studies/Ricinus-communis-seeds/README.md

+
+## Ricinus-communis-seeds
+
+![fpls-14-1182105-g001.jpg](resources/fpls-14-1182105-g001.jpg)
+
+**Figure 1**
+
+Endosperm morphology in seeds and germinating seedling of R. communis. Photographs were taken from the whole plant (upper panel) and two cotyledons embedded in the endosperm (lower panel) from mature seeds (dry), 24h-imbibed seeds (imb) and from 1- to 7-day old dark-grown castor bean seedlings (1-7d). Scale bar = 1 cm.
\ No newline at end of file

studies/Ricinus-communis-seeds/protocols/Plant-growth-conditions.md

+# 2.1 Plant growth conditions
+
+Dry seeds of *Ricinus communis* var. *zanzibariensis* were surface- sterilized in a solution of 0.1% (w/v) 8-quinolinol in water for ten minutes and soaked in running tap water over night for seed imbibition. The imbibed seeds were placed on moist vermiculite and incubated at 30°C in the dark (Beevers and Breidenbach, 1974).
+
+
+- Beevers, H., and Breidenbach, R. W. (1974). “Glyoxysomes,” in Methods in enzymology, Vol 16 Biomembranes, Part A. Eds. S. Fleciher and L. packer (New York London: Academic Press), 565–571.
\ No newline at end of file

workflows/2022-04-13_skim_results.Rmd

----
-title: "Untitled"
-author: "Dominik"
-date: "4/13/2022"
-output:
-  pdf_document: default
-  html_document: default
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-
-library(knitr)
-library(tidyverse)
-library(openxlsx)
-
-```
-
-
-```{r}
-getwd()
-
-dir("../assays/2022-04-13_proteome_discoverer/dataset/")
-
-```
-
-## Count peptides in fasta
-
-```{bash}
-grep ">" ../assays/2022-04-13_proteome_discoverer/dataset/AllProteins.fasta | wc -l
-
-head ../assays/2022-04-13_proteome_discoverer/dataset/AllProteins.fasta 
-
-grep "OS=Ricinus communis" ../assays/2022-04-13_proteome_discoverer/dataset/AllProteins.fasta | wc -l
-```
-
-## skim excel files
-
-### quantification.xlsx
-
-```{r}
-quantification <- readxl::read_xlsx("../assays/2022-04-13_proteome_discoverer/dataset/quantification.xlsx")
-dim(quantification)
-colnames(quantification)
-length(unique(quantification$Accession))
-
-```
-
-### quantification_with_peptides.xlsx
-
-```{r}
-quantification_peptides <- readxl::read_xlsx("../assays/2022-04-13_proteome_discoverer/dataset/quantification_with_peptides.xlsx")
-dim(quantification_peptides)
-colnames(quantification_peptides)
-length(unique(quantification_peptides$Accession))
-
-## This is a whole new level of untidy. A table nested in a table. I hate it. Thank you, Thermo Fisher Scientific. 
-
-### 1. keep only high confidence rows
-
-quant_proteins <- filter(quantification_peptides, `Protein FDR Confidence: Combined` == "High")
-
-### 2. remove empty columns
-quant_proteins <- quant_proteins[, colSums(is.na(quant_proteins)) != nrow(quant_proteins)]
-
-### 3. dummy check, that content is the same... 
-dim(quant_proteins) == dim(quantification)
-sum(as.numeric(unlist(quantification[,6])) == as.numeric(unlist(quant_proteins[,6])))
-
-
-## pull out peptide data 
-
-peptides <- filter(quantification_peptides, is.na(`Protein FDR Confidence: Combined`))
-nrow(quantification_peptides) - nrow(peptides)
-
-### 2. remove empty columns
-peptides <- peptides[, colSums(is.na(peptides)) != nrow(peptides)]
-
-### 3. strip between-data headlines
-colnames(peptides) = as.character(peptides[1, ])
-peptides <- filter(peptides, Checked != "Checked")
-
-peptides <- type_convert(peptides)
-
-
-```
-
-
-## create an isa.run.xlsx like data dictionary 
-
-```{r}
-
-sum_tmp <- data.frame(summary(quantification)) %>% 
-  pivot_wider(names_from = Var1, values_from = c(Var1, Freq), 
-              values_fn = function(x){paste(na.exclude(x), collapse = "|")})
-
-quantification_summary <-  cbind.data.frame(sum_tmp[,c(1,3)],t(summarise_all(quantification, class)))
-colnames(quantification_summary) <- c("Identifier", "ObjectSummary", "ObjectType")
-quantification_summary$TargetFile <- "quantification.xlsx"
-
-
-sum_tmp <- data.frame(summary(peptides)) %>% 
-  pivot_wider(names_from = Var1, values_from = c(Var1, Freq), 
-              values_fn = function(x){paste(na.exclude(x), collapse = "|")})
-
-peptides_summary <-  cbind.data.frame(sum_tmp[,c(1,3)],t(summarise_all(peptides, class)))
-
-colnames(peptides_summary) <- c("Identifier", "ObjectSummary", "ObjectType")
-peptides_summary$TargetFile <- "quantification_with_peptides.xlsx"
-
-
-
-isa_run <- rbind.data.frame(quantification_summary, peptides_summary)[, c(4, 1, 3, 2)]
-row.names(isa_run) <- NULL
-isa_run$Comment <- ""
-isa_run$Definition <- ""
-
-
-write.xlsx(isa_run, file =  "../assays/2022-04-13_proteome_discoverer/isa.run.xlsx", overwrite = T, asTable = T)
-
-
-```
-
-
-
-
-
-
-```{r}
-
-
-# extract abundances per accession only
-
-abundances_grouped <- quantification[, c("Accession", grep("Abundances (Grouped)", colnames(quantification), fixed = T, value = T))]
-
-# pivot and split column
-
-abundances_grouped2 <-  
-  abundances_grouped %>%
-  pivot_longer(!Accession, names_to = c("organelle", "compartment"), values_to = "abundance", names_sep = ', ')
-
-# remove 
-
-abundances_grouped2$organelle <- gsub("Abundances (Grouped): ", "", abundances_grouped2$organelle, fixed = T)
-
-# transform columns to factor 
-
-abundances_grouped2$organelle <- as.factor(abundances_grouped2$organelle)
-abundances_grouped2$compartment <- as.factor(abundances_grouped2$compartment)
-
-# pick random accessions
-selected_accs <- sample(unique(abundances_grouped2$Accession), 4)
-
-# filter plot subset
-plotsub <- filter(abundances_grouped2, Accession %in% selected_accs)
-
-
-ggplot(plotsub, aes(x = organelle, y = abundance, fill = compartment)) +
-  geom_col(position = position_dodge(width = 0.7), width = 0.7) + 
-  facet_wrap(~Accession, scales = 'free') + 
-  scale_fill_brewer(palette = "Dark2")
-  
-```
-
-
-### Calculate and draw a PCA to get an overview of the dataset
-
-```{r, fig.width = 3, fig.height = 3, fig.align = "center", eval=T}
-
-pca_data <- as.data.frame(pivot_wider(abundances_grouped2,
-                                      names_from = Accession,
-                                      values_from =  abundance,
-                                      id_cols = c("organelle", "compartment")))
-
-pca_data <- unite(pca_data,  organelle, compartment, col = 'merger', sep = '_')
-rownames(pca_data) <- pca_data$merger
-pca_data <- pca_data[, -1]
-
-
-####### double-check
-pca_data[is.na(pca_data)] <- 0
-####### double-check
-
-pca_data <- pca_data[, apply(pca_data, 2, function(x) {sum(x) != 0})]
-
-pca <- prcomp(pca_data, scale = T)
-pcaPlotData <- as.data.frame(pca$x)
-pcaPlotData$merger <- rownames(pcaPlotData)
-pcaPlotData <- separate(data = pcaPlotData, col = merger, sep = '_',
-                        into = c("organelle", "compartment"))
-
-ggplot(pcaPlotData, aes_string(color = 'organelle', shape = 'compartment', x = 'PC1', y = 'PC2')) +
-  geom_point(size = 3,  stroke = 1.5) +
-  coord_equal() +
-  # theme_dominik +
-  scale_color_brewer(palette = 'Dark2')
-
-```
-
-
-
-
-
-```{r}
-
-
-# extract abundances per accession only
-
-abundances <- quantification[, c("Accession", grep("Abundances (Normalized)", colnames(quantification), fixed = T, value = T))]
-
-# pivot and split column
-
-abundances2 <-  
-  abundances %>%
-  pivot_longer(!Accession, names_to = c("sample", "organelle", "compartment"), values_to = "abundance", names_sep = ', ')
-
-# remove 
-
-abundances2$sample <- gsub("Abundances (Normalized): ", "", abundances2$sample, fixed = T)
-abundances2$sample <- gsub(": Sample", "", abundances2$sample, fixed = T)
-
-# transform columns to factor 
-
-abundances2$sample <- as.factor(abundances2$sample)
-abundances2$organelle <- as.factor(abundances2$organelle)
-abundances2$compartment <- as.factor(abundances2$compartment)
-
-# pick random accessions
-selected_accs <- sample(unique(abundances2$Accession), 4)
-
-# filter plot subset
-plotsub <- filter(abundances2, Accession %in% selected_accs)
-
-
-ggplot(plotsub, aes(x = organelle, y = abundance, fill = compartment)) +
-  geom_point(position = position_dodge(width = 0.7), width = 0.7) +
-  facet_wrap(~Accession, scales = 'free') + 
-  scale_fill_brewer(palette = "Dark2")
-  
-```
-
-
-
-### Calculate and draw a PCA to get an overview of the dataset
-
-```{r, fig.width = 3, fig.height = 3, fig.align = "center", eval=T}
-
-pca_data <- as.data.frame(pivot_wider(abundances2,
-                                      names_from = Accession,
-                                      values_from =  abundance,
-                                      id_cols = c("sample", "organelle", "compartment")))
-
-pca_data <- unite(pca_data,  sample, organelle, compartment, col = 'merger', sep = '_')
-rownames(pca_data) <- pca_data$merger
-pca_data <- pca_data[, -1]
-
-
-####### double-check
-pca_data[is.na(pca_data)] <- 0
-####### double-check
-
-pca_data <- pca_data[, apply(pca_data, 2, function(x) {sum(x) != 0})]
-
-pca <- prcomp(pca_data, scale = T)
-pcaPlotData <- as.data.frame(pca$x)
-pcaPlotData$merger <- rownames(pcaPlotData)
-pcaPlotData <- separate(data = pcaPlotData, col = merger, sep = '_',
-                        into = c("sample", "organelle", "compartment"))
-
-pca_plot_individuals <- ggplot(pcaPlotData, aes_string(color = 'organelle', shape = 'compartment', x = 'PC1', y = 'PC2')) +
-  geom_point(size = 2,  stroke = 1.5) +
-  coord_equal() +
-  # theme_dominik +
-  scale_color_brewer(palette = 'Dark2')
-
-png(file = "pca_plot_individuals.png", res = 300, width = 2000, height = 2000)
-pca_plot_individuals
-dev.off()
-
-png(file = "pca_plot_individuals_labelled.png", res = 300, width = 2000, height = 2000)
-pca_plot_individuals + geom_text(aes(label = sample), nudge_x = 5)
-dev.off()
-
-
-```
-
-```{r, out.width = "100%", eval= T, echo=F}
-include_graphics('pca_plot_individuals.png')
-include_graphics('pca_plot_individuals_labelled.png')
-```
-
-
-
-
-### exlude ER
-
-```{r, fig.width = 3, fig.height = 3, fig.align = "center", eval=T}
-
-pca_data <- as.data.frame(pivot_wider(filter(abundances2, organelle != "endoplasmatic reticulum"),
-                                      names_from = Accession,
-                                      values_from =  abundance,
-                                      id_cols = c("sample", "organelle", "compartment")))
-
-
-pca_data <- unite(pca_data,  sample, organelle, compartment, col = 'merger', sep = '_')
-rownames(pca_data) <- pca_data$merger
-pca_data <- pca_data[, -1]
-
-
-####### double-check
-pca_data[is.na(pca_data)] <- 0
-####### double-check
-
-pca_data <- pca_data[, apply(pca_data, 2, function(x) {sum(x) != 0})]
-
-pca <- prcomp(pca_data, scale = T)
-pcaPlotData <- as.data.frame(pca$x)
-pcaPlotData$merger <- rownames(pcaPlotData)
-pcaPlotData <- separate(data = pcaPlotData, col = merger, sep = '_',
-                        into = c("sample", "organelle", "compartment"))
-
-pca_plot_excl_ER <- ggplot(pcaPlotData, aes_string(color = 'organelle', shape = 'compartment', x = 'PC1', y = 'PC2')) +
-  geom_point(size = 2,  stroke = 1.5) +
-  coord_equal() +
-  # theme_dominik +
-  scale_color_brewer(palette = 'Dark2')
-
-png(file = "pca_plot_excl_ER.png", res = 300, width = 2000, height = 2000)
-pca_plot_excl_ER
-dev.off()
-
-```
-
-```{r, out.width = "100%", eval= T, echo=F}
-include_graphics('pca_plot_excl_ER.png')
-```
-
-
-
-
-
-