A genetic strategy for targeting local astrocytes in adult Drosophila.

1. ---
2. title: "synapses_by_roi"
3. output: html_document
4. date: "2024-05-31"
5. ---
7. ```{r setup, include=FALSE}
8. knitr::opts_chunk$set(echo = TRUE)
9. ```
11. ## R Markdown
13. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
15. When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
17. ```{r libraries}
18. library(neuronbridger)
19. library(neuprintr)
20. library(dplyr)
21. library(purrr)
22. library(data.table)
23. library(ggplot2)
24. ```
26. ```{r}
27. driver_list <- c("MB441B", "MB042B", "MB196B", "MB315C", "MB312B", "MB607B", "MB009B", "MB112C", "MB298B", "MB504B", "MB320C", "MB025B", "MB188B", "MB463B", "MB418B", "MB185B", "MB008B", "MB371B")
28. ```
30. ```{r}
31. conn = neuprintr::neuprint_login(server= "https://neuprint.janelia.org/",
32. dataset= "hemibrain:v1.2.1",
33. token= "xxx")
34. ```
36. 1. Get cell IDs for all drivers into same df
37. a. loop through lines with neuronbridge to get cell IDs
38. ```{r}
40. # Function to get data for each line
41. get_line_contents <- function(line) {
42. result <- safely(neuronbridge_line_contents)(line = line, threshold = 0)
43. if (!is.null(result$error)) {
44. message(paste("Error for line:", line, "- skipping"))
45. return(NULL)
46. }
47. return(result$result)
48. }
50. line_contents <- driver_list %>%
51. map(get_line_contents) %>%
52. compact() %>%
53. bind_rows()
54. ```
56. ```{r}
57. line_contents_sub <- line_contents[, c("bodyid", "searched", "name", "type")]
58. ```
60. b. filter neuronbridge output
61. I used no threshold, so that I don't miss hits (would need to determine threshold by line and it's arbitrary). I want to remove false positives by removing those cells that project into unwanted neuropil.
62. get list of names associated with each line and filter by the column 'name' the ones that are relevant
63. ```{r}
64. target_ROI <- line_contents_sub %>%
65. group_by(searched) %>%
66. summarise(values = paste(name, collapse = ', '))
67. ```
69. ```{r}
70. #filtering cell IDs by name column to get only cells projecting into the relevant compartments for each driver
71. filtered <- line_contents_sub %>%
72. filter(!(searched == "MB441B" & !(grepl("PAM", name) & grepl("y3", name)) & !(is.na(name)))) %>%
73. filter(!(searched == "MB042B" & !(grepl("PAM", name) & grepl("y3", name)) & !(is.na(name)))) %>%
74. filter(!(searched == "MB196B" & !(grepl("PAM", name)))) %>%
75. filter(!(searched == "MB315C" & !(grepl("PAM", name) & grepl("y5", name)) & !(is.na(name)))) %>%
76. filter(!(searched == "MB312B" & !(grepl("PAM", name) & grepl("y4", name)) & !(is.na(name)))) %>%
77. filter(!(searched == "MB607B" & !(grepl("KCg", name)))) %>%
78. filter(!(searched == "MB320C" & !is.na(name))) %>%
79. filter(!(searched == "MB025B" & !(grepl("PAM", name) & grepl("B'1", name)) & !(is.na(name)))) %>%
80. filter(!(searched == "MB418B" & !(grepl("KC", name)))) %>%
81. filter(!(searched == "MB371B" & !(grepl("KC", name)) & !(is.na(name)))) %>%
82. filter(!(searched == "MB188B" & !(grepl("PAM", name) & grepl("y3", name)) & !(is.na(name)))) %>%
83. filter(!(searched == "MB047B" & !(grepl("PAM", name) & grepl("B'2", name)) & !(is.na(name)))) %>%
84. filter(!(searched == "MB112C" & !(grepl("MBON", name)))) %>%
85. filter(!(searched == "MB298B" & !(grepl("MBON", name))))
86. ```
88. 2. get info on number of synapses per cell ID in ROI
89. ```{r}
90. #available ROIs
91. neuprint_ROIs(fromNeuronFields=T, superLevel = NULL)
92. ```
94. ```{r}
95. output_ROIs = c("g1(R)", "g2(R)", "g3(R)", "g4(R)", "g5(R)", "a'1(R)", "a'2(R)", "a'3(R)", "a1(R)", "a2(R)", "a3(R)", "b'1(R)", "b'2(R)", "b1(R)", "b2(R)")
96. ```
98. per ROI, what cell IDs have synapses there?
99. ```{r}
100. get_ROI_contents <- function(ROI) {
101. result <- neuprint_bodies_in_ROI(ROI)
102. # adding a column with ROI to the result
103. result$ROI <- ROI
104. return(result)
105. }
107. ROI_contents <- output_ROIs %>%
108. map(get_ROI_contents) %>%
109. compact() %>%
110. bind_rows()
111. ```
113. 3. merge the dfs: result is a table with each bodyid matched to a driver line
114. ```{r}
115. ids <- merge(filtered, ROI_contents, by="bodyid")
116. ```
118. ```{r}
119. ids <- ids %>%
120. rename(line = searched)
121. ```
123. add extra column ct by line
124. ```{r}
125. df_all <- ids %>%
126. left_join(drivers, by = "line")
127. ```
129. ```{r}
130. summary <- df_all %>%
131. group_by(ROI, line, individual_astro, ct, ncell) %>%
132. summarize(
133. roipre_sum = sum(roipre),
134. roipre_avg = mean(roipre),
135. roipre_med = median(roipre),
136. roipre_count = n(),
137. .groups = 'drop'
138. ) %>%
139. mutate(avg_n = roipre_avg * ncell)
140. ```
142. ```{r}
143. final <- summary %>%
144. filter(!(line == "MB441B" & !(ROI == "g3(R)"))) %>%
145. filter(!(line == "MB196B" & !(ROI == "g4(R)"))) %>%
146. filter(!(line == "MB315C" & !(ROI == "g5(R)"))) %>%
147. filter(!(line == "MB312B" & !(ROI == "g4(R)"))) %>%
148. filter(!(line == "MB607B" & !(ROI %in% c("g1(R)", "g2(R)", "g3(R)", "g4(R)", "g5(R)")))) %>%
149. filter(!(line == "MB025B" & !(ROI == "b'1(R)"))) %>%
150. filter(!(line == "MB418B" & !(ROI %in% c("b'1(R)", "b'2(R)", "a'1(R)", "a'2(R)", "a'3(R)")))) %>%
151. filter(!(line == "MB188B" & !(ROI == "g3(R)"))) %>%
152. filter(!(line == "MB047B")) %>%
153. filter(!(line == "MB112C" & !(ROI %in% c("b1(R)", "b2(R)", "a1(R)", "a2(R)", "a3(R)")))) %>%
154. filter(!(line == "MB298B" & !(ROI %in% c("g1(R)", "g2(R)")))) %>%
155. filter(!(line == "MB504B" & !(ROI %in% c("g1(R)", "g2(R)", "a'1(R)", "a2(R)", "a'2(R)", "a3(R)"))))
156. ```
158. ```{r}
159. final$ROI <- gsub("\\(R\\)", "", final$ROI)
160. final$ROI <- factor(final$ROI, levels = c("g1", "g2", "g3", "g4", "g5", "a'1", "a'2", "a'3", "b'1", "b'2", "a1", "a2", "a3", "b1", "b2"))
161. ```
163. ```{r}
164. #creating different shades of same colour per cell type category
165. setDT(final)
166. line_ct <- unique(final[, .(line, ct)])
168. kc_lines <- line_ct[ct == "KC", line]
169. kc_colors <- c("#d62728", "#ff9896")
171. mbon_lines <- line_ct[ct == "MBON", line]
172. mbon_colors <- c("#ff7f0e", "#ffbb78")
174. ppl1_lines <- line_ct[ct == "PPL1", line]
175. ppl1_colors <- "#2ca02c"
177. pam_lines <- line_ct[ct == "PAM", line]
178. pam_colors <- c("#440381", "#9467bd", "#6b6ecf", "#17becf", "#9edae5", "#1f77b4")
180. # Combine into one named vector
181. line_colors <- setNames(
182. c(kc_colors, mbon_colors, ppl1_colors, pam_colors),
183. c(kc_lines, mbon_lines, ppl1_lines, pam_lines)
184. )
186. line_colors
188. ```
190. ```{r}
191. ct_order <- c("KC", "MBON", "PPL1", "PAM")
192. line_ct[, ct := factor(ct, levels = ct_order)]
193. setorder(line_ct, ct) # Now sorted by ct, then line
194. line_levels_ordered <- line_ct$line # ordered by ct
196. # Apply to original dt
197. final[, line := factor(line, levels = line_levels_ordered)]
199. ```
202. ```{r}
203. driver_plot <- ggplot(final, aes(x=ROI, y=log2(avg_n), color=line)) +
204. geom_vline(xintercept = unique(final$ROI), color="grey", linetype="dotted", linewidth=0.3) +
205. geom_point(aes(size=roipre_count, shape=individual_astro), stroke=1.5) +
206. guides(size=guide_legend(override.aes = list(shape=c(3))),
207. col=guide_legend(override.aes = list(shape=c(3))))+
208. theme_classic() +
209. scale_color_manual(values = line_colors) +
210. scale_shape_manual(values = c(4, 3))+
211. scale_size_continuous(limits=c(1,43), breaks=c(1,10,20,30,40))+
212. theme(legend.box = "horizontal") +
213. ylab("log2(estimated synapse number)") +
214. xlab("Mushroom Body compartment")
216. ggsave(driver_plot, filename="/file/path/x.svg", height = 4, width = 7, dpi=300)
217. ```
220. Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.

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