volc <- ggplot(t5_iPSC_full, aes(x = log2FoldChange, y = -log10(padj), color = DE)) +
  81. geom_point(size = 0.5) +
  82. theme_minimal() +
  83. scale_color_manual(values = c("#1465AC", "darkgray", "#B31B21")) +
  84. geom_vline(xintercept = c(-1,1), col = "#333", linetype = "dashed") +
  85. geom_hline(yintercept = -log10(0.05), col = "#333", linetype = "dashed") +
  86. labs(title = "rs11610045 (Edit vs. background)") +
  87. theme(
  88. text = element_text(size = 10),
  89. plot.title = element_text(
  90. #family = "Arial", # Change to a standard font family (e.g., "Times", "Arial", "Helvetica")
  91. face = "plain", # Font face (e.g., "plain", "italic", "bold", "bold.italic")
  92. size = 10, # Font size
  93. color = "black" # Font color
  94. ),
  95. legend.position = "none"
  96. ) +
  97. geom_text(data = t5_top, aes(label = gene), vjust = 1, hjust = 1, size = 2.5)
  98. ggsave("~/Library/CloudStorage/OneDrive-TheUniversityofAuckland/myhome (files.auckland.ac.nz)/Liggins_PD/Manuscripts/CRISPR_iPSC_Tx/Figures/t5_iPSC_volcano.pdf", plot = t5_volc, width = 6, height = 5)
  100. # T5 neuron volcano
  102. t5_neuron_full <- read_tsv("../../lfcshrink_full_310724/edited_neurons_vs_other_lfcshrink_full/t5_neurons_vs_other_neurons_lfcshrk_full.tsv")
  103. t5_neuron_full$DE <- "NS"
  104. t5_neuron_full$DE[t5_neuron_full$log2FoldChange >1 & t5_neuron_full$padj < 0.05] <- "Up"
  105. t5_neuron_full$DE[t5_neuron_full$log2FoldChange < -1 & t5_neuron_full$padj < 0.05] <- "Down"
  106. t5N_top <- t5_neuron_full %>%
  107. arrange(padj) %>%
  108. filter(!is.na(padj) & padj < 0.05) %>%
  109. filter(log2FoldChange > 1 | log2FoldChange < -1) %>%
  110. top_n(-10, padj)
  112. t5N_volc <- ggplot(t5_neuron_full, aes(x = log2FoldChange, y = -log10(padj), color = DE)) +
  113. geom_point(size = 0.5) +
  114. theme_minimal() +
  115. scale_color_manual(values = c("#1465AC", "darkgray", "#B31B21")) +
  116. geom_vline(xintercept = c(-1,1), col = "#333", linetype = "dashed") +
  117. geom_hline(yintercept = -log10(0.05), col = "#333", linetype = "dashed") +
  118. theme(
  119. text = element_text(size = 10),
  120. plot.title = element_text(
  121. family = "Arial", # Change to a standard font family (e.g., "Times", "Arial", "Helvetica")
  122. face = "plain", # Font face (e.g., "plain", "italic", "bold", "bold.italic")
  123. size = 10, # Font size
  124. color = "black" # Font color
  125. ),
  126. legend.position = "none"
  127. ) +
  128. geom_text(data = t5N_top, aes(label = gene), vjust = 1, hjust = 1, size = 2.5)
  130. # Key gene & reversal intersect -------------------------------------------
  132. # T5 key gene & reversal
  134. t5_rvs_0 <- read_tsv("reverse_vs_targets/t5rev_vs_t5_lfcshrk0_p0.01.tsv") #LINC00882 is DEG in logFC > 1 but not logFC > 0 ??
  135. t5_rvs_1 <- read_tsv("reverse_vs_targets/t5rev_vs_t5_lfcshrk1_p0.01.tsv")
  137. t5_rvs_key_overlap_0 <- Reduce(intersect, list(t5_unique$gene, t5_rvs_0$gene))
  138. t5_rvs_key_overlap_1 <- Reduce(intersect, list(t5_unique$gene, t5_rvs_1$gene))
  139. t5_rvs_all <- setdiff(Reduce(intersect, list(t5_genes$gene, t5_rvs_1$gene)), c("ADARB2", "NEGR1")) #ADARB2 - off target effect
  141. write.table(t5_rvs_key_overlap_0, file = "Key_genes/t5_rvs0_key_overlap.txt", sep = "\t", row.names = FALSE)
  143. write.table(t5_rvs_key_overlap_1, file = "Key_genes/t5_rvs1_key_overlap.txt", sep = "\t", row.names = FALSE)
  145. t5_key_rvs <- t5_unique[t5_unique$gene %in% t5_rvs_key_overlap_1,]
  146. t5_key_rvs <- t5_genes[t5_genes$gene %in% t5_rvs_all,]
  148. # Group 2 T5 reversal -----------------------------------------------------
  150. g2_T5_0 <- read_tsv("../../../rna_seq_analysis_2324/targeted_analysis/results/group2/t5_vs_all_lfcshrk0_p0.01.tsv")
  151. g2_T5_1 <- read_tsv("../../../rna_seq_analysis_2324/targeted_analysis/results/group2/t5_vs_all_lfcshrk1_p0.01.tsv")
  153. t5_rvs_0 <- read_tsv("reverse_vs_targets/t5rev_vs_t5_lfcshrk0_p0.01.tsv") #LINC00882 is DEG in logFC > 1 but not logFC > 0 ??
  154. t5_rvs_1 <- read_tsv("reverse_vs_targets/t5rev_vs_t5_lfcshrk1_p0.01.tsv")
  156. g2_t5_rvs_key_overlap_0 <- Reduce(intersect, list(g2_T5_0$gene, t5_rvs_0$gene))
  157. g2_t5_rvs_key_overlap_1 <- Reduce(intersect, list(g2_T5_1$gene, t5_rvs_1$gene))
  158. g2_t5_rvs_key_overlap_1_unique <- Reduce(intersect, list(g2_T5_1$gene, t5_rvs_1$gene, t5_unique$gene))
  160. g2_t5_key_rvs <- g2_T5_1[g2_T5_1$gene %in% g2_t5_rvs_key_overlap_1,]
  161. g2_t5_key_rvs_unique <- g2_T5_1[g2_T5_1$gene %in% g2_t5_rvs_key_overlap_1_unique,]
  163. T5_all_vs_g2 <- Reduce(intersect, list(t5_all_1$gene, g2_T5_1$gene))
  165. # Bar plots gene reversal -------------------------------------------------
  167. # T5
  169. t5_bar <- t5_key_rvs[,-c(2,4,5,6,7)]
  171. t5_bar_mean <- aggregate(log2FoldChange~gene,t5_bar,mean)
  172. t5_bar_mean$target <- 'T5'
  174. ggplot(t5_bar_mean, aes(x = gene, y = log2FoldChange, fill = gene)) +
  175. geom_bar(stat = "identity", width = 0.8, fill = "grey") +
  176. theme_minimal() +
  177. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  178. labs(title = "T5 key genes")
  180. t5_rvs_bar <- t5_rvs_0[t5_rvs_0$gene %in% t5_rvs_key_overlap_0,]
  181. t5_rvs_bar <- t5_rvs_bar[, -c(2,4,5,6)]
  182. t5_rvs_bar$target <- 'T5_RVS'
  184. ggplot(t5_rvs_bar, aes(x = gene, y = log2FoldChange, fill = gene)) +
  185. geom_bar(stat = "identity", width = 0.8, fill = "grey") +
  186. theme_minimal() +
  187. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  188. labs(title = "T5 key genes")
  190. t5_both <- rbind(t5_bar_mean, t5_rvs_bar)
  191. t5_both <- t5_both[t5_both$gene %in% t5_both$gene[duplicated(t5_both$gene)],]
  192. t5_both <- t5_both[t5_both$gene != "ENSG00000267745",] #these genes removed as likely pluripotent DEG
  193. t5_both <- t5_both[t5_both$gene != "POU3F4",]
  195. ggplot(t5_both, aes(x = gene, y = log2FoldChange, fill = target)) +
  196. geom_bar(stat = "identity", position = position_dodge(width = 0), width = 0.8) +
  197. theme_minimal() +
  198. scale_fill_manual(values = get_pal("Korora")) +
  199. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  200. labs(title = "T5 key genes")
  201. ggsave("../plots/T5_key_log2fc_bar.pdf", width = 6, height = 4)
  203. # Group 2 T5
  205. g2_t5_bar <- g2_t5_key_rvs[,-c(2,4,5,6)]
  206. g2_t5_bar_unique <- g2_t5_key_rvs_unique[,-c(2,4,5,6)]
  207. g2_t5_bar_unique$target <- 'T5'
  209. g2_t5_bar_mean <- aggregate(log2FoldChange~gene,g2_t5_bar,mean)
  210. g2_t5_bar_mean$target <- 'T5'
  212. ggplot(g2_t5_bar, aes(x = gene, y = log2FoldChange, fill = gene)) +
  213. geom_bar(stat = "identity", width = 0.8, fill = "grey") +
  214. theme_minimal() +
  215. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  216. labs(title = "T5 key genes")
  218. ggplot(g2_t5_bar_unique, aes(x = gene, y = log2FoldChange, fill = gene)) +
  219. geom_bar(stat = "identity", width = 0.8, fill = "grey") +
  220. theme_minimal() +
  221. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  222. labs(title = "T5 key genes")
  224. g2_t5_rvs_bar <- t5_rvs_1[t5_rvs_1$gene %in% g2_t5_rvs_key_overlap_1,]
  225. g2_t5_rvs_bar <- g2_t5_rvs_bar[, -c(2,4,5,6)]
  226. g2_t5_rvs_bar$target <- 'T5_RVS'
  228. ggplot(g2_t5_rvs_bar, aes(x = gene, y = log2FoldChange, fill = gene)) +
  229. geom_bar(stat = "identity", width = 0.8, fill = "grey") +
  230. theme_minimal() +
  231. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  232. labs(title = "T5 key genes")
  234. g2_t5_both <- rbind(g2_t5_bar_mean, g2_t5_rvs_bar)
  235. g2_t5_both <- g2_t5_both[g2_t5_both$gene %in% g2_t5_both$gene[duplicated(g2_t5_both$gene)],]
  236. g2_t5_both <- g2_t5_both[g2_t5_both$gene != "ENSG00000267745",] #these genes removed as likely pluripotent DEG
  237. g2_t5_both <- g2_t5_both[g2_t5_both$gene != "POU3F4",]
  239. ggplot(g2_t5_both, aes(x = gene, y = log2FoldChange, fill = target)) +
  240. geom_bar(stat = "identity", position = position_dodge(width = 0), width = 0.8) +
  241. theme_minimal() +
  242. scale_fill_manual(values = get_pal("Korora")) +
  243. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size = 4)) +
  244. labs(title = "T5 key genes")
  246. ggsave("../plots/g2_T5_key_log2fc_bar.pdf", width = 8, height = 4)
  248. write.table(g2_t5_both, file = "Key_genes/G2_T5_reversed.txt", sep = "\t", row.names = FALSE)
  250. g2_t5_both_uniq <- rbind(g2_t5_bar_unique, g2_t5_rvs_bar)
  251. g2_t5_both_uniq <- g2_t5_both_uniq[g2_t5_both_uniq$gene %in% g2_t5_both_uniq$gene[duplicated(g2_t5_both_uniq$gene)],]
  252. #g2_t5_both_uniq <- g2_t5_both[g2_t5_both$gene != "ENSG00000267745",] #these genes removed as likely pluripotent DEG
  253. #g2_t5_both_uniq <- g2_t5_both[g2_t5_both$gene != "POU3F4",]
  255. ggplot(g2_t5_both_uniq, aes(x = gene, y = log2FoldChange, fill = target)) +
  256. geom_bar(stat = "identity", position = position_dodge(width = 0), width = 0.8) +
  257. theme_minimal() +
  258. scale_fill_manual(values = get_pal("Korora")) +
  259. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  260. labs(title = "T5 key genes")
  262. # iPDGC neuron DEG log2FC plots -------------------------------------------
  264. # T5 neuron
  266. t5_iPDGC <- read.table("edit_neuron_vs_WTN/T5_neuron_iPDGC.txt", header = TRUE, sep = "\t")
  268. t5_iPDGC_neuron_bar <- ggplot(t5_iPDGC, aes(x = gene, y = log2FoldChange, fill = gene)) +
  269. geom_bar(stat = "identity", width = 0.8, fill = "#85BEDC") +
  270. #scale_fill_manual(values = rep(get_pal("Kotare"),2)) +
  271. #scale_fill_brewer(palette = "BrBG") +
  272. theme_minimal() +
  273. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
  274. ggsave("../plots/t5_iPDGC_neuron_bar.pdf", width = 6, height = 4)
  276. # iPSC Micro-C TPM --------------------------------------------------------
  278. t5_TPM <- read.table("targets_vs_nfwt/T5_microC_genes_TPM.txt", header = TRUE, sep = "\t")
  280. t5_TPM_long <- t5_TPM %>%
  281. gather(target, TPM, T5.4:NFWT.6, factor_key = TRUE)
  282. t5_TPM_long$target <- sub("T.*","T5", t5_TPM_long$target)
  283. t5_TPM_long$target <- sub("NFWT.*","NFWT",t5_TPM_long$target)
  285. t5_microC_TPM <- ggplot(t5_TPM_long, aes(x = target, y = TPM, fill = target)) +
  286. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  287. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  288. scale_fill_manual(values = c("grey","#85BEDC")) +
  289. facet_grid(. ~ Gene) +
  290. theme_bw() +
  291. theme(legend.position = "none")
  292. ggsave("../plots/t5_microC_TPM.pdf", width = 5, height = 4)
  294. # Pluripotency Markers --------------------------------------------------------
  296. rs116_TPM <- read_csv("../../rsem/Pluripotency/rs11610045_pluri_TPM.csv")
  298. rs116_TPM$state <- factor(rs116_TPM$state, levels = c("Background", "Target", "RVS"))
  300. proliferation <- rs116_TPM[rs116_TPM$Gene %in% c("SOX2", "NANOG", "SALL4", "ZIC1", "UTF1"),]
  302. proliferation_bar <- ggplot(proliferation, aes(x = state, y = TPM, fill = state)) +
  303. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  304. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.0) +
  305. scale_fill_manual(values = c("#0D4A80","#9CCB86","#355e3b")) +
  306. facet_grid(. ~ Gene) +
  307. theme_bw() +
  308. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none")
  310. proliferation_bar
  311. ggsave("../../2025_plots/rs116_pluri_TPM.pdf", width = 10, height = 4)
  313. OCT4 <- rs116_TPM[rs116_TPM$Gene %in% c("OCT4"),]
  314. OCT4_bar <- ggplot(OCT4, aes(x = state, y = TPM, fill = state)) +
  315. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  316. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  317. scale_fill_manual(values = c("#0D4A80","#9CCB86","#355e3b")) +
  318. facet_grid(. ~ Gene) +
  319. theme_bw() +
  320. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none") +
  321. ylim(0,3500)
  322. OCT4_bar
  323. ggsave("../../2025_plots/rs116_OCT4_TPM.pdf", width = 3, height = 5)
  325. states <- rs116_TPM[rs116_TPM$Gene %in% c("UTF1","ZIC1","NANOG","IDO1"),]
  327. states_bar <- ggplot(states, aes(x = Gene, y = TPM, fill = Gene)) +
  328. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  329. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  330. scale_fill_manual(values = get_pal("Kotare")) +
  331. theme_bw() +
  332. theme(legend.position = "none")
  333. ggsave("../../2025_plots/UTF1_ZIC1_bar.pdf", width = 7, height = 4)
  335. states_line <- ggplot(states, aes(x = Gene, y = TPM, group = `Target ID`)) +
  336. geom_line(position = position_dodge(0.1), colour = "grey") +
  337. geom_point(aes(fill = Gene), colour = "black", pch = 21, size = 2, position=position_dodge(0.1)) +
  338. scale_fill_manual(values = get_pal("Kotare")) +
  339. theme_bw() +
  340. theme(legend.position = "none")
  342. states_line
  343. ggsave("../../2025_plots/UTF1_ZIC1_line.pdf", width = 7, height = 4)
  345. # rs116 (and rvs) vs background
  347. all_TPM <- read_csv("../../rsem/Pluripotency/all_pluri_TPM.csv")
  349. all_TPM$state <- factor(all_TPM$State, levels = c("Background", "Target", "RVS"))
  351. proliferation <- all_TPM[all_TPM$Gene %in% c("SOX2", "NANOG", "SALL4", "ZIC1", "UTF1"),]
  353. proliferation_bar <- ggplot(proliferation, aes(x = State, y = TPM, fill = State)) +
  354. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  355. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.0) +
  356. scale_fill_manual(values = c("#0D4A80","#9CCB86","#355e3b")) +
  357. facet_grid(. ~ Gene) +
  358. theme_bw() +
  359. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none")
  361. proliferation_bar
  362. ggsave("../../2025_plots/all_pluri_TPM.pdf", width = 10, height = 4)
  364. OCT4 <- all_TPM[all_TPM$Gene %in% c("OCT4"),]
  365. OCT4_bar <- ggplot(OCT4, aes(x = State, y = TPM, fill = State)) +
  366. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  367. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  368. scale_fill_manual(values = c("#0D4A80","#9CCB86","#355e3b")) +
  369. facet_grid(. ~ Gene) +
  370. theme_bw() +
  371. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none") +
  372. ylim(0,3500)
  373. OCT4_bar
  374. ggsave("../../2025_plots/all_OCT4_TPM.pdf", width = 3, height = 5)
  376. states <- all_TPM[all_TPM$Gene %in% c("UTF1","ZIC1","NANOG","IDO1"),]
  378. states_bar <- ggplot(states, aes(x = Gene, y = TPM, fill = Gene)) +
  379. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  380. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  381. scale_fill_manual(values = get_pal("Kotare")) +
  382. theme_bw() +
  383. theme(legend.position = "none")
  384. ggsave("../../2025_plots/all_UTF1_ZIC1_bar.pdf", width = 7, height = 4)
  386. states_line <- ggplot(states, aes(x = Gene, y = TPM, group = `Target ID`)) +
  387. geom_line(position = position_dodge(0.1), colour = "grey") +
  388. geom_point(aes(fill = Gene), colour = "black", pch = 21, size = 2, position=position_dodge(0.1)) +
  389. scale_fill_manual(values = get_pal("Kotare")) +
  390. theme_bw() +
  391. theme(legend.position = "none")
  392. ggsave("../../2025_plots/all_UTF1_ZIC1_line.pdf", width = 7, height = 4)
  394. # Neuronal Markers --------------------------------------------------------
  396. neuron <- read.table("Neuronal_markers/neuronal_markers.txt", header = TRUE, sep = "\t") %>%
  397. mutate(State = "Neuron")
  398. neuron_noTUBB <- neuron[neuron$Gene %in% c("MAP2","POU3F2","SNAP25","SYN1"),]
  400. neuron_bar <- ggplot(neuron, aes(x = Gene, y = TPM, fill = Gene)) +
  401. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  402. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  403. scale_fill_manual(values = get_pal("Kakapo")) +
  404. #ylab("log10 expression (TPM)") +
  405. theme_bw() +
  406. theme(legend.position = "none")
  408. neuron_new <- read.table("Neuronal_markers/neuron_markers_280624.txt", header = TRUE, sep = "\t") %>%
  409. mutate(State = "Neuron")
  410. nn_noTUBB <- neuron_new[neuron_new$Gene %in% c("MAP2","POU3F2","SNAP25","SYN1"),]
  412. neuron_new$Target <- sub("\\.genes.*", "", neuron_new$Target)
  413. colnames(neuron_new)[1] <- "seq_ID"
  414. neuron_ID <- read.table("../../rsem/output/Neuron_RNAseqID_T5.txt", header = TRUE, sep = "\t")
  415. neuron_new <- neuron_new %>%
  416. inner_join(neuron_ID)
  418. neuron_new_bar <- neuron_new %>%
  419. filter(Edit %in% c("BG", "T5")) %>%
  420. ggplot(aes(x = Gene, y = TPM, fill = Edit)) +
  421. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  422. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  423. scale_fill_manual(
  424. values = c("BG" = "#CABEE9", "T5" = "#E88471"),
  425. labels = c("BG" = "Background", "T5" = "Target")
  426. ) +
  427. ylab("log10 expression (TPM)") +
  428. theme_bw() +
  429. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "right")
  430. neuron_new_bar + scale_y_continuous(trans='log10') # only if want to log transform the y axis
  432. neuron_new_bar <- ggplot(neuron_new, aes(x = Gene, y = TPM, fill = Edit)) +
  433. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  434. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  435. scale_fill_manual(values = c(colours <- c("#CF597E","#009392","#9CCB86","#E9E29C","#E88471","#0D4A80"))) +
  436. ylab("log10 expression (TPM)") +
  437. theme_bw() +
  438. theme(legend.position = "right")
  440. neuron_new_bar + scale_y_continuous(trans='log10') #only did if want to log transform the y axis
  441. ggsave("../plots/Neuron_marker_targets_bar_log10.pdf", width = 7, height = 4)
  443. neuron_line <- neuron_new[neuron_new$Gene %in% c("MAP2","TUBB3"),]
  444. neuron_line <- ggplot(neuron_new, aes(x = Gene, y = TPM, group = seq_ID, fill = Edit)) +
  445. geom_line(position = position_dodge(0.1), colour = "#565052", alpha = 0.4) +
  446. geom_point(aes(fill = Edit), colour = "black", pch = 21, size = 2, position=position_dodge(0.1)) +
  447. scale_fill_manual(values = c(colours <- c("#CF597E","#009392","#9CCB86","#E9E29C","#E88471","#0D4A80"))) +
  448. ylab("log10 expression (TPM)") +
  449. theme_bw() +
  450. theme(legend.position = "right")
  451. neuron_line + scale_y_continuous(trans='log10')
  453. neuron_line <- neuron_new[neuron_new$Gene %in% c("MAP2","TUBB3"),]
  454. neuron_line <- neuron_new %>%
  455. filter(Edit %in% c("BG", "T5")) %>%
  456. ggplot(aes(x = Gene, y = TPM, group = seq_ID, fill = Edit)) +
  457. geom_line(position = position_dodge(0.1), colour = "#565052", alpha = 0.4) +
  458. geom_point(aes(fill = Edit), colour = "black", pch = 21, size = 2, position=position_dodge(0.1)) +
  459. scale_fill_manual(
  460. values = c("BG" = "#CABEE9", "T5" = "#E88471"),
  461. labels = c("BG" = "Background", "T5" = "Target")
  462. ) +
  463. ylab("log10 expression (TPM)") +
  464. theme_bw() +
  465. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "right")
  466. neuron_line + scale_y_continuous(trans='log10')
  468. # Target 5 iPSC vs reverted ------------------------------------------------
  470. t5_all <- read.table("../../../120623/edited_ipsc_vs_other_edited_ipsc/t5_ipsc_vs_other_ipsc_lfcshrk0_p0.05.tsv", header = TRUE)
  471. t5_all <- t5_all[,-c(2,4,5,6,7)]
  472. t5_all$target <- 'T5'
  473. t5_all <- t5_all %>% filter(log2FoldChange > 1 | log2FoldChange < -1)
  474. t5_all_OT <- t5_all[!(t5_all$gene %in% T5_OT$Genes),]
  476. write.table(t5_all, "t5_all.txt", sep = "\t")
  477. write.table(t5_all_OT, "t5_all_OT.txt", sep = "\t", row.names = FALSE)
  479. t5_rvs_0 <- read_tsv("reverse_vs_targets/t5rev_vs_t5_lfcshrk0_p0.01.tsv")
  480. t5_rvs_0 <- t5_rvs_0[,-c(2,4,5,6)]
  481. t5_rvs_0$target <- 'T5_RVS'
  482. t5_rvs_1 <- read_tsv("reverse_vs_targets/t5rev_vs_t5_lfcshrk1_p0.01.tsv")
  483. t5_rvs_1 <- t5_rvs_1[,-c(2,4,5,6)]
  484. t5_rvs_1$target <- 'T5_RVS'
  486. t5_rvs_all_overlap_1 <- Reduce(intersect, list(t5_all_OT$gene, t5_rvs_1$gene))
  488. t5_rvs_all <- rbind(t5_all_OT, t5_rvs_1)
  490. t5_rvs_bar <- t5_rvs_1[t5_rvs_1$gene %in% t5_rvs_all_overlap_1,]
  492. t5_both <- rbind(t5_all_OT, t5_rvs_bar)
  493. t5_both <- t5_both[t5_both$gene %in% t5_both$gene[duplicated(t5_both$gene)],]
  494. t5_both <- t5_both[t5_both$gene != "NEGR1",]
  496. ref_gene <- read.table("../../gene_reference_sorted.txt", header = TRUE)
  497. colnames(ref_gene)[5] = "gene"
  499. T5_both_coords <- merge(x = t5_both, y = ref_gene, by = "gene", all.x = TRUE)
  501. write.table(T5_both_coords, file = "../../../120623/edited_ipsc_vs_other_edited_ipsc/T5_vs_all_RVS.txt", sep = "\t", row.names = FALSE)
  503. t5_both_ordered <- t5_both %>%
  504. arrange(target, desc(log2FoldChange)) %>%
  505. mutate(gene = factor(gene, levels = unique(gene)))
  507. ggplot(t5_both_ordered, aes(x = gene, y = log2FoldChange, fill = target)) +
  508. geom_bar(stat = "identity", position = position_dodge(width = 0), width = 0.8) +
  509. theme_minimal() +
  510. scale_fill_manual(values = c("#9CCB86","#0D4A80")) +
  511. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size=6)) +
  512. theme(legend.position = "none") +
  513. geom_hline(yintercept = c(1,-1),
  514. color = "black", linewidth = 0.3) +
  515. scale_y_continuous(breaks = seq(floor(min(t5_both_ordered$log2FoldChange)), ceiling(max(t5_both_ordered$log2FoldChange)), by = 1))
  517. ggsave("../plots/T5_vs_alliPSC_RVS_ordered.pdf", width = 10, height = 6)
  519. # Specific genes TPM iPSC------------------------------------------------------
  521. iPSC_ID <- read.table("../../rsem/output/iPSC/iPSC_ID.txt", header = TRUE, sep = "\t")
  523. T5_keygenes <- read.table("../../rsem/output/iPSC/T5_keygenes_TPM.txt", header = TRUE, sep = "\t")
  524. T5_integrin <- T5_keygenes[T5_keygenes$Gene != "PDGFB",]
  526. T5_order <- c("WTNF", "T5", "T5-RVS")
  527. T5_keygenes$State <- factor(T5_keygenes$State, levels = T5_order)
  529. T5_keygenes_TPM_bar <- ggplot(T5_keygenes, aes(x = State, y = TPM, fill = State)) +
  530. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  531. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.0) +
  532. scale_fill_manual(values = c("#0D4A80","#9CCB86","#355e3b")) +
  533. facet_grid(. ~ Gene) +
  534. ylab("log10 expression (TPM)") +
  535. theme_bw() +
  536. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none")
  537. T5_keygenes_TPM_bar + scale_y_continuous(trans='log10')
  538. ggsave("../plots/T5_keygenes_TPM_bar.pdf", width = 8, height = 4)
  540. T5_order <- c("WTNF", "T5", "T5-RVS")
  541. T5_integrin$State <- factor(T5_integrin$State, levels = T5_order)
  543. T5_integrin_TPM <- ggplot(T5_integrin, aes(x = State, y = TPM, fill = State)) +
  544. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  545. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.0) +
  546. scale_fill_manual(values = c("#0D4A80","#9CCB86","#355e3b")) +
  547. facet_grid(. ~ Gene) +
  548. ylab("log10 expression (TPM)") +
  549. theme_bw() +
  550. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none")
  551. T5_integrin_TPM + scale_y_continuous(trans='log10')
  552. ggsave("../plots/T5_integrin_TPM_bar.pdf", width = 10, height = 5)
  554. microC_TPM <- read.table("../../rsem/output/iPSC/t5_microC_tpm.txt", sep = "\t")
  555. microC_TPM$V1 <- sub("\\.genes.*", "", microC_TPM$V1)
  556. colnames(microC_TPM)[1] <- "seq_ID"
  557. microC_TPM <- microC_TPM %>%
  558. inner_join(iPSC_ID)
  559. microC_TPM_T5 <- microC_TPM %>% filter(Edit == "T5" | Edit == "WTNF")
  560. microC_arhgap22 <- microC_TPM %>% filter(V2 == "ARHGAP22")
  562. # grouping T5 vs. all others as background
  563. microC_group <- microC_TPM %>%
  564. mutate(state = case_when(
  565. Edit == "T5" ~ "Target",
  566. Edit == "T5-RVS" ~ "RVS",
  567. TRUE ~ "Background"
  568. ))
  569. microC_arhgap22 <- microC_group %>% filter(V2 == "ARHGAP22")
  571. #ordering x-axis on plot
  572. microC_arhgap22$state <- factor(microC_arhgap22$state, levels = c("Background", "Target", "RVS"))
  573. microC_group$state <- factor(microC_group$state, levels = c("Background", "Target", "RVS"))
  575. T5_microC <- ggplot(microC_group, aes(x = state, y = V6, fill = state)) +
  576. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  577. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1) +
  578. scale_fill_manual(values = c("#0D4A80", "#9CCB86", "#355e3b")) +
  579. theme_bw() +
  580. facet_grid(. ~ V2) +
  581. theme(legend.position = "right") +
  582. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  583. xlab("") +
  584. ylab("log10 expression (TPM)")
  585. T5_microC + scale_y_continuous(trans='log10')
  586. ggsave("../plots/T5_microC_3genes.pdf", width = 8, height = 6)
  588. T5_trans <- read.table("../../rsem/output/iPSC/t5_transPD_TPM.txt", header = FALSE, sep = "\t")
  589. T5_trans$V1 <- sub("\\.genes.*", "", T5_trans$V1)
  590. colnames(T5_trans)[1] <- "seq_ID"
  591. colnames(T5_trans)[6] <- "TPM"
  592. colnames(T5_trans)[2] <- "gene"
  593. T5_trans <- T5_trans %>%
  594. inner_join(iPSC_ID)
  596. T5_trans <- T5_trans %>%
  597. mutate(state = case_when(
  598. Edit == "T5" ~ "Target",
  599. Edit == "T5-RVS" ~ "Reverse",
  600. Edit == "WTNF" ~"WTNF",
  601. TRUE ~ "Background"
  602. ))
  603. T5_trans <- T5_trans[!grepl('Background', T5_trans$state),]
  604. T5_trans$gene[T5_trans$gene == 'ENST00000372080.8'] <- 'CEL'
  605. T5_trans$gene[T5_trans$gene == 'ENST00000260356.6,ENST00000397591.2,ENST00000466755.1,ENST00000484734.1,ENST00000490247.1,ENST00000497720.1,ENST00000559746.1,ENST00000560894.1'] <- 'THBS1'
  606. T5_trans$gene[T5_trans$gene == 'ENST00000331163.11,ENST00000381551.8,ENST00000440375.1,ENST00000455790.5'] <- 'PDGFB'
  608. T5_order <- c("WTNF", "Target", "Reverse")
  609. T5_trans$state <- factor(T5_trans$state, levels = T5_order)
  611. T5_trans_plot <- ggplot(T5_trans, aes(x = state, y = TPM, fill = state)) +
  612. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  613. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1) +
  614. scale_fill_manual(values = c("#0D4A80", "#9CCB86", "#355e3b")) +
  615. theme_bw() +
  616. facet_grid(. ~ gene) +
  617. theme(legend.position = "none") +
  618. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  619. xlab("") +
  620. ylab("log10 expression (TPM)")
  621. T5_trans_plot + scale_y_continuous(trans='log10')
  622. ggsave("../plots/T5_trans_TPM.pdf", width = 8, height = 6)
  624. # Neuron DEG TPM --------------------------------------------------------------
  626. neuron_ID <- read.table("../../rsem/output/Neuron_RNAseqID_edit.txt", header = TRUE, sep = "\t")
  628. TMEM175 <- read.table("../../rsem/output/iPSC/TMEM175.txt", sep = "\t")
  630. TMEM175_bar <- ggplot(TMEM175, aes(x = V2, y = V6)) +
  631. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  632. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  633. scale_fill_manual(values = get_pal("Kakapo")) +
  634. theme_bw() +
  635. theme(legend.position = "none")
  636. TMEM175_bar
  638. XPO6 <- read.table("../../rsem/output/rsem_neuron/XPO6_TPM.txt", sep = "\t")
  639. XPO6$V1 <- sub("\\.genes.*", "", XPO6$V1)
  640. colnames(XPO6)[1] <- "seq_ID"
  641. XPO6 <- XPO6 %>%
  642. inner_join(neuron_ID)
  644. XPO6_bar <- ggplot(XPO6, aes(x = Edit, y = V6)) +
  645. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  646. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  647. scale_fill_manual(values = get_pal("Kakapo")) +
  648. theme_bw() +
  649. theme(legend.position = "none")
  650. XPO6_bar
  652. SYNJ1 <- read.table("../../rsem/output/rsem_neuron/SYNJ1_TPM.txt", sep = "\t")
  653. SYNJ1$V1 <- sub("\\.genes.*", "", SYNJ1$V1)
  654. colnames(SYNJ1)[1] <- "seq_ID"
  655. SYNJ1 <- SYNJ1 %>%
  656. inner_join(neuron_ID)
  658. SYNJ1_bar <- ggplot(SYNJ1, aes(x = Edit, y = V6)) +
  659. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  660. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  661. scale_fill_manual(values = get_pal("Kakapo")) +
  662. theme_bw() +
  663. theme(legend.position = "none")
  664. SYNJ1_bar
  666. T5_neuron_dopa <- read.table("../../rsem/output/rsem_neuron/T5_neuron_dopa.txt", sep = "\t")
  667. T5_neuron_dopa$V1 <- sub("\\.genes.*", "", T5_neuron_dopa$V1)
  668. colnames(T5_neuron_dopa)[1] <- "seq_ID"
  669. T5_neuron_dopa <- T5_neuron_dopa %>%
  670. inner_join(neuron_ID)
  672. T5_dopa_group <- T5_neuron_dopa %>%
  673. mutate(state = case_when(
  674. Edit == "T5" ~ "Target",
  675. TRUE ~ "Background"
  676. ))
  678. T5_neuron_dopa_bar <- ggplot(T5_dopa_group, aes(x = state, y = V6, fill = state)) +
  679. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  680. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  681. scale_fill_manual(values = get_pal("Kakapo")) +
  682. facet_grid(. ~ V2) +
  683. xlab("") +
  684. #ylab("log10 expression (TPM)") +
  685. theme_bw() +
  686. theme(legend.position = "none")
  687. T5_neuron_dopa_bar #+ scale_y_continuous(trans='log10')
  688. ggsave("../plots/T5_neuron_dopaminegenes.pdf", width = 8, height = 6)
  690. CNTN1 <- read.table("neuron_vs_targets/CNTN1_TPM.txt", header = TRUE, sep = "\t")
  691. CNTN1_T8 <- CNTN1[CNTN1$Target %in% c("T8","WT"),]
  693. CNTN1_bar <- ggplot(CNTN1_T8, aes(x = Target, y = TPM, fill = Target)) +
  694. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  695. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  696. scale_fill_manual(values = c("#E6007E","grey")) +
  697. theme_bw() +
  698. theme(legend.position = "none") +
  699. xlab("") +
  700. ylab("CNTN1 TPM")
  702. CNTN1_bar
  703. ggsave("../plots/CNTN1_bar.pdf", width = 3, height = 4)
  705. # looking at cis genes for T5 (3)
  707. NFAT2CIP <- read.table("../../rsem/output/rsem_neuron/NFATC2IP_neuron.txt", sep = "\t")
  708. NFAT2CIP$V1 <- gsub(".genes.results:ENSG00000176953.13", "", as.character(NFAT2CIP$V1))
  709. colnames(NFAT2CIP)[1] <- "seq_ID"
  710. NFAT2CIP <- NFAT2CIP %>%
  711. inner_join(neuron_ID)
  713. NFAT2CIP_bar <- ggplot(NFAT2CIP, aes(x = Edit, y = V6, fill = Edit)) +
  714. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  715. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  716. scale_fill_manual(values = c("#CF597E","#009392","#9CCB86","#E9E29C","#E88471","#0D4A80")) +
  717. theme_bw() +
  718. theme(legend.position = "none") +
  719. xlab("Edit") +
  720. ylab("NFAT2CIP TPM")
  721. NFAT2CIP_bar
  723. # Off-target analysis ----------------------------------------------------
  725. T5_OT_SNV <- read_excel("../../OT_analysis_240724/KOLF2_T5_somatic_snvs/KOLF2_T5.wf-somatic-snv.filtered.gene.only-snv.split.xlsx", range = cell_cols(1:15))
  726. T5_OT_indel <- read_excel("../../OT_analysis_240724/KOLF2_T5_somatic_indel/KOLF2_T5.wf-somatic-snv.filtered.gene.only-indel.split.xlsx", range = cell_cols(1:15))
  728. T5_OT <- merge(T5_OT_indel, T5_OT_SNV, all = TRUE)
  729. write.table(T5_OT, "T5_OT.txt", sep = "\t", row.names = FALSE)
  731. T5_OT_genes <- T5_OT[,15, drop = FALSE]
  732. T5_OT_genes <- distinct(T5_OT_genes)
  734. t5_all <- read.table("../../../120623/edited_ipsc_vs_other_edited_ipsc/t5_ipsc_vs_other_ipsc_lfcshrk0_p0.05.tsv", header = TRUE)
  735. t5_all <- t5_all[,-c(2,4,5,6,7)]
  736. t5_all$target <- 'T5'
  738. #keep only genes that are likely OT effects
  739. t5_genes_OT <- t5_all[t5_all$gene %in% T5_OT$Genes, ]
  740. t5_genes_OT_2 <- merge(t5_all, T5_OT, by.x = "gene", by.y = "Genes")
  742. t5_rvs_OT <- rbind(t5_genes_OT, t5_rvs_0)
  743. t5_rvs_OT <- t5_rvs_OT[t5_rvs_OT$gene %in% t5_rvs_OT$gene[duplicated(t5_rvs_OT$gene)],]
  745. ggplot(t5_rvs_OT, aes(x = gene, y = log2FoldChange, fill = target)) +
  746. geom_bar(stat = "identity", position = position_dodge(width = 0), width = 0.5) +
  747. theme_minimal() +
  748. scale_fill_manual(values = c("#9CCB86","#0D4A80")) +
  749. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size=8)) +
  750. theme(legend.position = "none") +
  751. geom_hline(yintercept = c(1,-1),
  752. color = "black", linewidth = 0.3)
  754. ggsave("../plots/T5_off-target_reversed.pdf", width = 5, height = 3)
  756. # Upset plots - T5 off target
  758. T5_OT_indel$Source <- "T5_indel"
  759. T5_OT_SNV$Source <- "T5_SNV"
  760. T5_RVS_OT_indel$Source <- "RVS_indel"
  761. T5_RVS_OT_SNV$Source <- "RVS_SNV"
  763. T5_OT_indel <- T5_OT_indel %>% mutate(QUAL = as.character(QUAL))
  764. T5_OT_SNV <- T5_OT_SNV %>% mutate(QUAL = as.character(QUAL))
  765. T5_RVS_OT_indel <- T5_RVS_OT_indel %>% mutate(QUAL = as.character(QUAL))
  766. T5_RVS_OT_SNV <- T5_RVS_OT_SNV %>% mutate(QUAL = as.character(QUAL))
  768. T5_OT <- bind_rows(T5_OT_indel, T5_OT_SNV, T5_RVS_OT_indel, T5_RVS_OT_SNV)
  770. target_NFWT <- read.table("targets_vs_nfwt/Targets_NFWT_logFC_long.txt", header = TRUE, sep = "\t")
  772. split <- split(T5_OT, f = T5_OT$Source)
  774. targetlist <- list("Introduced Indel" = (split$T5_indel$Genes),
  775. "Introduced SNV" = (split$T5_SNV$Genes),
  776. "Introduced SNV (RVS)" = (split$RVS_SNV$Genes),
  777. "Introduced Indel (RVS)" = (split$RVS_indel$Genes))
  779. OT_overlap <- UpSetR::upset(fromList(targetlist), sets.bar.color = "darkgrey", main.bar.color = "#9CCB86", nsets = 4,
  780. mainbar.y.label = "Overlapping OT effects (#)", #empty.intersections = "on",
  781. order.by = "freq",
  782. point.size = 2.5, line.size = 0.5, matrix.color = "gray7", sets.x.label = "OT effects (#)",
  783. text.scale = c(1.25,1.25,1.05,1.05,1.25,1.25))
  785. count <- as.data.frame(table(T5_OT$Genes))
  787. # TPM plot
  789. T5_OT_genes_TPM <- read.table("../../rsem/output/iPSC/OT_genes_TPM.txt", sep = "\t")
  790. T5_OT_genes_TPM$V1 <- sub("\\.genes.*", "", T5_OT_genes_TPM$V1)
  791. colnames(T5_OT_genes_TPM)[1] <- "seq_ID"
  792. T5_OT_genes_TPM <- T5_OT_genes_TPM %>%
  793. inner_join(iPSC_ID)
  795. T5_OT_genes_TPM <- T5_OT_genes_TPM %>%
  796. mutate(state = case_when(
  797. Edit == "T5" ~ "T5 edit",
  798. Edit == "T5-RVS" ~ "T5 RVS",
  799. TRUE ~ "Background"
  800. ))
  802. T5_order <- c("Background", "T5 edit", "T5 RVS")
  803. T5_OT_genes_TPM$state <- factor(T5_OT_genes_TPM$state, levels = T5_order)
  805. T5_OT_genes <- ggplot(T5_OT_genes_TPM, aes(x = state, y = V6, fill = state)) +
  806. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  807. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  808. scale_fill_manual(values = c("#0D4A80","#9CCB86","#355e3b")) +
  809. facet_grid(. ~ V2) +
  810. theme_bw() +
  811. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none") +
  812. ylab("Log2FC")
  813. ggsave("../plots/VIPR2_TPM.pdf", VIPR2, width = 6, height = 4)
  815. # T5 RVS
  816. T5_RVS_OT_SNV <- read_excel("../../OT_analysis_240724/KOLF2_T5-5_RVS.wf-somatic-snv.filtered.gene.only-snv.split.xlsx", range = cell_cols(1:15))
  817. T5_RVS_OT_indel <- read_excel("../../OT_analysis_240724/KOLF2_T5-5_RVS.wf-somatic-snv.filtered.gene.only-indel.split.xlsx", range = cell_cols(1:15))
  819. T5_RVS_OT <- merge(T5_RVS_OT_indel, T5_RVS_OT_SNV, all = TRUE)
  821. overlap_T5 <- intersect(T5_OT$POS, T5_RVS_OT$POS)
  823. # WT-NF
  824. WTNF_OT_SNV <- read_excel("../../OT_analysis_240724/KOLF2_WT-nucCTRL-A2.wf-somatic-snv.filtered.gene.only-snv.split.xlsx", range = cell_cols(1:15))
  825. WTNF_OT_indel <- read_excel("../../OT_analysis_240724/KOLF2_WT-nucCTRL-A2.wf-somatic-snv.filtered.gene.only-indel.split.xlsx", range = cell_cols(1:15))
  827. WTNF_OT <- merge(WTNF_OT_indel, WTNF_OT_SNV, all = TRUE)
  829. write.table(WTNF_OT, "~/Library/CloudStorage/OneDrive-TheUniversityofAuckland/myhome (files.auckland.ac.nz)/Liggins_PD/Manuscripts/CRISPR_iPSC_Tx/WTNF_OT.txt", row.names = FALSE, sep = "\t")
  832. OTs <- list(WTNF_OT, T5_OT, T7_OT, T6_OT, T8_OT)
  833. overlap <- Reduce(intersect, lapply(OTs, function(df) df$POS))
  834. print_list(overlap)
  836. print_list <- function(lst) {
  837. for (item in lst) {
  838. if (is.character(item)) {
  839. cat(item, "\n")
  840. } else {
  841. cat(item, "\n")
  842. }
  843. }
  844. }
  846. # Methylation - T5 reversed genes -----------------------------------------
  848. T5_DMR <- as.data.frame(read.table("../../Methylation_240724/KOLF2-WT-NF_vs_T5_target-diff-genes-DMRs/annotated_DMRs/dmrs_table_annotated.bed", header = TRUE, sep="\t",stringsAsFactors=FALSE, quote=""))
  849. T5_DMR <- T5_DMR[,-c(4,5,9)]
  850. T5_RVS_DMR <- as.data.frame(read.table("../../Methylation_240724/KOLF2-WT-NF_vs_T5-RVS_target-diff-genes-DMRs/annotated_DMRs/dmrs_table_annotated.bed", header = TRUE, sep="\t",stringsAsFactors=FALSE, quote=""))
  851. T5_RVS_DMR <- T5_RVS_DMR[,-c(1,4,5,8,9,13)]
  853. T5_and_RVS_DMR <- merge(T5_DMR, T5_RVS_DMR, by = c("start", "end", "annotation_chr", "annotation_start", "annotation_end", "biotype", "gene"), all.x = TRUE, all.y = TRUE, suffixes = c("_T5", "_RVS")) %>%
  854. distinct() %>%
  855. mutate(T5_Difference = meanMethy2_T5 - meanMethy1_T5)
  856. T5_and_RVS_DMR <- T5_and_RVS_DMR %>%
  857. mutate(T5_RVS_Difference = meanMethy2_RVS - meanMethy1_RVS)
  859. write.table(T5_and_RVS_DMR, file = "../../Methylation_240724/T5_and_RVS_DMR.txt", sep = "\t", row.names = FALSE)
  861. # VIPR2 DMR
  863. VIPR2 <- read.table("../../rsem/output/iPSC/VIPR2_TPM.txt", sep = "\t")
  864. VIPR2$V1 <- sub("\\.genes.*", "", VIPR2$V1)
  865. colnames(VIPR2)[1] <- "seq_ID"
  866. VIPR2 <- VIPR2 %>%
  867. inner_join(iPSC_ID)
  869. VIPR2_T5 <- VIPR2 %>%
  870. mutate(state = case_when(
  871. Edit == "T5" ~ "T5 edit",
  872. Edit == "T5-RVS" ~ "T5 RVS",
  873. TRUE ~ "Background"
  874. ))
  876. T5_order <- c("Background", "T5 edit", "T5 RVS")
  877. VIPR2_T5$state <- factor(VIPR2_T5$state, levels = T5_order)
  879. VIPR2 <- ggplot(VIPR2_T5, aes(x = state, y = V6, fill = state)) +
  880. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  881. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  882. scale_fill_manual(values = c("#0D4A80","#9CCB86","#355e3b")) +
  883. theme_bw() +
  884. theme(legend.position = "none") +
  885. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  886. ylab("log10 expression (TPM)") +
  887. xlab("")
  888. VIPR2 + scale_y_continuous(trans='log10')
  889. ggsave("../plots/VIPR2_TPM.pdf", VIPR2, width = 6, height = 4)
  891. # STMN3 DMR
  893. STMN3 <- read.table("../../rsem/output/iPSC/STMN3_TPM.txt", sep = "\t")
  894. STMN3$V1 <- sub("\\.genes.*", "", STMN3$V1)
  895. colnames(STMN3)[1] <- "seq_ID"
  896. STMN3 <- STMN3 %>%
  897. inner_join(iPSC_ID)
  899. STMN3_T5 <- STMN3 %>%
  900. mutate(state = case_when(
  901. Edit == "T5" ~ "T5 edit",
  902. Edit == "T5-RVS" ~ "T5 RVS",
  903. TRUE ~ "Background"
  904. ))
  906. T5_order <- c("Background", "T5 edit", "T5 RVS")
  907. STMN3_T5$state <- factor(STMN3_T5$state, levels = T5_order)
  909. STMN3 <- ggplot(STMN3_T5, aes(x = state, y = V6, fill = state)) +
  910. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  911. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1.5) +
  912. scale_fill_manual(values = c("#0D4A80","#9CCB86","#355e3b")) +
  913. theme_bw() +
  914. theme(legend.position = "none") +
  915. ylab("Log2FC")
  916. ggsave("../plots/STMN3_TPM.pdf", STMN3, width = 6, height = 4)
  918. #T5 DMR but not in T5 rvs
  919. T5_DMR <- read.table("../../rsem/output/iPSC/T5_DMR_genes.txt", sep = "\t")
  920. T5_DMR$V1 <- sub("\\.genes.*", "", T5_DMR$V1)
  921. colnames(T5_DMR)[1] <- "seq_ID"
  922. T5_DMR_lab <- T5_DMR %>%
  923. inner_join(iPSC_ID)
  925. T5_DMR_lab <- T5_DMR_lab %>%
  926. mutate(state = case_when(
  927. Edit == "T5" ~ "Target",
  928. Edit == "T5-RVS" ~ "RVS",
  929. TRUE ~ "Background"
  930. ))
  932. # ordering x-axis on plot
  933. T5_DMR_lab$state <- factor(T5_DMR_lab$state, levels = c("Background", "Target", "RVS"))
  935. T5_DMR <- ggplot(T5_DMR_lab, aes(x = state, y = V6, fill = state)) +
  936. geom_boxplot(outlier.colour = NA, alpha = 0.5, lwd = 0.3) +
  937. geom_quasirandom(dodge.width = 0.8, shape = 21, size = 1) +
  938. scale_fill_manual(values = c("#0D4A80", "#9CCB86", "#355e3b")) +
  939. theme_bw() +
  940. facet_grid(. ~ V2) +
  941. theme(legend.position = "none") +
  942. theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  943. xlab("") +
  944. ylab("TPM")
  945. ggsave("../plots/T5_DMR_4genes.pdf", width = 10, height = 6)
  947. # Gene set enrichment - neurons --------------------------------------------------------------------
  949. library("clusterProfiler")
  950. library("org.Hs.eg.db")
  951. library("AnnotationDbi")
  952. library("enrichplot")
  954. T5_sig <- t5_neuron_full %>%
  955. filter(padj < 0.05, abs(log2FoldChange) > 1) %>%
  956. arrange(desc(log2FoldChange))
  957. T5_list <- T5_sig$gene
  958. names(D_list) <- GP2_expression_1$Gene
  959. DAn_genes <- names(DAn_list)
  961. T5_gse <- enrichGO(T5_list,
  962. ont = "BP",
  963. keyType = "SYMBOL",
  964. pvalueCutoff = 0.05,
  965. pAdjustMethod = "BH",
  966. qvalueCutoff = 0.2,
  967. minGSSize = 10,
  968. OrgDb = "org.Hs.eg.db")
  969. T5_results <- as.data.frame(T5_gse)
  971. T5_gse_dot <- dotplot(T5_gse, showCategory = 20, size = "Count") +
  972. ggplot2::theme(
  973. axis.text.x = ggplot2::element_text(angle = 45, hjust = 1, size = 8),
  974. axis.text.y = ggplot2::element_text(size = 8)
  975. ) +
  976. ggplot2::coord_fixed(ratio = 0.015)

CRISPR_Tx_plots.R at commit d0c6733, no license · at the source

Overview

Authors: Sophie L. Farrow1,2,3, Sreemol Gokuladhas1, Izlem Su Akan4, Denis Nyaga1, Antony A. Cooper5,6, Ralph Stefan Grand4, Justin M. O’Sullivan1,5,7,8
ORCID iDs: Sophie L. Farrow, Izlem Su Akan
  1. Liggins Institute, The University of Auckland, Auckland 1023, New Zealand
  2. Oxford Parkinson’s Disease Centre and Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QU, UK
  3. Kavli Institute for Neuroscience Discovery, Dorothy Crowfoot Hodgkin Building, University of Oxford, South Parks Road, Oxford OX1 3QU, UK
  4. Centre for Molecular Biology of Heidelberg University (ZMBH), University of Heidelberg, 69120 Heidelberg, Germany
  5. Australian Parkinsons Mission, Garvan Institute of Medical Research, Sydney, NSW, Australia
  6. St Vincent’s Clinical School, University of New South Wales, Sydney, NSW, Australia
  7. Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
  8. MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
Institutions: University of Auckland (New Zealand); University of Oxford (United Kingdom); Heidelberg University (Germany); Garvan Institute of Medical Research (Australia); UNSW Sydney (Australia); Agency for Science, Technology and Research (Singapore); Singapore Institute for Clinical Sciences (Singapore); MRC Lifecourse Epidemiology Unit (United Kingdom); University of Southampton (United Kingdom)
Journal: iScience, volume 29, issue 6, article 116143
Dates: received 20 May 2025; accepted 12 May 2026; published online 1 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116143 · PMID 42291199 · PMCID PMC13253082 · OpenAlex W7163015641
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism)
Methods: Statistics
Keywords: Human genetics, Non-infectious disease, Clinical neuroscience
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Michael J. Fox Foundation for Parkinson’s Research (021131); Australian Government; Parkinson's UK (AVR03420); Dines Family Charitable Trust; New Zealand e-Science Infrastructure; NeSI
Citations: cited by 1 paper (Europe PMC); 55 references in the paper
Research resources: B-III tubulin; chicken RRID:AB_10000548, SNAP25; rabbit RRID:AB_2192187, MAP2; mouse RRID:AB_3273979

Abstract

Non-coding variants associated with complex disease can shape gene regulatory networks across multiple genomic loci and cellular contexts. Here, we investigated the functional impact of the Parkinson-disease-associated variant rs11610045 using an isogenic induced pluripotent stem cell model. Using CRISPR-Cas9 editing and reversal, we generated matched clones carrying either the A|A or G|G genotype, enabling controlled comparison of allele-specific effects. We identified widespread genotype-dependent regulation of distal genes, including THBS1 and PDGFB. Affinity purification followed by mass spectrometry revealed differential binding of regulatory proteins to the G|G allele, including the transcription factor TCF7L1. Differentiation into cortical neurons demonstrated context-dependent effects, with 24 genes differentially expressed and PAX5 consistently altered across developmental stages. Together, these findings link a non-coding disease-associated variant to coordinated changes in gene expression and protein binding, support trans-acting mechanisms underlying regulatory variation, and provide a generalizable framework for dissecting disease-associated loci in human cellular models.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

sfar956/PD_iPSC_Tx

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d0c6733c1216b143fa5e331660dd784a0d9688ad, 30 April 2026
Languages: R (1)
Size: 4 files, 1 script
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: clusterProfiler (1 file), DESeq2 (1 file), ggplot2 (1 file), ggpubr (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/sfar956/PD_iPSC_Tx.git

nanoporetech/pod5-file-format

License: MPL-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: fdbe60a9a52aab8ce548be23b7e4025c9af89779, 21 September 2026
Languages: Python (62), C++ (61), C/C++ (57), Shell (26), C (1)
Size: 312 files, 207 scripts
Software Heritage: archived
Found in: the text, “Sequence data analysis, off-target characterisat”
Holds: README, license file, environment (pyproject.toml, benchmarks/image/Dockerfile.base, benchmarks/image/requirements-benchmarks.txt, ci/docker/Dockerfile.conda, ci/docker/Dockerfile.fuzz, ci/docker/Dockerfile.py39.arm64, ci/docker/Dockerfile.py39.x64, ci/docker/Dockerfile.ubu-build-deps, ci/docker/Dockerfile.ubu-build-no-deps, python/lib_pod5/pyproject.toml, python/lib_pod5/setup.py, python/pod5/pyproject.toml), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (21 files), pandas (6 files), h5py (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
209 files
At the source: github.com/nanoporetech/pod5-file-format

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;
  • 208 scripts, each with its path and the digest of its content;
  • 3 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 and code availability

• RNA-seq and long-read WGS data have been deposited in the NCBI Sequence Read Archive (SRA) and are publicly available as of the date of publication. AP-MS data are available via the PRIDE repository and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. • All original code has been deposited in appropriate repositories and is available at https://github.com/sfar956/PD_iPSC_Tx.git. R Studio v.4.2.1 was used for data analyses and visualization. • Summary data are provided in the supplementary tables. • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Reproduced under the paper's license (CC BY), 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 2, 28 September 2026

  • Authors: added Sophie L. Farrow (0000-0002-6578-4219); removed Sophie L. Farrow

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 6 funders, 54 references, 3 RRIDs.

Cite

This paper

Farrow, S. L., Gokuladhas, S., Akan, I. S., Nyaga, D., Cooper, A. A., Grand, R. S., & O’Sullivan, J. M. (2026). Dissecting genotype-specific effects of disease-associated genetic variants. iScience, 29(6), 116143. https://doi.org/10.1016/j.isci.2026.116143

BibTeX

@article{farrow2026dissecting,
author = {Farrow, Sophie L. and Gokuladhas, Sreemol and Akan, Izlem Su and Nyaga, Denis and Cooper, Antony A. and Grand, Ralph Stefan and O’Sullivan, Justin M.},
title = {{Dissecting genotype-specific effects of disease-associated genetic variants}},
journal = {iScience},
year = {2026},
month = jun,
volume = {29},
number = {6},
pages = {116143},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116143},
url = {https://doi.org/10.1016/j.isci.2026.116143},
pmid = {42291199},
pmcid = {PMC13253082}
}

RIS

TY - JOUR
AU - Farrow, Sophie L.
AU - Gokuladhas, Sreemol
AU - Akan, Izlem Su
AU - Nyaga, Denis
AU - Cooper, Antony A.
AU - Grand, Ralph Stefan
AU - O’Sullivan, Justin M.
TI - Dissecting genotype-specific effects of disease-associated genetic variants
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/06/01
VL - 29
IS - 6
SP - 116143
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116143
UR - https://doi.org/10.1016/j.isci.2026.116143
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2026.116143",
"type": "article-journal",
"title": "Dissecting genotype-specific effects of disease-associated genetic variants",
"container-title": "iScience",
"author": [
{
"family": "Farrow",
"given": "Sophie L."
},
{
"family": "Gokuladhas",
"given": "Sreemol"
},
{
"family": "Akan",
"given": "Izlem Su"
},
{
"family": "Nyaga",
"given": "Denis"
},
{
"family": "Cooper",
"given": "Antony A."
},
{
"family": "Grand",
"given": "Ralph Stefan"
},
{
"family": "O’Sullivan",
"given": "Justin M."
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "6",
"page": "116143",
"DOI": "10.1016/j.isci.2026.116143",
"PMID": "42291199",
"PMCID": "PMC13253082",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.116143",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.
Journal: Nature communications
In common: DESeq2, h5py, ggpubr, 4 other tools, genetics / omics, 3 references
[3] doi:10.1093/bioinformatics/btag592 [code]
Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.
Journal: Bioinformatics (Oxford, England)
In common: DESeq2, clusterProfiler, ggpubr, 4 other tools, genetics / omics, 3 references
[4] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: DESeq2, clusterProfiler, ggpubr, 4 other tools, genetics / omics, 3 references
[5] doi:10.1016/j.stemcr.2026.102930 [code]
ZFHX4 is necessary for dopaminergic neuron differentiation and controls cell cycle by regulating LIN28A.
Journal: Stem cell reports
In common: DESeq2, clusterProfiler, ggpubr, 4 other tools, genetics / omics, 3 references
[6] doi:10.1016/j.xcrm.2026.102766 [code]
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: DESeq2, clusterProfiler, h5py, 5 other tools, genetics / omics, 1 reference
[7] doi:10.1093/bib/bbag175 [code]
Uncovering causal relationships in single-cell omic studies with causarray.
Journal: Briefings in bioinformatics
In common: DESeq2, clusterProfiler, h5py, 4 other tools, 2 references
[8] doi:10.1126/sciadv.aed2952 [code]
Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease.
Journal: Science advances
In common: DESeq2, clusterProfiler, ggpubr, 4 other tools, 3 references
[9] doi:10.1038/s41467-026-71525-6 [code]
Single-nucleus brain transcriptomics reveals microglia dysfunction in multiple system atrophy.
Journal: Nature communications
In common: DESeq2, clusterProfiler, ggpubr, 4 other tools, genetics / omics, 2 references
[10] doi:10.1016/j.isci.2026.116412 [code]
KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease.
Journal: iScience
In common: DESeq2, clusterProfiler, ggpubr, 4 other tools, genetics / omics, 2 references

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