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IgLON5 autoimmune antibodies activate Tau via neuronal hyperactivity.

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  1. [1] § MATERIALS AND METHODS › RNA sequencing ↔ 250327_publication.Rmd, lines 416–550 · score 0.72 · fold change, DESeq2, shrinkage, IHW, apeglm, threshold

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  1. ---
  2. title: "DESeq2 RNA-seq Analysis"
  3. author: " Pia Grundschoettel"
  4. date: "17.04.2025"
  5. output:
  6. html_document:
  7. toc: true
  8. toc_float: true
  9. ---
  11. # 1. R requirements
  13. ## 1.1 Install and load packages
  16. ### Install CRAN
  17. ```{r}
  18. # CRAN packages
  19. list.of.packages <- c("apeglm",
  20. "factoextra",
  21. "ggbeeswarm",
  22. "ggplot2",
  23. "ggrepel",
  24. "gplots",
  25. "hexbin",
  26. "Hmisc",
  27. "openxlsx",
  28. "patchwork",
  29. "plotly",
  30. "reshape2",
  31. "scales",
  32. "tidyr",
  33. # "VennDiagram",
  34. "UpSetR",
  35. "rstatix",
  36. "ggExtra",
  37. "multcomp"
  38. # "RANN",
  39. # "igraph",
  40. # "leidenbase"
  41. )
  43. new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
  44. if(length(new.packages)>0) install.packages(new.packages)
  45. ```
  48. ### Install BioConductor
  50. ```{r}
  51. # BioconductoR packages
  52. list.of.bioc.packages <- c("biomaRt",
  53. "clusterProfiler",
  54. "ComplexHeatmap",
  55. "DESeq2",
  56. "DOSE",
  57. "genefilter",
  58. "ggpubr",
  59. "GSEABase",
  60. "GSVA",
  61. "IHW",
  62. "limma",
  63. "org.Hs.eg.db",
  64. "pheatmap",
  65. "RColorBrewer",
  66. "rhdf5",
  67. # "sva",
  68. "tximport"
  69. # "vsn",
  70. # "Seurat",
  71. # "SeuratDisk"
  72. )
  74. new.packages.bioc <- list.of.bioc.packages[!(list.of.bioc.packages %in% installed.packages()[,"Package"])]
  75. if(length(new.packages.bioc)>0) if (!requireNamespace("BiocManager")) install.packages("BiocManager")
  76. BiocManager::install(new.packages.bioc, update = FALSE)
  77. ```
  80. ### Load Packages
  82. ```{r load packages, results='hide',message=FALSE,warning=FALSE}
  83. lapply(c(list.of.packages,list.of.bioc.packages), require, character.only = TRUE)
  84. ```
  86. ```{r}
  87. rm(list.of.packages,list.of.bioc.packages, new.packages, new.packages.bioc)
  88. ```
  91. ## 1.2 Functions used
  93. ### TX import
  94. ```{r}
  95. ### TX import
  96. tx_import <- function(file,
  97. aligner = c("STAR", "Kallisto")) {
  99. if(aligner == "STAR") {
  100. tx <- read.delim(file = file,
  101. header = F ,
  102. stringsAsFactors = F,
  103. col.names = c("GENEID", "SYMBOL", "GENETYPE"))
  105. } else if(aligner == "Kallisto") {
  107. tx <- read.delim(file = file,
  108. header = F ,
  109. stringsAsFactors = F,
  110. col.names = c("GENEID", "TXNAME", "SYMBOL", "GENETYPE"))
  112. } else {print("Unknown aligner - plase choose STAR or Kallisto")}
  114. return(tx)
  115. }
  117. ```
  119. ### Generate norm_anno
  120. ```{r}
  121. generate_norm_anno <- function(dds_object = dds,
  122. annotation_file = tx_annotation){
  124. norm_anno <- as.data.frame(counts(dds_object, normalized=T))
  125. norm_anno$GENEID <- row.names(norm_anno)
  127. # add gene annotation
  128. gene_annotation <- annotation_file[!duplicated(annotation_file$GENEID), c("GENEID", "SYMBOL", "GENETYPE")]
  130. # merge expression table and annotation
  131. norm_anno <- dplyr::left_join(norm_anno, gene_annotation, by = "GENEID")
  132. rownames(norm_anno) <- norm_anno$GENEID
  134. return(norm_anno)
  135. }
  136. ```
  138. ### Boxplot of normalized expression per sample
  139. ```{r}
  140. boxplot_norm <- function(norm_anno = norm_anno,
  141. col.by = "condition",
  142. col.use = col_condition){
  144. # create a sample table just taking the sample ID and condition for boxplot visualization
  145. box_sample_table <- sample_table[ ,c("ID", col.by)]
  147. # annotation
  148. box_norm_table <- norm_anno[ ,colnames(norm_anno) %in% box_sample_table$ID]
  149. box_norm_table$GENEID <- rownames(box_norm_table)
  151. # restructuring the table for ggplot2 analysis w/ melt function
  152. box_norm_table <- melt(box_norm_table, id.vars = c("GENEID"))
  153. colnames(box_norm_table) <- c("GENEID","sample","expression")
  154. box_norm_table <- merge(box_norm_table, box_sample_table, by.x="sample", by.y="ID")
  156. p <- ggplot(box_norm_table, aes(x = sample , y = expression+1))+
  157. geom_boxplot(aes_string(fill = col.by))+
  158. scale_y_log10(labels = scales::label_number(big.mark = ","))+
  159. # scale_y_continuous() +
  160. scale_fill_manual(values = col.use)+
  161. theme_bw() +
  162. theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))+
  163. xlab("Samples") + ylab("Normalized expression") +
  164. ggtitle("Normalized gene expression per sample")
  166. plot(p)
  167. }
  168. ```
  170. ### Generate norm_anno
  171. ```{r}
  173. generate_vst_anno <- function(dds_object = dds_vst,
  174. annotation_file = tx_annotation){
  176. # get gene annotation
  177. gene_annotation <- annotation_file[!duplicated(annotation_file$GENEID), c("GENEID", "SYMBOL", "GENETYPE")]
  179. # vst anno log
  180. vst_anno_log <- as.data.frame(assay(dds_object))
  181. vst_anno_log$GENEID <- row.names(vst_anno_log)
  182. vst_anno_log <- dplyr::left_join(vst_anno_log, gene_annotation, by = "GENEID")
  183. rownames(vst_anno_log) <- vst_anno_log$GENEID
  185. # vst anno (unlog)
  186. vst_anno <- as.data.frame(assay(dds_object))
  187. vst_anno <- 2^vst_anno
  188. vst_anno$GENEID <- row.names(vst_anno)
  189. vst_anno <- dplyr::left_join(vst_anno, gene_annotation, by = "GENEID")
  190. rownames(vst_anno) <- vst_anno$GENEID
  192. vst_anno <- list("log" = vst_anno_log,
  193. "unlog" = vst_anno)
  195. return(vst_anno)
  196. }
  197. ```
  199. ### Heatmap
  200. ```{r}
  201. plotHeatmap <- function(input = norm_anno,
  202. geneset = "all",
  203. title = "",
  204. keyType = "Ensembl",
  205. gene_type = "all",
  206. cutree_cols = 1,
  207. show_rownames = FALSE,
  208. cluster_cols = FALSE,
  209. sample_annotation = sample_table,
  210. plot_mean = FALSE,
  211. method = "complete",
  212. distance = "euclidean")
  213. {
  214. if (geneset[1] != "all") {
  215. if (keyType == "Ensembl") {
  216. input <- input[input$GENEID %in% geneset, ]
  217. }
  218. else if (keyType == "Symbol") {
  219. input <- input[input$SYMBOL %in% geneset, ]
  220. }
  221. else {
  222. print("Wrong keyType. Choose Ensembl or Symbol!")
  223. }
  224. }
  226. if (gene_type != "all") {
  227. input <- input[input$GENETYPE %in% gene_type, ]
  228. }
  230. rownames(input) <- paste(input$GENEID, ":", input$SYMBOL, sep = "")
  232. if(plot_mean == FALSE ){
  233. input <- input[ , colnames(input) %in% sample_annotation$ID]
  234. input_scale <- t(scale(t(input)))
  235. input_scale <- input_scale[, order(sample_annotation[[plot_order]], decreasing = FALSE)]
  237. column_annotation <- plot_annotation
  239. } else {
  240. input <- input[ , colnames(input) %in% plot_annotation_mean$condition]
  241. input_scale <- t(scale(t(input)))
  243. column_annotation <- plot_annotation_mean
  244. }
  246. breaks <- scaleColors(data = input_scale, maxvalue = 2)[["breaks"]]
  248. pheatmap(input_scale, main = as.character(title),
  249. show_rownames = show_rownames,
  250. show_colnames = TRUE,
  251. cluster_cols = cluster_cols,
  252. clustering_method = method,
  253. clustering_distance_rows = distance,
  254. clustering_distance_cols = distance,
  255. fontsize = 7,
  256. cutree_cols = cutree_cols,
  257. border_color = "NA",
  258. annotation_col = column_annotation,
  259. annotation_colors = ann_colors,
  260. breaks = breaks,
  261. color = scaleColors(data = input_scale, maxvalue = 2)[["color"]])
  262. }
  263. ```
  265. ### Heatmap colors
  266. ```{r}
  267. scaleColors <- function(data = input_scale, # data to use
  268. maxvalue = NULL # value at which the color is fully red / blue
  269. ){
  270. if(is.null(maxvalue)){
  271. maxvalue <- floor(min(abs(min(data)), max(data)))
  272. }
  273. if(max(data) > abs(min(data))){
  274. if(ceiling(max(data)) == maxvalue){
  275. myBreaks <- c(floor(-max(data)), seq(-maxvalue+0.2, maxvalue-0.2, 0.2), ceiling(max(data)))
  276. } else{
  277. myBreaks <- c(floor(-max(data)), seq(-maxvalue, maxvalue, 0.2), ceiling(max(data)))
  278. }
  279. paletteLength <- length(myBreaks)
  280. myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
  281. } else {
  282. if(-floor(min(data)) == maxvalue){
  283. myBreaks <- c(floor(min(data)), seq(-maxvalue+0.2, maxvalue-0.2, 0.2), ceiling(min(data)))
  284. } else{
  285. myBreaks <- c(floor(min(data)), seq(-maxvalue, maxvalue, 0.2), ceiling(abs(min(data))))
  286. }
  287. paletteLength <- length(myBreaks)
  288. myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
  289. }
  291. ret <- list(breaks = myBreaks, color = myColor)
  292. return(ret)
  293. }
  295. ```
  297. ### PCA function
  298. ```{r}
  299. plotPCA <- function(pca_input = dds_vst,
  300. pca_sample_table = sample_table,
  301. ntop=500,
  302. xPC=1,
  303. yPC=2,
  304. color,
  305. anno_colour,
  306. add_density = T,
  307. add_silhouette = T,
  308. split_by = "NULL",
  309. shape="NULL",
  310. point_size=3,
  311. title="PCA",
  312. label = NULL,
  313. label_subset = NULL){
  315. if(is.character(pca_input)){
  316. vst_matrix <- as.matrix(removedbatch_dds_vst)
  317. }else if(!is.data.frame(pca_input)){
  318. vst_matrix <- as.matrix(assay(pca_input))
  319. }else{
  320. vst_matrix <- pca_input
  321. }
  323. if(length(ntop)>1){
  324. pca <- prcomp(t(vst_matrix[dimnames(vst_matrix)[[1]] %in% ntop,]))
  325. }else if (ntop == "all"){
  326. pca <- prcomp(t(vst_matrix))
  328. }else{
  329. # select the ntop genes by variance
  330. select <- order(rowVars(vst_matrix), decreasing=TRUE)[c(1:ntop)]
  331. pca <- prcomp(t(vst_matrix[select,]))
  332. }
  334. #calculate explained variance per PC
  335. explVar <- pca$sdev^2/sum(pca$sdev^2)
  336. # transform variance to percent
  337. percentVar <- round(100 * explVar[c(xPC,yPC)], digits=1)
  339. # Define data for plotting
  340. pcaData <- data.frame(xPC=pca$x[,xPC],
  341. yPC=pca$x[,yPC],
  342. color = pca_sample_table[[color]],
  343. name= as.character(pca_sample_table$ID),
  344. stringsAsFactors = F)
  346. #plot PCA
  347. if(is.factor(pcaData$color) || is.character(pcaData$color)|| is.integer(pcaData$color)){
  348. if(shape == "NULL"){
  349. pca_plot <- ggplot(pcaData, aes(x = xPC, y = yPC, colour=color)) +
  350. geom_point(size =point_size)
  351. }else{
  352. pcaData$shape = pca_sample_table[[shape]]
  353. pca_plot <- ggplot(pcaData, aes(x = xPC, y = yPC, color = color, shape=shape)) +
  354. geom_point(size =point_size) +
  355. scale_shape_discrete(name=shape)
  357. }
  359. if(add_silhouette){
  360. pca_plot <- pca_plot + stat_ellipse(geom = "polygon", aes(fill = color), alpha = 0.05)
  361. }
  363. if(anno_colour[1] == "NULL"){
  364. pca_plot <- pca_plot + scale_color_discrete(name=color)
  365. }else{
  366. pca_plot <- pca_plot + scale_color_manual(values=anno_colour, name=color, aesthetics = c("colour", "fill"))
  367. }
  369. }else if(is.numeric(pcaData$color)){
  370. if(shape == "NULL"){
  371. pca_plot <- ggplot(pcaData, aes(x = xPC, y = yPC, colour=color)) +
  372. geom_point(size =point_size) +
  373. scale_color_gradientn(colours = bluered(100),name=color) +
  374. scale_fill_gradientn(colours = bluered(100),name=color)
  375. }else{
  376. pcaData$shape = pca_sample_table[[shape]]
  377. pca_plot <- ggplot(pcaData, aes(x = xPC, y = yPC, colour=color, shape=shape)) +
  378. geom_point(size =point_size) +
  379. scale_color_gradientn(colours = bluered(100),name=color) +
  380. scale_fill_gradientn(colours = bluered(100),name=color) +
  381. scale_shape_discrete(name=shape)
  382. }
  383. }
  385. # adds a label to the plot. To label only specific points, put them in the arument label_subset
  386. if (!is.null(label) == TRUE){
  387. pcaData$label <- pca_sample_table[[label]]
  388. if(!is.null(label_subset) == TRUE){
  389. pcaData_labeled <- pcaData[pcaData$label %in% label_subset,]
  390. } else {
  391. pcaData_labeled <- pcaData
  392. }
  393. pca_plot <- pca_plot +
  394. geom_text_repel(data = pcaData_labeled, aes(label = label), nudge_x = 2, nudge_y = 2, colour = "black")
  395. }
  397. pca_plot <- pca_plot+
  398. xlab(paste0("PC ",xPC, ": ", percentVar[1], "% variance")) +
  399. ylab(paste0("PC ",yPC,": ", percentVar[2], "% variance")) +
  400. coord_fixed()+
  401. theme_bw()+
  402. theme(aspect.ratio = 1,
  403. legend.position = "bottom")+
  404. ggtitle(title)
  406. if(add_density){
  407. ggMarginal(pca_plot, groupFill = T, type = "density")
  408. } else if (split_by != "NULL"){
  409. pca_plot + facet_wrap(~pca_sample_table[[split_by]])
  410. } else {
  411. pca_plot
  412. }
  413. }
  414. ```
  416. ### Wrapper Function to perform DESeq2 differential testing
  417. ```{r}
  418. DEAnalysis <- function(input = dds,
  419. condition,
  420. comparison_table = comparison_table,
  421. alpha = 0.05,
  422. lfcThreshold = 0,
  423. sigFC = 2,
  424. multiple_testing = "IHW",
  425. pAdjustMethod = "BH",
  426. independentFiltering= TRUE,
  427. shrinkage = TRUE,
  428. shrinkType = "normal"){
  430. setClass(Class = "DESeq2_analysis_object",
  431. slots = c(results="data.frame", DE_genes="list", Number_DE_genes="list"))
  433. # create results_list
  434. results_list <- list()
  435. # print parameters
  436. results_list$parameters <-list(multiple_testing = multiple_testing,
  437. p_value_threshold = alpha,
  438. log2_FC_threshold = lfcThreshold,
  439. sigFC = sigFC,
  440. shrinkage = shrinkage,
  441. shrinkage_type = shrinkType)
  442. # Run results() function on comparisons defined in comparison table
  443. for (x in 1:nrow(comparison_table)){
  444. # create DE_object
  445. DE_object <- new(Class = "DESeq2_analysis_object")
  446. # IHW
  447. if (multiple_testing=="IHW") {
  448. res_deseq_lfc <- results(input,
  449. contrast = c(condition,
  450. paste(comparison_table$comparison[x]),
  451. paste(comparison_table$control[x])),
  452. lfcThreshold = lfcThreshold,
  453. alpha = alpha,
  454. filterFun = ihw,
  455. pAdjustMethod = pAdjustMethod,
  456. altHypothesis = "greaterAbs")
  457. # Independent Filtering
  458. }else {
  459. res_deseq_lfc <- results(input,
  460. contrast = c(condition,
  461. paste(comparison_table$comparison[x]),
  462. paste(comparison_table$control[x])),
  463. lfcThreshold = lfcThreshold,
  464. alpha = alpha,
  465. independentFiltering = independentFiltering,
  466. altHypothesis = "greaterAbs",
  467. pAdjustMethod= pAdjustMethod)
  468. }
  469. if(shrinkage == TRUE){
  470. if(shrinkType %in% c("normal", "ashr")){
  472. res_deseq_lfc <- lfcShrink(input,
  473. contrast = c(condition,
  474. paste(comparison_table$comparison[x]),
  475. paste(comparison_table$control[x])),
  476. res=res_deseq_lfc,
  477. type = shrinkType)
  479. }else if(shrinkType == "apeglm"){
  481. res_deseq_lfc <- lfcShrink(input,
  482. coef = paste0(condition, "_",
  483. comparison_table$comparison[x], "_vs_",
  484. comparison_table$control[x]),
  485. res=res_deseq_lfc,
  486. type = shrinkType,
  487. returnList = F)
  488. }
  489. }
  490. res_deseq_lfc <- as.data.frame(res_deseq_lfc)
  491. # indicate significant DE genes
  492. res_deseq_lfc$regulation <- ifelse(!is.na(res_deseq_lfc$padj)&
  493. res_deseq_lfc$padj <= alpha&
  494. res_deseq_lfc$log2FoldChange > log(sigFC,2),
  495. "up",
  496. ifelse(!is.na(res_deseq_lfc$padj)&
  497. res_deseq_lfc$padj <= alpha&
  498. res_deseq_lfc$log2FoldChange < -log(sigFC,2),
  499. "down",
  500. "n.s."))
  501. # add gene annotation to results table
  502. res_deseq_lfc$GENEID <- row.names(res_deseq_lfc)
  503. res_deseq_lfc <- merge(res_deseq_lfc,
  504. norm_anno[,c("GENEID",
  505. "SYMBOL",
  506. "GENETYPE")],
  507. by = "GENEID")
  508. row.names(res_deseq_lfc) <- res_deseq_lfc$GENEID
  509. res_deseq_lfc$comparison<-paste(comparison_table$comparison[x]," vs ",comparison_table$control[x],
  510. sep="")
  511. # re-order results table
  512. if (multiple_testing=="IHW") {
  513. res_deseq_lfc<-res_deseq_lfc[,c("GENEID",
  514. "SYMBOL",
  515. "GENETYPE",
  516. "comparison",
  517. "regulation",
  518. "baseMean",
  519. "log2FoldChange",
  520. "lfcSE",
  521. "pvalue",
  522. "padj"
  523. )]
  524. }else{
  525. res_deseq_lfc<-res_deseq_lfc[,c("GENEID",
  526. "SYMBOL",
  527. "GENETYPE",
  528. "comparison",
  529. "regulation",
  530. "baseMean",
  531. "log2FoldChange",
  532. "lfcSE",
  533. "pvalue",
  534. "padj")]
  535. }
  536. # print result table
  537. DE_object@results <- res_deseq_lfc
  538. # print DE genes in separate tables
  539. DE_object@DE_genes <- list(up_regulated_Genes = res_deseq_lfc[res_deseq_lfc$regulation =="up",],
  540. down_regulated_Genes= res_deseq_lfc[res_deseq_lfc$regulation =="down",])
  541. # print the numbers of DE genes
  542. DE_object@Number_DE_genes <- list(up_regulated_Genes = nrow(DE_object@DE_genes$up_regulated_Genes),
  543. down_regulated_Genes= nrow(DE_object@DE_genes$down_regulated_Genes))
  544. # write DE_object into results_list
  545. results_list[[paste(comparison_table$comparison[x], "vs", comparison_table$control[x], sep="_")]] <- DE_object
  546. }
  547. return(results_list)
  548. }
  550. ```
  552. ### Venn Diagram
  553. ```{r}
  555. plotVenn <- function(comparisons,
  556. regulation=NULL){
  557. venn <- NULL
  558. for(i in 1:length(comparisons)){
  559. res <- DEresults[names(DEresults) %in% comparisons]
  560. comp <- as.data.frame(res[[i]]@results)
  561. if(is.null(regulation)){
  562. DE <- ifelse(comp$regulation %in% c("up","down"), 1, 0)
  563. venn <- cbind(venn, DE)
  564. colnames(venn)[i]<- paste(names(res)[[i]], "up&down", sep=": ")
  565. } else {
  566. DE <- ifelse(comp$regulation == regulation, 1, 0)
  567. venn <- cbind(venn, DE)
  568. colnames(venn)[i]<- paste(names(res)[[i]], regulation, sep=": ")
  569. }
  571. }
  572. vennDiagram(venn,cex = 1, counts.col = "blue")
  573. }
  574. ```
  576. ### GSEA function
  577. ```{r}
  580. #' wrapper function for GSEA on different DE calls
  581. #' @author Charlotte Kröger, Lisa Holsten, Kilian Dahm
  582. #' @param comparisons name of DE analysis comparisons (class: character)
  583. #' @param DE_results list object containing DE analysis results (class: list)
  584. #' @param universe vector of all genes included in the analysis
  585. #' @param GeneSets databases to be used, one of : 'GO', 'KEGG' or 'HALLMARK'
  586. #' @param ontology ontology of the GO database to be used, one of: 'BP', 'MF', 'CC'
  587. #' @param pCorrection p-value adjustment method such as 'BH' (less strict) or 'bonferroni'
  588. #' @param pvalueCutoff set the unadj. or adj. p-value cutoff (depending on correction method)
  589. #' @param qvalueCutoff set the q-value (adj. p-value) cutoff
  590. #'
  591. enrichGSEA <- function(comparisons,
  592. DE_results = DEresults,
  593. universe = universe,
  594. GeneSets = c("GO","KEGG", "HALLMARK", "REACTOME"),
  595. pCorrection = "bonferroni",
  596. pvalueCutoff = 0.05,
  597. qvalueCutoff = 0.05){
  599. ## Results lists
  600. GOresults.list <- list()
  601. KEGGresults.list <- list()
  602. HALLMARKresults.list <- list()
  603. REACTOMEresults.list <- list()
  605. ## Enrichment
  606. for(i in comparisons){
  608. print(paste0("Performing enrichment for ", i))
  610. ## DEGs
  611. genes <- DE_results[[i]]@results
  612. DE_up <- genes[genes$regulation=="up",]$SYMBOL
  613. DE_down <- genes[genes$regulation=="down",]$SYMBOL
  615. # Compare the Clusters regarding their GO enrichment
  616. if("GO" %in% GeneSets){
  617. print("Performing GO enrichment")
  618. resGOup <- as.data.frame(enricher(gene = DE_up,
  619. universe = universe,
  620. TERM2GENE= GO_terms,
  621. pAdjustMethod = pCorrection,
  622. pvalueCutoff = pvalueCutoff,
  623. qvalueCutoff = qvalueCutoff))
  624. if(nrow(resGOup)>0){
  625. resGOup$comparison <- paste0(as.character(i), "_up")
  626. resGOup$regulation <- "up"
  627. GOresults.list[[paste(i, "_up")]] <- resGOup
  628. }
  629. resGOdown <- as.data.frame(enricher(gene = DE_down,
  630. universe = universe,
  631. TERM2GENE= GO_terms,
  632. pAdjustMethod = pCorrection,
  633. pvalueCutoff = pvalueCutoff,
  634. qvalueCutoff = qvalueCutoff))
  635. if(nrow(resGOdown)>0){
  636. resGOdown$comparison <- paste0(as.character(i), "_down")
  637. resGOdown$regulation <- "down"
  638. GOresults.list[[paste(i, "_down")]] <- resGOdown
  639. }
  640. }
  641. # Compare the Clusters regarding their HALLMARK enrichment
  642. if("HALLMARK" %in% GeneSets){
  643. print("Performing HALLMARK enrichment")
  644. resHMup <- as.data.frame(enricher(gene = DE_up,
  645. universe = universe,
  646. TERM2GENE = HALLMARK_terms,
  647. pAdjustMethod = pCorrection,
  648. pvalueCutoff = pvalueCutoff,
  649. qvalueCutoff = qvalueCutoff))
  650. if(nrow(resHMup)>0){
  651. resHMup$comparison <- paste0(as.character(i), "_up")
  652. resHMup$regulation <- "up"
  653. HALLMARKresults.list[[paste(i, "_up")]] <- resHMup
  654. }
  655. resHMdown <- as.data.frame(enricher(gene = DE_down,
  656. universe = universe,
  657. TERM2GENE = HALLMARK_terms,
  658. pAdjustMethod = pCorrection,
  659. pvalueCutoff = pvalueCutoff,
  660. qvalueCutoff = qvalueCutoff))
  661. if(nrow(resHMdown)>0){
  662. resHMdown$comparison <- paste0(as.character(i), "_down")
  663. resHMdown$regulation <- "down"
  664. HALLMARKresults.list[[paste(i, "_down")]] <- resHMdown
  665. }
  666. }
  667. # Compare the Clusters regarding their KEGG enrichment
  668. if("KEGG" %in% GeneSets){
  669. print("Performing KEGG enrichment")
  670. resKEGGup <- as.data.frame(enricher(gene = DE_up,
  671. universe = universe,
  672. TERM2GENE = KEGG_terms,
  673. pAdjustMethod = pCorrection,
  674. pvalueCutoff = pvalueCutoff,
  675. qvalueCutoff = qvalueCutoff))
  676. if(nrow(resKEGGup)>0){
  677. resKEGGup$comparison <- paste0(as.character(i), "_up")
  678. resKEGGup$regulation <- "up"
  679. KEGGresults.list[[paste(i, "_up")]] <- resKEGGup
  680. }
  681. resKEGGdown <- as.data.frame(enricher(gene = DE_down,
  682. universe = universe,
  683. TERM2GENE = KEGG_terms,
  684. pAdjustMethod = pCorrection,
  685. pvalueCutoff = pvalueCutoff,
  686. qvalueCutoff = qvalueCutoff))
  687. if(nrow(resKEGGdown)>0){
  688. resKEGGdown$comparison <- paste0(as.character(i), "_down")
  689. resKEGGdown$regulation <- "down"
  690. KEGGresults.list[[paste(i, "_down")]] <- resKEGGdown
  691. }
  692. }
  693. # Compare the Clusters regarding their REACTOME enrichment
  694. if("REACTOME" %in% GeneSets){
  695. print("Performing REACTOME enrichment")
  696. resREACTOMEup <- as.data.frame(enricher(gene = DE_up,
  697. universe = universe,
  698. TERM2GENE = REACTOME_terms,
  699. pAdjustMethod = pCorrection,
  700. pvalueCutoff = pvalueCutoff,
  701. qvalueCutoff = qvalueCutoff))
  702. if(nrow(resREACTOMEup)>0){
  703. resREACTOMEup$comparison <- paste0(as.character(i), "_up")
  704. resREACTOMEup$regulation <- "up"
  705. REACTOMEresults.list[[paste(i, "_up")]] <- resREACTOMEup
  706. }
  707. resREACTOMEdown <- as.data.frame(enricher(gene = DE_down,
  708. universe = universe,
  709. TERM2GENE = REACTOME_terms,
  710. pAdjustMethod = pCorrection,
  711. pvalueCutoff = pvalueCutoff,
  712. qvalueCutoff = qvalueCutoff))
  713. if(nrow(resREACTOMEdown)>0){
  714. resREACTOMEdown$comparison <- paste0(as.character(i), "_down")
  715. resREACTOMEdown$regulation <- "down"
  716. REACTOMEresults.list[[paste(i, "_down")]] <- resREACTOMEdown
  717. }
  718. }
  720. }
  721. GOresults <- do.call("rbind", GOresults.list)
  722. HALLMARKresults <- do.call("rbind", HALLMARKresults.list)
  723. KEGGresults <- do.call("rbind", KEGGresults.list)
  724. REACTOMEresults <- do.call("rbind", REACTOMEresults.list)
  725. results.list <- list("GO" = GOresults,
  726. "HALLMARK" = HALLMARKresults,
  727. "KEGG" = KEGGresults,
  728. "REACTOME" = REACTOMEresults)
  729. return(results.list)
  730. }
  732. ```
  734. ### GSEA dotplot
  735. ```{r}
  736. #' wrapper function for combined dotplot representation of all comparisons used in the enrichGSEA function
  737. #' @author Charlotte Kröger, Lisa Holsten, Kilian Dahm
  738. #' @param enrich.df data frame of enrichGSEA function
  739. #' @param show number of terms to be shown per comparison
  740. #' @param orderBy parameter for ordering terms on the y-axis
  741. #' @param colorBy parameter for coloring the dots
  742. #' @param scaleBy parameter for scaling the dots
  743. #'
  744. dotplotGSEA <- function(enrich.df,
  745. show =10,
  746. orderBy = c("padj", "count", "GeneRatio"),
  747. colorBy = c("padj", "regulation"),
  748. scaleBy = c("count", "GeneRatio")
  749. ){
  750. if(nrow(enrich.df)<1){ print("No enrichment found.")
  751. } else{
  752. # ordering
  754. enrich.df$gene.ratio <- sapply(strsplit(enrich.df$GeneRatio, split = "/"), function(x) as.numeric(x[1]) / as.numeric(x[2]))
  756. if(orderBy=="padj"){
  757. enrich.df %>% group_by(comparison) %>% arrange(desc(Count), .by_group = TRUE) -> x
  758. x %>% group_by(comparison) %>% arrange(desc(Count), .by_group = TRUE) %>% dplyr::top_n(n = -show, wt = p.adjust) %>% dplyr::pull(Description) -> terms
  759. x <- x[x$Description %in% terms,]
  760. x$Description <- ifelse(nchar(x$Description)>80, paste(substr(x$Description, 1, 80),"[...]",sep=""), x$Description)
  761. x$Description <- factor(x$Description, levels = rev(unique(x$Description)))
  763. } else if (orderBy=="count"){
  764. enrich.df %>% group_by(comparison) %>% arrange(p.adjust, .by_group = TRUE) -> x
  765. x %>% group_by(comparison) %>% arrange(p.adjust, .by_group = TRUE) %>% dplyr::top_n(n = show, wt = Count) %>% dplyr::pull(Description) -> terms
  766. x <- x[x$Description %in% terms,]
  767. x$Description <- ifelse(nchar(x$Description) > 80, paste(substr(x$Description, 1, 80),"[...]",sep=""), x$Description)
  768. x$Description <- factor(x$Description, levels = rev(unique(x$Description)))
  770. } else if (orderBy=="GeneRatio"){
  771. enrich.df %>% group_by(comparison) %>% arrange(p.adjust, .by_group = TRUE) -> x
  772. x %>% group_by(comparison) %>% arrange(p.adjust, .by_group = TRUE) %>% dplyr::top_n(n = show, wt = gene.ratio) %>% dplyr::pull(Description) -> terms
  773. x <- x[x$Description %in% terms,]
  774. x$Description <- ifelse(nchar(x$Description) > 80, paste(substr(x$Description, 1, 80),"[...]",sep=""), x$Description)
  775. x$Description <- factor(x$Description, levels = rev(unique(x$Description)))
  776. }else {
  777. "Please select orderBy='padj', orderBy='count'or orderBy='GeneRatio'."
  778. }
  780. ## coloring
  781. if(colorBy=="regulation" & scaleBy == "count"){
  782. p <- ggplot(data = x ,aes(x=comparison, y=Description, size=Count , fill=regulation)) +
  783. geom_point(pch=21) +
  784. scale_fill_manual(values = c(up = "firebrick4", down = "dodgerblue3")) +
  785. xlab("") +
  786. ylab("") +
  787. scale_radius() +
  788. theme_linedraw()+
  789. theme(axis.text.y = element_text(color = "black"),
  790. axis.text.x = element_text(angle=90, vjust=0.5,hjust=1, color = "black"),
  791. text = element_text(size = 12),
  792. panel.grid.major = element_line(colour = "grey70"))
  793. plot(p)
  795. } else if (colorBy=="regulation" & scaleBy == "GeneRatio"){
  796. p <- ggplot(data = x ,aes(x=comparison, y=Description, size=gene.ratio , fill=regulation)) +
  797. geom_point(pch=21) +
  798. scale_fill_manual(values = c(up = "firebrick4", down = "dodgerblue3")) +
  799. xlab("") +
  800. ylab("") +
  801. scale_radius() +
  802. theme_linedraw()+
  803. theme(axis.text.y = element_text(color = "black"),
  804. axis.text.x = element_text(angle=90, vjust=0.5,hjust=1, color = "black"),
  805. text = element_text(size = 12),
  806. panel.grid.major = element_line(colour = "grey70"))
  807. plot(p)
  809. } else if (colorBy=="padj" & scaleBy=="count"){
  810. p <- ggplot(data = x, aes(x=comparison, y=Description, size=Count, color=p.adjust)) +
  811. geom_point(pch=21) +
  812. scale_colour_gradientn(colours = c('red', 'orange', 'darkblue', 'darkblue'),
  813. limits = c(0, 1),
  814. values = c(0, 0.05, 0.2, 0.5, 1),
  815. breaks = c(0.05, 0.2, 1),
  816. labels = format(c(0.05, 0.2, 1))) +
  817. xlab("") +
  818. ylab("") +
  819. scale_radius() +
  820. theme_linedraw()+
  821. theme(axis.text.y = element_text(color = "black"),
  822. axis.text.x = element_text(angle=90, vjust=0.5, hjust=1, color = "black"),
  823. text = element_text(size = 12),
  824. panel.grid.major = element_line(colour = "grey70"))
  825. plot(p)
  827. } else if (colorBy=="padj" & scaleBy=="GeneRatio"){
  828. p <- ggplot(data = x, aes(x=comparison, y=Description, size=gene.ratio, color=p.adjust)) +
  829. geom_point(pch=21) +
  830. scale_colour_gradientn(colours = c('red', 'orange', 'darkblue', 'darkblue'),
  831. limits = c(0, 1),
  832. values = c(0, 0.05, 0.2, 0.5, 1),
  833. breaks = c(0.05, 0.2, 1),
  834. labels = format(c(0.05, 0.2, 1))) +
  835. xlab("") +
  836. ylab("") +
  837. scale_radius() +
  838. theme_linedraw()+
  839. theme(axis.text.y = element_text(color = "black"),
  840. axis.text.x = element_text(angle=90, vjust=0.5,hjust=1, color = "black"),
  841. text = element_text(size = 12),
  842. panel.grid.major = element_line(colour = "grey70"))
  843. plot(p)
  845. }else {
  846. "Please select colorBy='regulation' or colorBy='padj' and scaleBy='count' or scaleBy='GeneRatio'."
  847. }
  848. }
  849. }
  851. ```
  853. ### Volcano Plot
  854. ```{r}
  855. plotVolcano <- function(DEresults_obj = DEresults,
  856. comparison,
  857. labelnum=20,
  858. y.max = NULL,
  859. signature = NULL){
  860. # specify labeling
  861. upDE <- as.data.frame(DEresults_obj[[comparison]]@results[DEresults_obj[[comparison]]@results$regulation =="up",])
  862. if(!is.null(signature))
  863. upDE <- upDE %>% dplyr::filter(.,SYMBOL %in% signature)
  864. FClabel_up <- upDE[order(abs(upDE$log2FoldChange), decreasing = TRUE),]
  865. if(nrow(FClabel_up)>labelnum){
  866. FClabel_up <- as.character(FClabel_up[c(1:labelnum),"GENEID"])
  867. } else {
  868. FClabel_up <- as.character(FClabel_up$GENEID)}
  869. plabel_up <- upDE[order(upDE$padj, decreasing = FALSE),]
  870. if(nrow(plabel_up)>labelnum){
  871. plabel_up <- as.character(plabel_up[c(1:labelnum),"GENEID"])
  872. } else {
  873. plabel_up <- as.character(plabel_up$GENEID)}
  875. downDE <- as.data.frame(DEresults_obj[[comparison]]@results[DEresults_obj[[comparison]]@results$regulation =="down",])
  876. if(!is.null(signature))
  877. downDE <- downDE %>% dplyr::filter(SYMBOL %in% signature)
  878. FClabel_down <- downDE[order(abs(downDE$log2FoldChange), decreasing = TRUE),]
  879. if(nrow(FClabel_down)>labelnum){
  880. FClabel_down <- as.character(FClabel_down[c(1:labelnum),"GENEID"])
  881. } else {
  882. FClabel_down <- as.character(FClabel_down$GENEID)}
  883. plabel_down <- downDE[order(downDE$padj, decreasing = FALSE),]
  884. if(nrow(plabel_down)>labelnum){
  885. plabel_down <- as.character(plabel_down[c(1:labelnum),"GENEID"])
  886. } else {
  887. plabel_down <- as.character(plabel_down$GENEID)}
  890. label<- unique(c(FClabel_up, plabel_up, FClabel_down, plabel_down))
  892. data <- DEresults_obj[[comparison]]@results
  893. data$label<- ifelse(data$GENEID %in% label == "TRUE",as.character(data$SYMBOL), "")
  894. data <- data[,colnames(data) %in% c("label", "log2FoldChange", "padj", "regulation")]
  895. data$color <- apply(data, 1, function(x){
  896. if(x["label"] != "") "black"
  897. else x["regulation"]
  898. }) %>% unlist()
  899. limits <- ceiling(max(DEresults_obj[[comparison]]@results$log2FoldChange))
  901. # Volcano Plot
  902. volcano <- ggplot(data=na.omit(data), aes(x=log2FoldChange, y=-log10(padj), fill=regulation, color = color)) +
  903. geom_point(shape = 21, alpha=0.75, size=1.75) +
  904. scale_color_manual(values = c(c("down" = "dodgerblue3","n.s." = "grey","up" = "firebrick", "black" = "black"))) +
  905. scale_fill_manual(values=c("down" = "dodgerblue3", "n.s." = "grey", "up" = "firebrick"))+
  906. xlab("log2(FoldChange)") +
  907. ylab("-log10(padj)") +
  908. geom_vline(xintercept = c(-log(DEresults_obj$parameters$sigFC,2),log(DEresults_obj$parameters$sigFC,2)), colour="darkgrey", linetype = "dashed")+
  909. geom_hline(yintercept=-log(0.05,10),colour="darkgrey", linetype = "dashed")+
  910. geom_text_repel(data=na.omit(data[!data$label =="",]),aes(label=label), size=3)+
  911. scale_x_continuous(limits = c(-(limits), limits), breaks = seq(-(limits), limits, by=1))+
  912. guides(color="none") +
  913. ggtitle(paste("Volcano Plot of: ",comparison,sep="")) +
  914. theme_bw() +
  915. theme(panel.grid.minor = element_blank())
  917. if(!is.null(y.max))
  918. volcano <- volcano +
  919. scale_y_continuous(limits = c(0, y.max), breaks = seq(0, y.max, by=1))
  920. else
  921. volcano <- volcano +
  922. scale_y_continuous()
  924. volcano
  925. }
  927. ```
  930. # 2. Project information
  933. ```{r}
  934. organism = "mouse"
  935. ```
  938. # 3. Data Import
  940. ## 3.1 Load gene annotation
  943. ```{r gene annotation import}
  945. tx_annotation <- tx_import(file = "/data/references/mouse/mm10/ID2SYMBOL_gencode_vM25.txt",
  946. aligner = "STAR")
  949. ```
  952. ## 3.2 Load gene set annotation
  954. ```{r}
  955. GO_terms <- read.gmt("/data/analysis/m5.go.bp.v2024.1.Mm.symbols.gmt")
  956. ```
  959. ## 3.3 Load sample table
  961. ```{r sample table import}
  962. # Import your sample table
  963. sample_table <- read.xlsx("/data/analysis/sample_table_final.xlsx")
  964. rownames(sample_table) <- sample_table$ID
  966. sample_table
  967. ```
  970. ### Format sample table
  972. ```{r colour definitions}
  973. unique(sample_table$condition)
  974. ## Add columns with factors for comparisons in model
  975. sample_table$condition <- factor(sample_table$condition,
  976. levels = c("crtx_PBS","crtx_IgG","crtx_LON5","hippo_PBS","hippo_IgG","hippo_LON5"))
  977. sample_table$tissue <- factor(sample_table$tissue,
  978. levels = c("crtx", "hippo"))
  980. sample_table$treat<- factor(sample_table$treat,
  981. levels = c("PBS", "IgG", "LON5"))
  983. sample_table$donor <- factor(sample_table$donor,
  984. levels= c("14", "32", "33", "38", "74", "10", "11", "15", "37", "75", "8", "9", "16", "17"))
  986. # define factor for order of samples in plotting
  987. plot_order <- c("condition")
  988. ```
  991. ### Colour scheme customization
  993. ```{r}
  994. col_condition <- c("#C2E0EB", "#83C5BE","#006D77", "#FFDDD2","#E29578", "#CA582B")
  995. names(col_condition) <- c("crtx_PBS","crtx_IgG","crtx_LON5","hippo_PBS","hippo_IgG","hippo_LON5")
  997. col_tissue <- c("#006D77", "#CA582B")
  998. names(col_tissue) <- c("crtx", "hippo")
  1000. col_treatment <- c("#fefae0", "#dda15e","#bc6c25" )
  1001. names(col_treatment) <- c("PBS", "IgG", "LON5")
  1003. col_donor <- c("#155289", "#95AAD3", "#B9DBF4", "#B8E2DE", "#94C47D", "#F0CF7F", "#F6BF93","#EDAEAE", "#E07F80", "#780000", "#C195C4", "#9B5C97", "#C43E96", "grey")
  1004. names(col_donor) <- c("14", "32", "33", "38", "74", "10", "11", "15", "37", "75", "8", "9", "16", "17")
  1006. # combine color code into list
  1007. ann_colors <- list(condition= col_condition,
  1008. tissue = col_tissue,
  1009. treat = col_treatment,
  1010. donor = col_donor
  1011. )
  1012. ```
  1015. ```{r}
  1017. alignstat <-sample_table[ , c("ID", "uniquely_mapped","total_reads")]
  1018. alignstat <-gather(alignstat, "total_reads", "uniquely_mapped", key = "type", value = "reads")
  1020. ggplot(alignstat, aes(x = reorder(ID,-reads), y = reads, fill = type)) +
  1021. geom_bar(stat = "identity", position = "dodge", colour = "black") +
  1022. scale_y_continuous(labels = scales::label_number(big.mark = ",")) +
  1023. scale_fill_manual(name = "", values = c("white", "grey")) +
  1024. ylab("Number of reads") +
  1025. xlab("Sample IDs") +
  1026. geom_hline(yintercept=5 * 10^6, linetype="dashed", color = "red") +
  1027. ggtitle("Total reads and uniquely mapped reads") +
  1028. theme(axis.text.x = element_text(angle = 90, size = 9, hjust = 1, vjust = .5),
  1029. strip.background = element_rect(fill = "white"),
  1030. panel.background = element_rect(fill = "white", colour = "black"),
  1031. legend.position = "bottom")
  1034. rm(alignstat)
  1035. ```
  1038. ### QC
  1040. ```{r qc sample exclusion}
  1041. # define read cutoff
  1042. samples_to_keep <- sample_table[sample_table$uniquely_mapped > 5000000,]$ID
  1044. length(samples_to_keep)
  1045. nrow(sample_table) - length(samples_to_keep)
  1047. sample_table <- sample_table[which(sample_table$ID %in% samples_to_keep),]
  1048. rownames(sample_table) <- sample_table$ID
  1050. ```
  1053. # 4. Import of count files
  1055. ```{r}
  1056. star.count <- read.table(file = "/data/analysis/count_matrix.tsv",
  1057. row.names = 1,
  1058. header = T,
  1059. stringsAsFactors = F,
  1060. check.names = F)
  1062. star.count <- star.count[, colnames(star.count) %in% sample_table$ID]
  1065. ```
  1071. # 5. Building the DESeqDataSet
  1074. ```{r}
  1075. identical(as.character(rownames(sample_table)), as.character(colnames(star.count)))
  1077. # Reorder the rows of the sample_table matrix if necessary (based on Occam's razor [least number of moves for reordering])
  1078. sample_table <- sample_table[match(colnames(star.count), rownames(sample_table)),]
  1080. dds_txi <- DESeqDataSetFromMatrix(countData = star.count,
  1081. colData = sample_table,
  1082. design = ~condition)
  1084. rm(star.count)
  1085. ```
  1088. ## 5.1 Pre-filtering
  1091. ```{r pre-filtering}
  1092. genes_to_keep <- rowSums(counts(dds_txi) >= 10) >= min(table(sample_table$condition)) #number of donors and number of different input amounts
  1094. table(genes_to_keep)
  1096. dds <- dds_txi[genes_to_keep,]
  1097. ```
  1099. **Number of genes after filtering is:** `r sum(genes_to_keep) `
  1101. ## 5.2 DESeq calculations
  1102. ```{r DESeq calculation}
  1103. dds <- DESeq(dds)
  1104. ```
  1106. ## 5.3 Normalized counts
  1107. ```{r gene annotation}
  1108. norm_anno <- generate_norm_anno(dds_object = dds)
  1109. ```
  1112. Boxplot of normalized samples
  1113. ```{r, fig.height=5, fig.width=8}
  1114. boxplot_norm(norm_anno = norm_anno,
  1115. col.by = "condition",
  1116. col.use = col_condition)
  1117. ```
  1119. ## 5.4 Variance stabilizing transformation
  1120. ```{r varStab}
  1121. if (nrow(colData(dds)) < 30) {
  1122. dds_vst <- rlog(dds, blind = TRUE)
  1123. } else dds_vst <- vst(dds, blind = TRUE)
  1124. ```
  1127. ```{r vst matrix}
  1128. vst <- generate_vst_anno(dds_object = dds_vst)
  1129. vst_anno_log <- vst$log
  1130. vst_anno <- vst$unlog
  1131. ncol(vst_anno_log)
  1132. nrow(sample_table)
  1133. vst_anno_log[1:3,c(1:2, (ncol(vst_anno_log)-5):ncol(vst_anno_log))]
  1134. vst_anno[1:3,c(1:2, (ncol(vst_anno)-5):ncol(vst_anno))]
  1135. ```
  1138. # 6. Exploratory Data Analysis
  1139. ```{r}
  1140. # choose columns from the sample table for the heatmap annotation
  1141. plot_annotation <- sample_table[ , c("condition",
  1142. "donor",
  1143. "treat",
  1144. "tissue"),
  1145. drop = F]
  1146. rownames(plot_annotation) <- sample_table$ID
  1149. ```
  1152. ## 6.2 Principle Component Analysis
  1154. ### Supplementary Figure S3 i
  1156. ```{r, fig.width=6, fig.height=5}
  1158. plotPCA(ntop = "all",
  1159. xPC = 1,
  1160. yPC = 2,
  1161. color = "condition",
  1162. anno_colour = col_condition,
  1163. shape = "input",
  1164. point_size = 3,
  1165. add_density = F,
  1166. add_silhouette = F,
  1167. label = NULL,
  1168. title ="Condition")
  1169. ```
  1171. # 7. Differential Expression Analysis
  1173. ```{r}
  1174. comparison_table <- data.frame(control = c("crtx_PBS" ,"crtx_PBS" ,"crtx_IgG" , "hippo_PBS", "hippo_PBS", "hippo_IgG"),
  1175. comparison = c("crtx_IgG" , "crtx_LON5", "crtx_LON5","hippo_IgG", "hippo_LON5", "hippo_LON5" ))
  1177. ```
  1180. ```{r}
  1181. dds_dea <- dds
  1184. DEresults_list <- list()
  1186. for (i in unique(comparison_table$control)) {
  1187. print(i)
  1188. dds_dea$condition <- relevel(dds_dea$condition, i)
  1189. dds_dea <- nbinomWaldTest(object = dds_dea)
  1190. comparison_table_subset <- comparison_table[comparison_table$control == i, ]
  1192. DEresults <- DEAnalysis(input = dds_dea,
  1193. comparison_table = comparison_table_subset,
  1194. condition = "condition",
  1195. alpha = 0.05 ,
  1196. lfcThreshold = 0,
  1197. sigFC = 2,
  1198. multiple_testing = "IHW",
  1199. pAdjustMethod = "BH",
  1200. shrinkage = TRUE,
  1201. shrinkType = "apeglm")
  1202. DEresults_list <- c(DEresults_list, DEresults)
  1203. }
  1205. DEresults <- DEresults_list[unique(names(DEresults_list))]
  1207. ```
  1210. ### Summary of DE genes
  1211. #### Figure 3 j
  1212. ```{r, fig.height=3, fig.width=6}
  1213. DEcounts <- NULL
  1215. for(i in 1:nrow(comparison_table)){
  1216. tmp <- unlist(DEresults[[1+i]]@Number_DE_genes)
  1217. DEcounts <- rbind(DEcounts, tmp)
  1218. }
  1220. rownames(DEcounts) <- names(DEresults)[-1]
  1222. DEcounts
  1224. DEcounts_melt <- reshape2::melt(DEcounts)
  1226. # ggplot(DEcounts_melt, aes(x = Var1, fill = Var2)) +
  1227. # geom_col(aes(y = value), position = "dodge", color = "black") +
  1228. # geom_text(aes(label = value, y = value), position = position_dodge(.9), hjust=1) +
  1229. # theme_bw()+
  1230. # theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5)) +
  1231. # scale_fill_manual(values = c("up_regulated_Genes" = "firebrick", "down_regulated_Genes" = "cornflowerblue")) +
  1232. # guides(fill = guide_legend(title = "Mode of regulation")) +
  1233. # xlab("Comparison") + ylab("Number of DEGs") + coord_flip()
  1235. ggplot(DEcounts_melt[DEcounts_melt$Var1 %in% c("hippo_IgG_vs_hippo_PBS", "hippo_LON5_vs_hippo_PBS", "hippo_LON5_vs_hippo_IgG"),], aes(x = Var1, fill = Var2)) +
  1236. geom_col(aes(y = value), position = "dodge", color = "black") +
  1237. geom_text(aes(label = value, y = value), position = position_dodge(.9), hjust=1) +
  1238. theme_bw()+
  1239. theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5)) +
  1240. scale_fill_manual(values = c("up_regulated_Genes" = "firebrick", "down_regulated_Genes" = "cornflowerblue")) +
  1241. guides(fill = guide_legend(title = "Mode of regulation")) +
  1242. xlab("Comparison") + ylab("Number of DEGs") + coord_flip()
  1243. ```
  1247. ### Upset Plot
  1248. #### Supplementary Figure S3 k
  1249. ```{r}
  1250. list_up <- list()
  1251. list_down <- list()
  1252. for(i in 1:nrow(comparison_table)){
  1253. comp <- paste0(comparison_table[i,2], "_vs_", comparison_table[i,1])
  1254. list_up[[paste0(comp, "_up")]] <- DEresults[[comp]]@DE_genes[["up_regulated_Genes"]][["SYMBOL"]]
  1255. list_down[[paste0(comp, "_down")]] <- DEresults[[comp]]@DE_genes[["down_regulated_Genes"]][["SYMBOL"]]
  1256. }
  1259. upset(fromList(list_up), nsets = 10,nintersects = 100, keep.order = F, sets.bar.color = "firebrick")
  1261. upset(fromList(list_down), nsets = 10,nintersects = 100, keep.order = F, sets.bar.color = "dodgerblue")
  1262. ```
  1263. ## 7.2 General DE Gene analysis
  1264. ### Venn diagrams
  1265. #### Supplementary Figure S3 j
  1266. ```{r, fig.height=6, fig.width=6}
  1267. # plotVenn(comparisons = c("hippo_LON5_vs_hippo_IgG","hippo_LON5_vs_hippo_PBS", "crtx_LON5_vs_crtx_IgG", "crtx_LON5_vs_crtx_PBS"),
  1268. # regulation ="up")
  1269. plotVenn(comparisons = c("hippo_LON5_vs_hippo_IgG", "crtx_LON5_vs_crtx_IgG"),
  1270. regulation ="up")
  1271. plotVenn(comparisons = c("hippo_LON5_vs_hippo_PBS", "crtx_LON5_vs_crtx_PBS"),
  1272. regulation ="up")
  1275. ```
  1277. ### Gene Set Enrichment of DE genes
  1278. ```{r}
  1279. # define universe
  1280. universe <- unique(as.character(norm_anno$SYMBOL))
  1281. ```
  1283. ```{r, warnings=FALSE, message=FALSE}
  1284. GSEA_results <- enrichGSEA(comparisons = "hippo_LON5_vs_hippo_IgG",
  1285. DE_results = DEresults,
  1286. universe = universe,
  1287. GeneSets = c("GO"),#, "HALLMARK", "REACTOME"
  1288. pCorrection = "bonferroni",
  1289. pvalueCutoff = 0.05,
  1290. qvalueCutoff = 0.05)
  1291. ```
  1293. ```{r}
  1294. GSEA_results$GO$comparison <-
  1295. factor(GSEA_results$GO$comparison,
  1296. levels = c(unique(GSEA_results$GO$comparison)))
  1297. ```
  1299. Figure 3 l
  1300. ```{r, fig.height=6, fig.width=15}
  1301. dotplotGSEA(enrich.df = GSEA_results$GO, show = 20, orderBy = "padj", colorBy = "regulation", scaleBy = "count")
  1302. ```
  1304. ### Heatmaps of DE genes
  1305. #### Figure 3 i
  1307. ```{r}
  1308. sample_anno <- sample_table[sample_table$tissue == "hippo",]
  1309. input <- norm_anno[,c(sample_anno$ID,"GENEID", "SYMBOL", "GENETYPE" )]
  1310. gene_anno <- tx_annotation
  1311. geneset <- DEresults[["hippo_LON5_vs_hippo_IgG"]]@results[DEresults[["hippo_LON5_vs_hippo_IgG"]]@results$regulation %in% c("up","down"),"GENEID"]
  1314. input <- input[input$GENEID %in% geneset,]
  1315. input <- input[,colnames(input) %in% sample_anno$ID]
  1316. input_scale <- t(scale(t(input)))
  1317. input_scale<-as.data.frame(input_scale)
  1318. input_scale$GENEID <- rownames(input_scale)
  1319. gene_anno <- gene_anno[match(rownames(input_scale), gene_anno$GENEID),]
  1320. input_scale <- merge(input_scale,
  1321. gene_anno,
  1322. by = "GENEID")
  1323. rownames(input_scale) <- input_scale$SYMBOL
  1325. title=paste("Heatmap of significant DE genes in: ","hippo_LON5_vs_hippo_IgG",sep="")
  1327. print(plotHeatmap(input=input_scale,
  1328. sample_annotation= sample_anno,
  1329. geneset = geneset,
  1330. title = title,
  1331. keyType = "Ensembl",
  1332. show_rownames = F,
  1333. cluster_cols = F,
  1334. gene_type="all",
  1335. plot_mean = F))
  1336. ```
  1339. ### Volcano Plots
  1340. #### Figure 3 k
  1342. ```{r}
  1343. # Plot Volcano Plot
  1344. print(plotVolcano(DEresults_obj = DEresults, comparison = "hippo_LON5_vs_hippo_IgG", labelnum = 20))
  1345. ```
  1347. ```{r}
  1348. sessionInfo()
  1349. ```

250327_publication.Rmd at commit 39b95ee, under LGPL-3.0 · at the source

Overview

Authors: Bilge Askin1,2, Cagla Kilic1, César Cordero Gómez1,3, Sophie Lan-Linh Duong1,3,4, Alvaro Domingues-Baquero1, Alexander Goihl5, Karsten Nalbach6,7,8, Joana Petushi1, Pia Grundschöttel9,10,11, Jessica Wagner6,7,12, Valentine Thomas1, Janne Lamberty1, Emily Withers1, Hanna Huber11, Sabrina Huebschmann1, Ekaterina Semenova1, Paul Turko13, Andrew G. Newman14, Lisa Diez1, Marc Beyer9,11
and 11 other authorsElena De Domenico9,11, Peter Körtvelyessy3,15, Dirk Reinhold5, Anja Schneider11, Jonas J. Neher6,7,12, Thomas Ulas9,10,11, Stefan F. Lichtenthaler6,7,8, Benjamin R. Rost1, Dietmar Schmitz1,2,14,16, Harald Prüss1,2,3, Susanne Wegmann1,2
ORCID iDs: Bilge Askin, Cagla Kilic, César Cordero Gómez, Sophie Lan-Linh Duong, Karsten Nalbach, Joana Petushi, Pia Grundschöttel, Jessica Wagner, Valentine Thomas, Emily Withers, Paul Turko, Andrew G. Newman, Lisa Diez, Marc Beyer, Elena De Domenico, Peter Körtvelyessy, Dirk Reinhold, Anja Schneider, Jonas J. Neher, Thomas Ulas, Stefan F. Lichtenthaler, Benjamin R. Rost, Dietmar Schmitz, Harald Prüss, Susanne Wegmann
16 affiliations
  1. German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
  2. Einstein Center for Neurosciences (ECN), Charité – Universitätsmedizin Berlin, Berlin, Germany
  3. Department of Neurology and Experimental Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
  4. Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Berlin, Germany
  5. Institute of Molecular and Clinical Immunology, Otto-von-Guericke-University, Magdeburg, Germany
  6. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
  7. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
  8. Neuroproteomics, School of Medicine and Health, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
  9. Platform for Single Cell Genomics and Epigenomics (PRECISE) at the German Center for Neurodegenerative Diseases and the University of Bonn, Bonn, Germany
  10. Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
  11. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
  12. Metabolic Biochemistry, Biomedical Center Munich (BMC), Faculty of Medicine, Ludwig Maximilian University, Munich, Germany
  13. Institute of Integrative Neuroanatomy, Charité, Berlin, Germany
  14. Institute of Cell Biology and Neurobiology, Charité – Universitätsmedizin Berlin, Berlin, Germany
  15. German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
  16. Neuroscience Research Center (NWFZ), Charité-Universitätsmedizin, Berlin, Germany
Institutions: German Center for Neurodegenerative Diseases (Germany); Einstein Center for Neurosciences Berlin (Germany); Charité - Universitätsmedizin Berlin (Germany); Berlin Institute of Health at Charité - Universitätsmedizin Berlin (Germany); Otto-von-Guericke-Universität Magdeburg (Germany); TUM Klinikum (Germany); Munich Cluster for Systems Neurology (Germany); Technical University of Munich (Germany); University of Bonn (Germany); Ludwig-Maximilians-Universität München (Germany)
Journal: Science advances, volume 12, issue 20, article eaec2042
Dates: received 10 September 2025; accepted 8 April 2026; published online 13 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aec2042 · PMID 42127169 · PMCID PMC13170660 · OpenAlex W7160985357
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Autoantibodies*, Cell Adhesion Molecules, Neuronal*, Neurons*, tau Proteins*, Animals, Autoimmune Diseases, Hippocampus, Humans, Mice, Phosphorylation (* major topic)
Journal subjects: Neuroscience, Diseases and Disorders
Topic: Autoimmune Neurological Disorders and Treatments (Neurology, Medicine), according to OpenAlex
Funding: Helmholtz Association (HIL-A03 B); Deutsche Forschungsgemeinschaft (504745852, PR1274/5-1, PR1274/9-1, PR1274/13-1); Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) (01GM1908D); Deutsches Zentrum für Neurodegenerative Erkrankungen (German Center for Neurodegenerative Diseases); Einstein Stiftung Berlin; Deutsche Forschungsgesellschaft (327654276, 273915538)
Citations: cited by 1 paper (Europe PMC); 116 references in the paper

Abstract

Anti-IgLON5 disease is an autoimmune disease, in which autoantibodies (AABs) against the neuronal cell surface protein IgLON5 lead to profound brain dysfunction and Tau pathology. How α-IgLON5 AABs cause neuronal Tau protein pathology and neurodegeneration remains unclear. We find that patient-derived α-IgLON5 AABs cluster IgLON5 proteins with other cell surface proteins, leading to neuronal hyperactivity that triggers pathological Tau missorting and phosphorylation, typically observed early in Tau-related neurodegenerative diseases. In wild-type mice, α-IgLON5 AABs induce hippocampal Tau phosphorylation and neuroinflammatory responses. Our findings establish a causal link between the α-IgLON5 AABs and Tau pathology in anti-IgLON5 disease patients and highlight the role of neuronal hyperactivity as a disease-overarching driver of Tau pathology and provide a potential target for therapeutic intervention.

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

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At the source: doi.org/10.5281/zenodo.19454006

gitlab.dzne.de/grundschoettelp/iglon5-autoimmune-antibody-influence-on-neurons

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Data

Datasets cited

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials or are available as follows: The plasmid for enhanced green fluorescent protein (eGFP)–TauP301L/S320F expression can be provided by S.W.’s pending scientific review and a completed material transfer agreement with the DZNE. Requests for the plasmid for eGFP-TauP301L/S320F should be submitted to: . RNA-seq data generated in this study are available in the GEO at www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE298951, under the accession number GSE298951, with the token: mduriemyzlsvdej. Lists of DEGs can be found in data S1. Code for RNA-seq analysis is available at https://gitlab.dzne.de/grundschoettelp/iglon5-autoimmune-antibody-influence-on-neurons. The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD066225 (token i5YAGUXZ5Yv4, username: , password: rbRRDufu6h6K). Lists of differentially enriched proteins can be found in data S3.

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Recorded: type, language, journal, volume, issue, pages, dates, 31 authors, 10 MeSH terms, 6 funders, 115 references.

Cite

This paper

Askin, B., Kilic, C., Cordero Gómez, C., Duong, S. L.-L., Domingues-Baquero, A., Goihl, A., Nalbach, K., Petushi, J., Grundschöttel, P., Wagner, J., Thomas, V., Lamberty, J., Withers, E., Huber, H., Huebschmann, S., Semenova, E., Turko, P., Newman, A. G., Diez, L., . . . Wegmann, S. (2026). IgLON5 autoimmune antibodies activate Tau via neuronal hyperactivity. Science advances, 12(20), eaec2042. https://doi.org/10.1126/sciadv.aec2042

BibTeX

@article{askin2026iglon5,
author = {Askin, Bilge and Kilic, Cagla and Cordero Gómez, César and Duong, Sophie Lan-Linh and Domingues-Baquero, Alvaro and Goihl, Alexander and Nalbach, Karsten and Petushi, Joana and Grundschöttel, Pia and Wagner, Jessica and Thomas, Valentine and Lamberty, Janne and Withers, Emily and Huber, Hanna and Huebschmann, Sabrina and Semenova, Ekaterina and Turko, Paul and Newman, Andrew G. and Diez, Lisa and Beyer, Marc and De Domenico, Elena and Körtvelyessy, Peter and Reinhold, Dirk and Schneider, Anja and Neher, Jonas J. and Ulas, Thomas and Lichtenthaler, Stefan F. and Rost, Benjamin R. and Schmitz, Dietmar and Prüss, Harald and Wegmann, Susanne},
title = {{IgLON5 autoimmune antibodies activate Tau via neuronal hyperactivity}},
journal = {Science advances},
year = {2026},
month = may,
volume = {12},
number = {20},
pages = {eaec2042},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aec2042},
url = {https://doi.org/10.1126/sciadv.aec2042},
pmid = {42127169},
pmcid = {PMC13170660}
}

RIS

TY - JOUR
AU - Askin, Bilge
AU - Kilic, Cagla
AU - Cordero Gómez, César
AU - Duong, Sophie Lan-Linh
AU - Domingues-Baquero, Alvaro
AU - Goihl, Alexander
AU - Nalbach, Karsten
AU - Petushi, Joana
AU - Grundschöttel, Pia
AU - Wagner, Jessica
AU - Thomas, Valentine
AU - Lamberty, Janne
AU - Withers, Emily
AU - Huber, Hanna
AU - Huebschmann, Sabrina
AU - Semenova, Ekaterina
AU - Turko, Paul
AU - Newman, Andrew G.
AU - Diez, Lisa
AU - Beyer, Marc
AU - De Domenico, Elena
AU - Körtvelyessy, Peter
AU - Reinhold, Dirk
AU - Schneider, Anja
AU - Neher, Jonas J.
AU - Ulas, Thomas
AU - Lichtenthaler, Stefan F.
AU - Rost, Benjamin R.
AU - Schmitz, Dietmar
AU - Prüss, Harald
AU - Wegmann, Susanne
TI - IgLON5 autoimmune antibodies activate Tau via neuronal hyperactivity
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/05/13
VL - 12
IS - 20
SP - eaec2042
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aec2042
UR - https://doi.org/10.1126/sciadv.aec2042
LA - en
ER -

CSL-JSON

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"issue": "20",
"page": "eaec2042",
"DOI": "10.1126/sciadv.aec2042",
"PMID": "42127169",
"PMCID": "PMC13170660",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aec2042",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
13
]
]
}
}

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