downsize vignette

vignettes/DEP.Rmd

@@ -26,7 +26,7 @@ It requires tabular input (e.g. txt files) as generated by quantitative analysis
 Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins.
 It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. 
 Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. 
-For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report (see the chapter on [Wrapper functions](#wrapper-functions-for-the-entire-workflow)). 
+For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report (see the chapter on [Workflow functions](#workflow-functions-for-the-entire-analysis)). 
 Even easier to use are the interactive Shiny apps that are provided by the package (see the chapter on [Shiny apps](#interactive-analysis-using-the-dep-shiny-apps)).
 
 # Installation
@@ -296,16 +296,16 @@ These visualizations assist in the determination of the optimal cutoffs to be us
 The PCA plot can be used to get a high-level overview of the data.
 This can be very useful to observe batch effects, such as clear differences between replicates.
 
-``` {r pca, fig.height = 4, fig.width = 5}
+``` {r pca, fig.height = 3, fig.width = 4}
 # Plot the first and second principal components
-plot_pca(dep, x = 1, y = 2, n = 500)
+plot_pca(dep, x = 1, y = 2, n = 500, point_size = 4)
 ```
 
 ### Correlation matrix
 
 A correlation matrix can be plotted as a heatmap, to visualize the Pearson correlations between the different samples.
 
-``` {r corr, fig.height = 4, fig.width = 6}
+``` {r corr, fig.height = 3, fig.width = 5}
 # Plot the Pearson correlation matrix
 plot_cor(dep, significant = TRUE, lower = 0, upper = 1, pal = "Reds")
 ```
@@ -316,10 +316,10 @@ The heatmap representation gives an overview of all significant proteins (rows)
 This allows to see general trends, for example if one sample or replicate is really different compared to the others.
 Additionally, the clustering of samples (columns) can indicate closer related samples and clustering of proteins (rows) indicates similarly behaving proteins. The proteins are clustered by k-means clustering and the number of clusters can be defined by argument _k_.
 
-``` {r heatmap, fig.height = 6, fig.width = 4}
+``` {r heatmap, fig.height = 5, fig.width = 3}
 # Plot a heatmap of all significant proteins with the data centered per protein
 plot_heatmap(dep, type = "centered", kmeans = TRUE, 
-             k = 6, col_limit = 4, row_font_size = 2)
+             k = 6, col_limit = 4, show_row_names = FALSE)
 ```
 
 The heatmap shows a clustering of replicates and indicates that 4Ubi and 6Ubi enrich a similar repertoire of proteins. The k-means clustering of proteins (general clusters of rows) nicely separates protein classes with different binding behaviors.  
@@ -328,10 +328,10 @@ The heatmap shows a clustering of replicates and indicates that 4Ubi and 6Ubi en
 
 Alternatively, a heatmap can be plotted using the contrasts, ie. the direct sample comparisons, as columns. Here, this emphasises the enrichment of ubiquitin interactors compared to the control sample.
 
-``` {r heatmap2, fig.height = 6, fig.width = 4}
+``` {r heatmap2, fig.height = 5, fig.width = 3}
 # Plot a heatmap of all significant proteins (rows) and the tested contrasts (columns)
 plot_heatmap(dep, type = "contrast", kmeans = TRUE, 
-             k = 6, col_limit = 10, row_font_size = 2)
+             k = 6, col_limit = 10, show_row_names = FALSE)
 ```
 
 ### Volcano plots of specific contrasts
@@ -413,15 +413,15 @@ save(data_se, data_norm, data_imp, data_diff, dep, file = "data.RData")
 load("data.RData")
 ```
 
-# Wrapper functions for the entire workflow
+# Workflow functions for the entire analysis
 
-The package contains wrapper functions that entail the complete analysis workflow and generate a report. 
+The package contains workflow functions that entail the complete analysis and generate a report. 
 
 ## LFQ-based DEP analysis
 
-Differential enrichment analysis of label-free proteomics data can be performed using the _LFQ_ wrapper function.
+Differential enrichment analysis of label-free proteomics data can be performed using the _LFQ_ workflow function.
 
-``` {r LFQ}
+``` {r LFQ, message = FALSE}
 # The data is provided with the package 
 data <- UbiLength
 experimental_design <- UbiLength_ExpDesign
@@ -434,28 +434,19 @@ data_results <- LFQ(data, experimental_design, fun = "MinProb",
 This wrapper produces a list of objects, which can be used to create a report and/or for further analysis.
 The _report_ function produces two reports (pdf and html), a results table and a RData object, which are saved in a generated "Report" folder.
 
-``` {r report2, results = "hide"}
+``` {r report2, eval = FALSE}
 # Make a markdown report and save the results
 report(data_results)
 ```
 
-The results generated by the LFQ function contain, among others, the _results_ object (data.frame object) and the _sign_ object (SummarizedExperiment object).
+The results generated by the LFQ function contain, among others, the _results_ object (data.frame object) and the _dep_ object (SummarizedExperiment object).
 
 ``` {r report1}
 # See all objects saved within the results object
 names(data_results)
 ```
 
-The _results_ object contains the essential results and the _sign_ object contains the full SummarizedExperiment object which can be used for further visualization.
-
-``` {r LFQ_results}
-# Colnames of the results object
-colnames(data_results$results)
-
-# the dep object
-data_results$dep
-```
-
+The _results_ object contains the essential results and the _dep_ object contains the full SummarizedExperiment object.
 The results table can be explored by selecting the _$results_ object
 
 ``` {r LFQ_results3}
@@ -466,7 +457,7 @@ results_table <- data_results$results
 results_table %>% filter(significant) %>% nrow()
 ```
 
-The full data (_sign_ object) can be used for the plotting functions as described in the chapter ["Visualization of the results"](#visualization-of-the-results).
+The full data (_dep_ object) can be used for the plotting functions as described in the chapter ["Visualization of the results"](#visualization-of-the-results), for example a volcano plot.
 
 ``` {r LFQ_results4, fig.height = 5, fig.width = 5}
 # Extract the sign object
@@ -476,11 +467,6 @@ full_data <- data_results$dep
 plot_volcano(full_data, "Ubi4_vs_Ctrl", label_size = 2, add_names = TRUE)
 ```
 
-``` {r LFQ_results5, fig.height = 6, fig.width = 3}
-# Use the full data to generate a heatmap
-plot_heatmap(full_data, "contrast", row_font_size = 2)
-```
-
 ## TMT-based DEP analysis
 
 Differential enrichment analysis of Tandem-Mass-Tag labeled proteomics data is also supported.