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Beat- and Side-family cell-surface molecules are expressed combinatorially in the partner neurons of the olfactory circuit in Drosophila.

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  1. [1] § Results › Expression of Beat/Side proteins in the antennal lobe ↔ single-cell RNAseq/PN_Xie_elife_2021/Visualizing the beat-side expression across PNs.Rmd, lines 36–58 · score 0.86 · Beat IIIC, Beat Ib, Beat VII, Side VII, Beat IIb, Beat Va
  2. [2] § Results › Expression of Beat/Side proteins in the antennal lobe ↔ single-cell RNAseq/ORN_McLaughlin_elife_2021/Visualizing the beat-side expression across ORNs.Rmd, lines 37–59 · score 0.86 · Beat IIIC, Beat Ib, Beat VII, Side VII, Beat IIb, Beat Va
  3. [3] § Results › Evolutionarily conserved expression of beat/side orthologs across insect ORNs ↔ Evolutionary_Analyses_beats_sides_across_insect_species/scRNAseq_AAegypti/Analysis of beat-side expression in A. aeg ORNs.Rmd, lines 99–106 · score 0.78 · beat IVa, side IIa, beat VII, insect species, beat Va, side III
  4. [4] § Materials and methods › beat/side glomerular expression pattern analysis of beats/sides ↔ GAL4_ORN_expression_analysis/ORN GAL4 pattern analysis.Rmd, lines 77–117 · score 0.77 · beat Vc, beat Ic, beat IIIc, side III, ORN expression, beat VI
  5. [5] § Materials and methods › beat/side glomerular expression pattern analysis of beats/sides ↔ GAL4_PN_expression_analysis/PN GAL4 pattern analysis.Rmd, lines 34–57 · score 0.76 · beat Vc, beat Ic, beat IIIc, side III, PN expression, beat VI
  6. [6] § Results › Beats/Sides form a robust molecular interaction network between ORN and PN partners ↔ GAL4_ORN_expression_analysis/ORN GAL4 pattern analysis.Rmd, lines 77–117 · score 0.71 · beat IIIa, Beat IIIc, Beat IIb, side III, side VIII, split
  7. [7] § Materials and methods › Linear regression for the ORN expression breadth of beat/side orthologs between species ↔ Evolutionary_Analyses_beats_sides_across_insect_species/Cross-species ortholog expression breadth/linear regression of ortholog expression breadth.Rmd, lines 80–106 · score 0.64 · expression breadth, cross species, Linear, mosquito, ant, regression
  8. [8] § Results › A glomerular map of beat/side expression in PNs ↔ GAL4_PN_expression_analysis/PN GAL4 pattern analysis.Rmd, lines 60–65 · score 0.60 · VA1v, birth sequence, adPN, PN lineage, DA1, PN class
  9. [9] § Results › Evolutionarily conserved expression of beat/side orthologs across insect ORNs ↔ Evolutionary_Analyses_beats_sides_across_insect_species/Cross-species ortholog expression breadth/linear regression of ortholog expression breadth.Rmd, lines 80–106 · score 0.51 · Linear regression, expression breadth, insect species, orthologs, mosquitoes, ants

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The authors' code

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  1. ---
  2. title: "beat-side GAL4 in ORNs"
  3. output: html_notebook
  4. ---
  6. This analysis was based on the GAL4 expression pattern data updated in April 2026.
  8. ## Load packages.
  9. ```{r}
  10. library(tidyverse)
  11. library(magrittr)
  12. library(gplots)
  13. library(RColorBrewer)
  14. library(ggpubr)
  16. library(showtext)
  17. library(pheatmap)
  18. ```
  20. ## Load the dataset.
  21. ```{r}
  22. df <- read_csv("ORN beat-side expression matrix_2026.csv") %>%
  23. as.data.frame()
  25. df$Sensillar_type <- factor(df$Sensillar_type, levels = c("TSB", "LB", "AT", "AC", "PB", "SA", "AR", "UNK"))
  26. ```
  28. ```{r}
  29. #For binary analysis in Fig 2, set all non-zero values to 1. For the analysis in Fig S5 based on the binned expression levels, keep the original matrix.
  31. df_binary <- df
  33. df_binary[, -(1:5)] <- (df_binary[, -(1:5)] != 0) * 1
  34. ```
  37. ## Fig 2G. Lineage-biased expression of beat/side genes.
  38. ```{r}
  39. df_binary %>%
  40. group_by(Sensillar_type) %>%
  41. count()
  43. expression_SensillarType <- df_binary %>%
  44. group_by(Sensillar_type) %>%
  45. summarise(across(where(is.numeric), mean))
  47. expression_SensillarType <- expression_SensillarType[order(expression_SensillarType$Sensillar_type), ]
  48. expression_SensillarType
  50. #write_csv(expression_SensillarType, "expression_SensillarType.csv")
  51. ```
  54. ```{r}
  55. expression_SensillarType_binarized <- expression_SensillarType %>%
  56. mutate(across(where(is.numeric), ~ ifelse(. >= 0.6, 1, ifelse(. <= 0.4, 0, 0.5))))
  58. mtx_template <- expression_SensillarType_binarized %>%
  59. select(-Sensillar_type) %>%
  60. as.matrix()
  62. rownames(mtx_template) <- expression_SensillarType_binarized$Sensillar_type
  64. pheatmap(mtx_template[(1:6), ],
  65. color = colorRampPalette(c("white", "black"))(3),
  66. cluster_rows = FALSE,
  67. cluster_cols = FALSE,
  68. show_rownames = TRUE,
  69. show_colnames = TRUE,
  70. fontsize = 14,
  71. angle_col = 90,
  72. #border_color = "gray",
  73. )
  74. ```
  77. ```{r}
  78. expression_SensillarType_binarized <- expression_SensillarType %>%
  79. mutate(across(where(is.numeric), ~ ifelse(. >= 0.6, "present", ifelse(. <= 0.4, "absent", "either"))))
  80. #mutate(across(where(is.numeric), ~ ifelse(. > 0.5, "present", ifelse(. < 0.5, "absent", "either"))))
  82. #write_csv(expression_SensillarType_binarized, "expression_SensillarType_binarized.csv")
  84. expression_SensillarType_binarized_long <- expression_SensillarType_binarized %>%
  85. pivot_longer(cols = -Sensillar_type, names_to = "gene", values_to = "expression")
  88. expression_SensillarType_binarized_long$gene <-
  89. factor(expression_SensillarType_binarized_long$gene,
  90. levels = c("beat-Ia", "beat-IIa", "beat-IIb", "side", "side-III",
  91. "beat-IIIb", "beat-IIIc", "beat-IV", "beat-VII", "side-VI",
  92. "side-II", "side-V", "beat-Ib",
  93. "beat-Ic", "beat-Vc", "beat-VI", "side-IV",
  94. "beat-IIIa", "beat-Va", "side-VIII"))
  96. expression_SensillarType_binarized_long %>%
  97. filter(Sensillar_type != "UNK" & Sensillar_type != "AR") %>%
  98. ggplot(aes(x = gene, y = Sensillar_type)) +
  99. scale_y_discrete(limits = rev) +
  100. geom_dotplot(aes(fill = expression), binaxis = "y", stackdir = "center", dotsize = 2) +
  101. #scale_fill_gradientn(colors = c("white", "gray", "black")) +
  102. scale_fill_manual(values = c("present"="black", "absent"="white", "either"="gray")) +
  103. geom_vline(xintercept = c(5.5, 12.5, 17.5), linewidth = 0.5) +
  104. theme(
  105. axis.title.x = element_blank(),
  106. axis.title.y = element_blank(),
  107. axis.text.x = element_text(size = 14, angle = 45, vjust = 1, hjust = 1),
  108. axis.text.y = element_text(size = 14),
  109. #legend.position = "none",
  110. legend.title = element_text(size = 12),
  111. legend.text = element_text(size = 12),
  112. panel.border = element_rect(color = "black", fill = NA, linewidth = 0.5),
  113. panel.background = element_rect(fill = "white", color = NA)
  114. )
  116. #ggsave("expression_template_split_revision1.pdf")
  117. ```
  120. ## Fig S12D. Expression breadth of beat/side genes based on the GAL4 labeling data across ORNs.
  121. ```{r}
  122. df_binary %>%
  123. summarise(across(where(is.numeric), sum)) %>%
  124. t() %>%
  125. as.data.frame()
  127. df_binary %>%
  128. summarise(across(where(is.numeric), mean)) %>%
  129. t() %>%
  130. as.data.frame()
  133. df_binary[df_binary$Sensillar_type %in% c("TSB", "AT", "LB", "AC"), ] %>%
  134. nrow()
  136. df_binary[df_binary$Sensillar_type %in% c("TSB", "AT", "LB", "AC"), ] %>%
  137. summarise(across(where(is.numeric), sum)) %>%
  138. t() %>%
  139. as.data.frame()
  141. df_binary[df_binary$Sensillar_type %in% c("TSB", "AT", "LB", "AC"), ] %>%
  142. summarise(across(where(is.numeric), mean)) %>%
  143. t() %>%
  144. as.data.frame()
  145. ```
  148. ## Fig 2B, 2C, 2D, S5. Hierarchical clustering analysis of beat/side expression across ORNs based on the binary/binned GAL4 labeling data.
  150. ### Fig 2B.
  151. ```{r}
  152. mtx <- df_binary[-c(1,2,3,4,5)] %>% as.matrix()
  154. rownames(mtx) <- df_binary[["Glomerulus"]]
  155. ```
  158. ```{r, fig.width = 30, fig.height = 36}
  159. row_colors <- brewer.pal(length(unique(df$Sensillar_type)), "Set2")[as.numeric(df$Sensillar_type)]
  161. #pdf("heatmap_revision_jaccard.pdf", width = 30,height = 36)
  163. heatmap.2(mtx,
  164. col = colorRampPalette(c("white", "dodgerblue"))(2),
  165. dendrogram = "both",
  166. trace = "none",
  167. RowSideColors = row_colors,
  168. colsep = 0:ncol(mtx),
  169. rowsep = 0:nrow(mtx),
  170. sepcolor = "gray",
  171. sepwidth = c(0.01, 0.01),
  172. cexRow = 3, # Font size for row labels
  173. cexCol = 4, # Font size for column labels
  174. key = FALSE,
  175. #density.info = "none",
  176. #keysize = 0.6, # smaller than default (default ~1.5)
  177. #key.par = list(mar = c(3, 3, 1, 1)) # shrink margins around key
  178. )
  180. legend("topleft",
  181. legend = sort(unique(df$Sensillar_type)),
  182. fill = brewer.pal(length(unique(df$Sensillar_type)), "Set2"),
  183. title = "Sensillar types",
  184. cex = 4.5)
  186. #dev.off()
  187. ```
  190. ### Fig 2C.
  191. ```{r}
  192. # Exclude two glomeruli that are innervated by more than one type of ORNs.
  193. df <- df_binary[df_binary$Glomerulus != "VP4" & df_binary$Glomerulus != "VL1", ]
  195. mtx <- df[-c(1,2,3,4,5)] %>% as.matrix()
  196. rownames(mtx) <- df[["Glomerulus"]]
  198. mtx_rcor <- cor(t(mtx))
  199. ```
  202. ```{r}
  203. similarity <- numeric()
  204. relation <- character()
  206. ndim = nrow(mtx_rcor)
  208. for (i in 1:(ndim-1)) {
  209. for (j in (i+1):ndim) {
  210. similarity = append(similarity, mtx_rcor[i, j])
  212. if (df$Sensillum[i] == df$Sensillum[j]) {
  213. relation = append(relation, "within sensillum")
  214. } else if (df$Sensillar_type[i] == df$Sensillar_type[j]) {
  215. relation = append(relation, "within sensillar type")
  216. } else {
  217. relation = append(relation, "between sensillar types")
  218. }
  219. }
  220. }
  221. ```
  224. ```{r}
  225. similarity_summary <- data.frame(similarity = similarity, relation = relation)
  227. similarity_summary$relation %<>% factor(levels = c("between sensillar types", "within sensillar type", "within sensillum"))
  229. #write_csv(similarity_summary, "Fig 2C_ORN_similarity_summary.csv")
  230. ```
  233. ```{r}
  234. similarity_summary %>%
  235. dplyr::group_by(relation) %>%
  236. dplyr::count()
  238. similarity_summary %>%
  239. dplyr::group_by(relation) %>%
  240. dplyr::summarise(mean(similarity, na.rm = FALSE), median(similarity, na.rm = FALSE))
  242. compare_means(similarity ~ relation, data = similarity_summary)
  243. ```
  246. ```{r}
  247. my_comparison <- list(c("between sensillar types", "within sensillar type"),
  248. c("between sensillar types", "within sensillum"),
  249. c("within sensillar type", "within sensillum"))
  250. ```
  253. ```{r}
  254. showtext_auto()
  256. data <- similarity_summary
  258. ggplot(data, aes(x = relation, y = similarity, fill = relation)) +
  259. geom_boxplot() +
  260. #geom_violin() +
  261. #geom_jitter() +
  262. geom_signif(
  263. comparisons = my_comparison,
  264. na.rm = TRUE,
  265. #test = "t.test",
  266. step_increase = 0.1,
  267. textsize = 4,
  268. map_signif_level = function(p) sprintf("%.2g", p),
  269. #y_position = 0.5
  270. ) +
  271. scale_fill_manual(values = c(
  272. "between sensillar types" = "#9BC985",
  273. "within sensillar type" = "#F7D58B",
  274. "within sensillum" = "#B595BF"
  275. )) +
  276. #ylim(NA, 1) +
  277. #coord_fixed(ratio=1) +
  278. theme_classic() +
  279. theme(aspect.ratio = 1) +
  280. theme(#axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
  281. axis.text.x = element_blank(),
  282. axis.title.x=element_blank(),
  283. axis.title.y=element_text(size = 18),
  284. legend.title=element_text(size = 18),
  285. legend.text=element_text(size = 15)
  286. )
  288. #ggsave("similarity_comparison_spectrum.pdf")
  289. ```
  292. ### Fig 2D.
  293. ```{r}
  294. within_sensillum_all <- numeric()
  295. within_type_all <- numeric()
  296. between_type_all <- numeric()
  298. # Run multiple times of shuffling.
  299. for (x in 1789:2024) {
  301. set.seed(x)
  302. df_shuffled <- df[sample(1:nrow(df)), ]
  304. mtx <- df_shuffled[-c(1,2,3,4,5)] %>% as.matrix()
  305. mtx_rcor <- cor(t(mtx))
  307. within_sensillum <- numeric()
  308. within_type <- numeric()
  309. between_type <- numeric()
  311. ndim = nrow(mtx_rcor)
  313. for (i in 1:(ndim-1)) {
  315. for (j in (i+1):ndim) {
  317. if (df$Sensillum[i] == df$Sensillum[j]) {
  318. within_sensillum = append(within_sensillum, mtx_rcor[i, j])
  319. } else if (df$Sensillar_type[i] == df$Sensillar_type[j]) {
  320. within_type = append(within_type, mtx_rcor[i, j])
  321. } else {
  322. between_type <- append(between_type, mtx_rcor[i, j])
  323. }
  324. }
  325. }
  327. within_sensillum_all = append(within_sensillum_all, mean(within_sensillum))
  328. within_type_all = append(within_type_all, mean(within_type))
  329. between_type_all <- append(between_type_all, mean(between_type))
  330. }
  332. # Summary of all shuffling runs.
  333. similarity_summary_all <- data.frame(similarity = c(within_sensillum_all, within_type_all, between_type_all),
  334. relation = c(rep("within sensillum", length(within_sensillum_all)),
  335. rep("within sensillar type", length(within_type_all)),
  336. rep("between sensillar types", length(between_type_all))
  337. )
  338. )
  340. # Summary of the last one (with seed = 2024) as the representative shuffling result.
  341. similarity_summary <- data.frame(similarity = c(within_sensillum, within_type, between_type),
  342. relation = c(rep("within sensillum", length(within_sensillum)),
  343. rep("within sensillar type", length(within_type)),
  344. rep("between sensillar types", length(between_type))
  345. )
  346. )
  348. similarity_summary_all$relation %<>% factor(levels = c("between sensillar types", "within sensillar type", "within sensillum"))
  349. similarity_summary$relation %<>% factor(levels = c("between sensillar types", "within sensillar type", "within sensillum"))
  351. #write_csv(similarity_summary, "Fig 2D_ORN_similarity_summary_shuffled.csv")
  352. ```
  355. ```{r}
  356. similarity_summary_all %>%
  357. dplyr::group_by(relation) %>%
  358. dplyr::count()
  360. similarity_summary_all %>%
  361. dplyr::group_by(relation) %>%
  362. dplyr::summarise(mean(similarity, na.rm = FALSE), median(similarity, na.rm = FALSE))
  364. compare_means(similarity ~ relation, data = similarity_summary_all)
  367. similarity_summary %>%
  368. dplyr::group_by(relation) %>%
  369. dplyr::count()
  371. similarity_summary %>%
  372. dplyr::group_by(relation) %>%
  373. dplyr::summarise(mean(similarity, na.rm = FALSE), median(similarity, na.rm = FALSE))
  375. compare_means(similarity ~ relation, data = similarity_summary)
  376. ```
  379. ```{r}
  380. my_comparison <- list(c("between sensillar types", "within sensillar type"),
  381. c("between sensillar types", "within sensillum"),
  382. c("within sensillar type", "within sensillum"))
  385. showtext_auto()
  387. data <- similarity_summary
  389. ggplot(data, aes(x = relation, y = similarity, fill = relation)) +
  390. geom_boxplot() +
  391. #geom_violin() +
  392. #geom_jitter() +
  393. geom_signif(
  394. comparisons = my_comparison,
  395. na.rm = TRUE,
  396. #test = "t.test",
  397. step_increase = 0.1,
  398. textsize = 4,
  399. map_signif_level = function(p) sprintf("%.2g", p),
  400. #y_position = 0.5
  401. ) +
  402. scale_fill_manual(values = c(
  403. "between sensillar types" = "#9BC985",
  404. "within sensillar type" = "#F7D58B",
  405. "within sensillum" = "#B595BF"
  406. )) +
  407. #ylim(NA, 1) +
  408. #coord_fixed(ratio=1) +
  409. theme_classic() +
  410. theme(aspect.ratio = 1) +
  411. theme(#axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
  412. axis.text.x = element_blank(),
  413. axis.title.x=element_blank(),
  414. axis.title.y=element_text(size = 18),
  415. legend.title=element_text(size = 18),
  416. legend.text=element_text(size = 15)
  417. )
  419. #ggsave("similarity_comparison_spectrum_shuffle.pdf")
  420. ```
  425. ### Fig S5A.
  426. ```{r}
  427. # Reload the original binned dataset.
  428. df <- read_csv("ORN beat-side expression matrix_2026.csv") %>%
  429. as.data.frame()
  431. df$Sensillar_type <- factor(df$Sensillar_type, levels = c("TSB", "LB", "AT", "AC", "PB", "SA", "AR", "UNK"))
  433. mtx <- df[-c(1,2,3,4,5)] %>% as.matrix()
  435. rownames(mtx) <- df[["Glomerulus"]]
  436. ```
  439. ```{r, fig.width = 30, fig.height = 36}
  440. row_colors <- brewer.pal(length(unique(df$Sensillar_type)), "Set2")[as.numeric(df$Sensillar_type)]
  443. #pdf("heatmap_revision_jaccard.pdf", width = 30,height = 36)
  445. pal <- brewer.pal(4, "Blues")
  446. pal[1] <- "white"
  448. heatmap.2(mtx,
  449. col = pal,
  450. dendrogram = "both",
  451. trace = "none",
  452. RowSideColors = row_colors,
  453. colsep = 0:ncol(mtx),
  454. rowsep = 0:nrow(mtx),
  455. sepcolor = "gray",
  456. sepwidth = c(0.01, 0.01),
  457. cexRow = 3, # Font size for row labels
  458. cexCol = 4, # Font size for column labels
  459. key = FALSE,
  460. #density.info = "none",
  461. #keysize = 0.6, # smaller than default (default ~1.5)
  462. #key.par = list(mar = c(3, 3, 1, 1)) # shrink margins around key
  463. )
  465. legend("topleft",
  466. legend = sort(unique(df$Sensillar_type)),
  467. fill = brewer.pal(length(unique(df$Sensillar_type)), "Set2"),
  468. title = "Sensillar types",
  469. cex = 4.5)
  471. #dev.off()
  472. ```
  475. ### Fig S5B
  476. ```{r}
  477. # Exclude two glomeruli that are innervated by more than one type of ORNs.
  478. df <- df[df$Glomerulus != "VP4" & df$Glomerulus != "VL1",]
  480. mtx <- df[-c(1,2,3,4,5)] %>% as.matrix()
  481. rownames(mtx) <- df[["Glomerulus"]]
  483. mtx_rcor <- cor(t(mtx))
  484. ```
  487. ```{r}
  488. similarity <- numeric()
  489. relation <- character()
  491. ndim = nrow(mtx_rcor)
  493. for (i in 1:(ndim-1)) {
  494. for (j in (i+1):ndim) {
  495. similarity = append(similarity, mtx_rcor[i, j])
  497. if (df$Sensillum[i] == df$Sensillum[j]) {
  498. relation = append(relation, "within sensillum")
  499. } else if (df$Sensillar_type[i] == df$Sensillar_type[j]) {
  500. relation = append(relation, "within sensillar type")
  501. } else {
  502. relation = append(relation, "between sensillar types")
  503. }
  504. }
  505. }
  506. ```
  509. ```{r}
  510. similarity_summary <- data.frame(similarity = similarity, relation = relation)
  512. similarity_summary$relation %<>% factor(levels = c("between sensillar types", "within sensillar type", "within sensillum"))
  514. #write_csv(similarity_summary, "Fig S5B_ORN_similarity_summary_binned.csv")
  515. ```
  518. ```{r}
  519. similarity_summary %>%
  520. dplyr::group_by(relation) %>%
  521. dplyr::count()
  523. similarity_summary %>%
  524. dplyr::group_by(relation) %>%
  525. dplyr::summarise(mean(similarity, na.rm = FALSE), median(similarity, na.rm = FALSE))
  527. compare_means(similarity ~ relation, data = similarity_summary)
  528. ```
  531. ```{r}
  532. my_comparison <- list(c("between sensillar types", "within sensillar type"),
  533. c("between sensillar types", "within sensillum"),
  534. c("within sensillar type", "within sensillum"))
  535. ```
  538. ```{r}
  539. showtext_auto()
  541. data <- similarity_summary
  543. ggplot(data, aes(x = relation, y = similarity, fill = relation)) +
  544. geom_boxplot() +
  545. #geom_violin() +
  546. #geom_jitter() +
  547. geom_signif(
  548. comparisons = my_comparison,
  549. na.rm = TRUE,
  550. #test = "t.test",
  551. step_increase = 0.1,
  552. textsize = 4,
  553. map_signif_level = function(p) sprintf("%.2g", p),
  554. #y_position = 0.5
  555. ) +
  556. scale_fill_manual(values = c(
  557. "between sensillar types" = "#9BC985",
  558. "within sensillar type" = "#F7D58B",
  559. "within sensillum" = "#B595BF"
  560. )) +
  561. #ylim(NA, 1) +
  562. #coord_fixed(ratio=1) +
  563. theme_classic() +
  564. theme(aspect.ratio = 1) +
  565. theme(#axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
  566. axis.text.x = element_blank(),
  567. axis.title.x=element_blank(),
  568. axis.title.y=element_text(size = 18),
  569. legend.title=element_text(size = 18),
  570. legend.text=element_text(size = 15)
  571. )
  573. #ggsave("similarity_comparison_spectrum.pdf")
  574. ```
  577. ### Fig S5C
  578. ```{r}
  579. within_sensillum_all <- numeric()
  580. within_type_all <- numeric()
  581. between_type_all <- numeric()
  583. # Run multiple times of shuffling.
  584. for (x in 1789:2024) {
  586. set.seed(x)
  587. df_shuffled <- df[sample(1:nrow(df)), ]
  589. mtx <- df_shuffled[-c(1,2,3,4,5)] %>% as.matrix()
  590. mtx_rcor <- cor(t(mtx))
  592. within_sensillum <- numeric()
  593. within_type <- numeric()
  594. between_type <- numeric()
  596. ndim = nrow(mtx_rcor)
  598. for (i in 1:(ndim-1)) {
  600. for (j in (i+1):ndim) {
  602. if (df$Sensillum[i] == df$Sensillum[j]) {
  603. within_sensillum = append(within_sensillum, mtx_rcor[i, j])
  604. } else if (df$Sensillar_type[i] == df$Sensillar_type[j]) {
  605. within_type = append(within_type, mtx_rcor[i, j])
  606. } else {
  607. between_type <- append(between_type, mtx_rcor[i, j])
  608. }
  609. }
  610. }
  612. within_sensillum_all = append(within_sensillum_all, mean(within_sensillum))
  613. within_type_all = append(within_type_all, mean(within_type))
  614. between_type_all <- append(between_type_all, mean(between_type))
  615. }
  617. # Summary of all shuffling runs.
  618. similarity_summary_all <- data.frame(similarity = c(within_sensillum_all, within_type_all, between_type_all),
  619. relation = c(rep("within sensillum", length(within_sensillum_all)),
  620. rep("within sensillar type", length(within_type_all)),
  621. rep("between sensillar types", length(between_type_all))
  622. )
  623. )
  625. # Summary of the last one (with seed = 2024) as the representative shuffling result.
  626. similarity_summary <- data.frame(similarity = c(within_sensillum, within_type, between_type),
  627. relation = c(rep("within sensillum", length(within_sensillum)),
  628. rep("within sensillar type", length(within_type)),
  629. rep("between sensillar types", length(between_type))
  630. )
  631. )
  633. similarity_summary_all$relation %<>% factor(levels = c("between sensillar types", "within sensillar type", "within sensillum"))
  634. similarity_summary$relation %<>% factor(levels = c("between sensillar types", "within sensillar type", "within sensillum"))
  636. #write_csv(similarity_summary, "Fig S5C_ORN_similarity_summary_binned_shuffled.csv")
  637. ```
  640. ```{r}
  641. similarity_summary_all %>%
  642. dplyr::group_by(relation) %>%
  643. dplyr::count()
  645. similarity_summary_all %>%
  646. dplyr::group_by(relation) %>%
  647. dplyr::summarise(mean(similarity, na.rm = FALSE), median(similarity, na.rm = FALSE))
  649. compare_means(similarity ~ relation, data = similarity_summary_all)
  652. similarity_summary %>%
  653. dplyr::group_by(relation) %>%
  654. dplyr::count()
  656. similarity_summary %>%
  657. dplyr::group_by(relation) %>%
  658. dplyr::summarise(mean(similarity, na.rm = FALSE), median(similarity, na.rm = FALSE))
  660. compare_means(similarity ~ relation, data = similarity_summary)
  661. ```
  664. ```{r}
  665. my_comparison <- list(c("between sensillar types", "within sensillar type"),
  666. c("between sensillar types", "within sensillum"),
  667. c("within sensillar type", "within sensillum"))
  669. showtext_auto()
  671. data <- similarity_summary
  673. ggplot(data, aes(x = relation, y = similarity, fill = relation)) +
  674. geom_boxplot() +
  675. #geom_violin() +
  676. #geom_jitter() +
  677. geom_signif(
  678. comparisons = my_comparison,
  679. na.rm = TRUE,
  680. #test = "t.test",
  681. step_increase = 0.1,
  682. textsize = 4,
  683. map_signif_level = function(p) sprintf("%.2g", p),
  684. #y_position = 0.5
  685. ) +
  686. scale_fill_manual(values = c(
  687. "between sensillar types" = "#9BC985",
  688. "within sensillar type" = "#F7D58B",
  689. "within sensillum" = "#B595BF"
  690. )) +
  691. #ylim(NA, 1) +
  692. #coord_fixed(ratio=1) +
  693. theme_classic() +
  694. theme(aspect.ratio = 1) +
  695. theme(#axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
  696. axis.text.x = element_blank(),
  697. axis.title.x=element_blank(),
  698. axis.title.y=element_text(size = 18),
  699. legend.title=element_text(size = 18),
  700. legend.text=element_text(size = 15)
  701. )
  703. #ggsave("similarity_comparison_spectrum_shuffle.pdf")
  704. ```

ORN GAL4 pattern analysis.Rmd at commit 0585e16, under MIT · at the source

Overview

Authors: Qichen Duan1, Sumie Okuwa1, Rachel Estrella1, Chun Yeung1, Yu-Chieh David Chen2, Laura Quintana Rio3, Chengcheng Du1, Khanh M. Vien1, Pelin Cayirlioglu Volkan1
ORCID iDs: Chun Yeung, Pelin Cayirlioglu Volkan
  1. Department of Biology, Duke University, Durham, North Carolina, United States of America
  2. Department of Biology, New York University, New York, New York, United States of America
  3. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America
Institutions: Duke University (United States); New York University (United States); Columbia University (United States)
Journal: PLoS biology, volume 24, issue 8, article e3003955
Dates: received 1 June 2026; accepted 3 August 2026; published online 27 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003955 · PMID 42659622 · PMCID PMC13521578 · OpenAlex W7204466559
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (organism), drosophila (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Machine learning, Evoked potentials, fMRI & imaging
MeSH: Drosophila melanogaster*, Drosophila Proteins*, Membrane Proteins*, Olfactory Pathways*, Olfactory Receptor Neurons*, Animals, Animals, Genetically Modified, Neurons, Synapses (* major topic)
Journal subjects: Biology and Life Sciences, Cell Biology, Cellular Types, Animal Cells, Neurons, Afferent Neurons, Olfactory Receptor Neurons, Neuroscience, Cellular Neuroscience, Research and Analysis Methods, Animal Studies, Experimental Organism Systems, Model Organisms, Drosophila Melanogaster, Animal Models, Zoology, Entomology, Insects, Drosophila, Organisms, Eukaryota, Animals, Invertebrates, Arthropoda, Hymenoptera, Ants, Genetics, Gene Expression, Epigenetics, RNA interference, Genetic interference, Biochemistry, Nucleic acids, RNA, Anatomy, Nervous System, Synapses, Medicine and Health Sciences, Physiology, Electrophysiology, Neurophysiology, Medical Conditions, Infectious Diseases, Disease Vectors, Insect Vectors, Mosquitoes, Species Interactions
Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Division of Integrative Organismal Systems (2006471); National Eye Institute (F32EY032750, K99EY035757); National Institute of Neurological Disorders and Stroke (NS070644)
Citations: not cited yet (Europe PMC); 121 references in the paper

Abstract

Over the past decades, many molecular players have been uncovered to control distinct steps of olfactory circuit assembly in Drosophila. Among these, multi-member gene families encoding cell-surface proteins are of interest as they can act as neuron-specific recognition tags in combinations and contribute to circuit assembly in complex brains. Recently, a multi-protein interactome has been described between Beat and Side families of IgSF proteins. Here, we use newly generated gene trap transgenic driver lines to probe the spatial expression pattern of beat/side genes in olfactory receptor neurons (ORNs) and their synaptic target projection neurons (PNs). Our results revealed that each ORN/PN class expresses a specific combination of beat/side genes, hierarchically regulated by lineage-specific genetic programs. To explore whether the class-specific expression of beats/sides defines ORN-PN matching specificity, we perturbed presynaptic beat-IIa and postsynaptic side-IV in two ORN-PN partners. However, disruption of Beat-IIa-Side-IV interaction did not produce any significant mistargeting in these two examined glomeruli. Our expression mapping revealed that the Beat/Side interactome between ORNs and PNs appears to be error-tolerant, supporting the robust trans-synaptic recognition. Though without affecting general glomerular targeting, knockdown of side in ORNs leads to the reduction of synaptic development. Interestingly, we found conserved expression patterns of beat/side orthologs across ORNs in ants and mosquitoes, indicating the shared regulatory strategies specifying the expression of these duplicated paralogs in insect evolution. This also implies the biological significance of beats/sides in ORN circuit development or function, which is preserved under selective pressure across divergent insect lineages. Overall, this comprehensive analysis of expression patterns lays a foundation for in-depth functional investigations into how Beat/Side combinatorial expression contributes to the olfactory circuit assembly.

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 9 matches between paragraphs and lines of code.

volkanlab/beats_sides_olfactory_circuit_Drosophila_Duan_PlosBio2026

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0585e16bb5e8d66faa8988c0a1087339bc7ade57, 29 July 2026
Languages: R (16)
Size: 72 files, 16 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, 16 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (16 files), ggpubr (5 files), pheatmap (3 files), ggplot2 (2 files), Seurat (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
18 files
At the source: github.com/volkanlab/beats_sides_olfactory_circuit_Drosophila_Duan_PlosBio2026

Zenodo 21652764

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.21652764

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;
  • 16 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

The underlying data for each figure can be found in S1-5 Data. The customized codes to generate all illustrations, statistical graphs, and reanalysis of previously published RNA-seq datasets can be accessed in https://github.com/volkanlab/beats_sides_olfactory_circuit_Drosophila_Duan_PlosBio2026 and are archived in https://doi.org/10.5281/zenodo.21652764.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 9 MeSH terms, 3 funders, 121 references.

Cite

This paper

Duan, Q., Okuwa, S., Estrella, R., Yeung, C., Chen, Y.-C. D., Rio, L. Q., Du, C., Vien, K. M., & Volkan, P. C. (2026). Beat- and Side-family cell-surface molecules are expressed combinatorially in the partner neurons of the olfactory circuit in Drosophila. PLoS biology, 24(8), e3003955. https://doi.org/10.1371/journal.pbio.3003955

BibTeX

@article{duan2026beat,
author = {Duan, Qichen and Okuwa, Sumie and Estrella, Rachel and Yeung, Chun and Chen, Yu-Chieh David and Rio, Laura Quintana and Du, Chengcheng and Vien, Khanh M. and Volkan, Pelin Cayirlioglu},
title = {{Beat- and Side-family cell-surface molecules are expressed combinatorially in the partner neurons of the olfactory circuit in Drosophila}},
journal = {PLoS biology},
year = {2026},
month = aug,
volume = {24},
number = {8},
pages = {e3003955},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003955},
url = {https://doi.org/10.1371/journal.pbio.3003955},
pmid = {42659622},
pmcid = {PMC13521578}
}

RIS

TY - JOUR
AU - Duan, Qichen
AU - Okuwa, Sumie
AU - Estrella, Rachel
AU - Yeung, Chun
AU - Chen, Yu-Chieh David
AU - Rio, Laura Quintana
AU - Du, Chengcheng
AU - Vien, Khanh M.
AU - Volkan, Pelin Cayirlioglu
TI - Beat- and Side-family cell-surface molecules are expressed combinatorially in the partner neurons of the olfactory circuit in Drosophila
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/08/27
VL - 24
IS - 8
SP - e3003955
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003955
UR - https://doi.org/10.1371/journal.pbio.3003955
LA - en
ER -

CSL-JSON

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"container-title": "PLoS biology",
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"family": "Duan",
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