Up and running with pcaExplorer
knitr::opts_chunk$set(crop = NULL)
First things first: install pcaExplorer and load it into your R session. You should receive a message notification if this is completed without errors.
This dataset consists of the gene-level expression measurements (as raw read counts) for an experiment where four different human airway smooth muscle cell lines are either treated with dexamethasone or left untreated.
To start the exploration, you just need the following lines:
The easiest way to explore the airway dataset is by clicking on the dedicated button in the Data Upload panel. This action will:
ddsobject, normalize the expression values (using the robust method proposed by Anders and Huber in the original DESeq manuscript), and compute the variance stabilizing transformed expression values (stored in the
If you want to load your expression data, please refer to the User Guide, which contains detailed information on the formats your data have to respect.
Once the preprocessing of the input is done, you should get a notification in the lower right corner that you’re all set. The whole preprocessing should take around 5-6 seconds (tested on a MacBook Pro, with i7 and 16 Gb RAM). You can check how each component looks like by clicking on its respective button, once they appeared in the lower half of the panel.
You can proceed to explore the expression values of your dataset in the Counts Table tab. You can change the data type you are displaying between raw counts, normalized, or transformed, and plot their values in a scatterplot matrix to explore their sample-to-sample correlations. To try this, select for example “Normalized counts”, change the correlation coefficient to “spearman”, and click on the
Run action button. The correlation values will also be displayed as a heatmap.
Additional features, both for samples and for features, are displayed in the Data overview panel. A closer look at the metadata of the
airway set highlights how each combination of cell type (
cell) and dexamethasone treatment (
dex) is represented by a single sequencing experiment. The 8 samples in the demo dataset are themselves a subsample of the full GEO record, namely the ones non treated with albuterol (
The relationship among samples can be seen in the sample-to-sample heatmap. For example, by selecting the Manhattan distance metric, it is evident how the samples cluster by dex treatment, yet they show a dendrogram structure that recalls the 4 different cell types used. The total sum of counts per sample is displayed as a bar plot.
Patterns can become clearer after selecting, in the App settings on the left, an experimental factor to group and color by: try selecting
dex, for example. If more than one covariate is selected, the interaction between these will be taken as a grouping factor. To remove one, simply click on it to highlight and press the del or backspace key to delete it. Try doing so by also clicking on
cell, and then removing
Basic summary information is also displayed for the genes. In the count matrix provided, one can check how many genes were detected, by selecting a “Threshold on the row sums of the counts” or on the row means of the normalized counts (more stringent). For example, selecting 5 in both cases, only 24345 genes have a total number of counts, summed by row, and 17745 genes have more than 5 counts (normalized) on average.
The Samples View and the Genes View are the tabs where most results coming from Principal Component Analysis, either performed on the samples or on the genes, can be explored in depth. Assuming you selected
cell in the “Group/color by” option on the left, the Samples PCA plot should clearly display how the cell type explain a considerable portion of the variability in the dataset (corresponding to the second PC). To check that
dex treatment is the main source of variability, select that instead of
The scree plot on the right shows how many components should be retained for a satisfactory reduced dimension view of the original set, with their eigenvalues from largest to smallest. To explore the PCs other than the first and the second one, you can just select them in the x-axis PC and y-axis PC widgets in the left sidebar.
If you brush (left-click and hold) on the PCA plot, you can display a zoomed version of it in the frame below. If you suspect some samples might be outliers (this is not the case in the
airway set, still), you can select them in the dedicated plot, and give a first check on how the remainder of the samples would look like. On the right side, you can quickly check which genes show the top and bottom loadings, split by principal component. First, change the value in the input widget to 20; then, select one of each list and try to check them in the Gene Finder tab; try for example with DUSP1, PER1, and DDX3Y.
While DUSP1 and PER1 clearly show a change in expression upon dexamethasone treatment (and indeed where reported among the well known glucocorticoid-responsive genes in the original publication of Himes et al., 2014), DDX3Y displays variability at the cell type level (select
cell in the Group/color by widget): this gene is almost undetected in N061011 cells, and this high variance is what determines its high loading on the second principal component.
You can see the single expression values in a table as well, and this information can be downloaded with a simple click.
Back to the Samples View, you can experiment with the number of top variable genes to see how the results of PCA are in this case robust to a wide range of this value - this might not be the case with other datasets, and the simplicity of interacting with these parameters makes it easy to iterate in the exploration steps.
Proceeding to the Genes View, you can see the dual of the Samples PCA: now the samples are displayed as arrows in the genes biplot, which can show which genes display a similar behaviour. You can capture this with a simple brushing action on the plot, and notice how their profiles throughout all samples are shown in the Profile explorer below; moreover, a static and an interactive heatmap, together with a table containing the underlying data, are generated in the rows below.
Since we compute the gene annotation table as well, it’s nice to read the gene symbols in the zoomed window (instead of the ENSEMBL ids). By clicking close enough to any of these genes, the expression values are plotted, in a similar fashion as in the Gene Finder.
The tab PCA2GO helps you understanding which are the biological common themes (default: the Gene Ontology Biological Process terms) in the genes showing up in the top and in the bottom loadings for each principal component. Since we launched the
pcaExplorer app without additional parameters, this information is not available, but can be computed live (this might take a while).
Still, a previous call to
pca2go is recommended, as it relies on the algorithm of the topGO package: it will require some additional computing time, but it is likely to deliver more precise terms (i.e. in turn more relevant from the point of view of their biological relevance). To do so, you should exit the live session, compute this object, and provide it in the call to
pcaExplorer (see more how to do so in the main user guide).
A typical session with
pcaExplorer includes one or more iterations on each of these tabs. Once you are finished, you might want to store the results of your analysis in different formats.
pcaExplorer you can do all of the following:
.RDatafile, as if it was a workspace (clicking on the cog icon in the right side of the task menu)
pcaExplorerand save” saves the state but in a specific environment of your R session, which you can later access by its name, which normally could look like
pcaExplorerState_YYYYMMDD_HHMMSS(also accessible from the cog)
pcaExplorercomes with a template analysis, that picks the latest status of the app during your session, and combines these reactive values together in a R Markdown document, which you can first preview live in the app, and then download as standalone HTML file - to store or share. This document stiches together narrative text, code, and output objects, and constitutes a compendium where all actions are recorded. If you are familiar with R, you can edit that live, with support for autocompletion, in the “Edit report” tab.
The functionality to display the report preview is based on
knit2html, and some elements such as
DataTable objects might not render correctly. To render them correctly, please install the PhantomJS executable before launching the app. This can be done by using the webshot package and calling
webshot::install_phantomjs() - HTML widgets will be rendered automatically as screenshots. Keep in mind that the fully rendered report (the one you can obtain with the “Generate & Save” button) is not affected by this, since it uses
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