Tutorial

Eric Bonnet edited this page Dec 7, 2015 · 35 revisions

Introduction

This is a step-by-step tutorial on how to use RNaviCell functions.

NaviCell is a technology using the Google maps engine (API) for the on-line exploration and analysis of large-scale biological molecular maps.

NaviCell Web Service is an extension of NaviCell for mapping and visualizing various types of high-throughput biological data types, such as gene expression data, copy-number data or mutation data. Users can choose between different types of graphical representations to visualize their data on the molecular maps.

The NaviCell Web Service can also be used in server mode, to automate the different steps of the visualization process. The server can receive REST commands (formatted http POST requests), to load data, choose a datatable and samples, set a given graphical representation, etc. A detailed REST API tutorial and reference manual is available.

To facilitate the work for developpers and users, we have also created a set of different bindings for NaviCell Web Service in different popular languages. A full implementation for Python is available, the R binding is presented here, a binding for Java, JNaviCell is available on GitHub.

Installation

The package can be installed directly from GitHub with the command install_github from the devtools R package:

library(devtools)
install_github('sysbio-curie/RNaviCell')

Voilà! Now you're ready to use RNaviCell.

Tutorial

In this tutorial, we will use RNaviCell to send data to the NaviCell server and visualize it on a map of the Cell Cycle using the map staining technique.

The data file and the R script reproduced here are located in the examples directory of the package.

We will assume that the RNaviCell directory is installed on your machine, with the mandatory R packages RJSONIO and RCurl also installed. You should be positionned in the directory examples.

After launching R in this directory, let's load the RNaviCell package (RJSONIO and RCurl will be loaded automatically):

library(RNaviCell)

Now let's create a NaviCell object:

navicell <- NaviCell()

We will load an expression data matrix into R, as a R matrix object. This matrix has 68 genes (as rows) and one column with expression (continuous) values. The gene expression was measured on a prostate cancer cell line named DU145.

mat <- navicell$readDatatable('DU145_data.txt')

Let's create a session with the NaviCell server. This command will automatically launch the default browser on your machine and point it to the Cell Cycle map on the NaviCell web site. Note that supported browsers are Chrome, Firefox and Safari.

navicell$launchBrowser()

The R session and the browser are now linked. Let's send the expression data matrix we just loaded to the map:

navicell$importDatatable("mRNA expression data", "DU145", mat)

Now we will change the default graphical parameters to use values that are more appropriate to visualize our data. This step is optional but usually very useful to facilitate the interpretation of the data on the map. Basically we will instruct the server to create a gradient of red (high expression values) to green (low expression value) with white (middle expression values):

navicell$continuousConfigSwitchSampleTab("DU145", "color")
navicell$continuousConfigSetStepCount("sample", 'color', 'DU145', 2)
navicell$continuousConfigSetColorAt("DU145", "sample", 1, 'FFFFFF')
navicell$continuousConfigSetValueAt("DU145", "color", "sample", 0, -1)
navicell$continuousConfigSetValueAt("DU145", "color", "sample", 2, 1)
navicell$continuousConfigApply("DU145", "color")

Let's visualize the data using the map staining technique. Territories around all the elements (proteins, genes, etc.) of the map are now colored according to the expression values associated to each element. With this visualization, it's quick and easy to see which part of the map, i.e. which biological pathways, are associated to high, middle or low expression values.

navicell$mapStainingEditorSelectDatatable('DU145')
navicell$mapStainingEditorSelectSample('data')
navicell$mapStainingEditorApply()

The resulting map looks like this:

Note that all the commands described in this tutorial are written in the script script.R. You can execute this script directly with the command:

Rscript script.R
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