receptoR

This is the R code for performing receptor gene expression analysis on transcriptomics data. The principle behind receptoR is that by pooling large numbers of existing transcriptomics datasets, we can identify molecular receptors expressed by specific cell types, and use this information to generate hypotheses about signaling modalities that influence cell behaviour. A live implementation of is available at https://wcm.ucalgary.ca/ungrinlab/receptoR, using publicly available microarray datasets from the GEO database. Using the code provided here, our analysis pipeline can be implemented on any type of data.

Installation

receptoR is a package for the R statisical computing language, and was built and tested on versions >= 3.5, and certain dependencies did not work during testing on 3.3. To install receptoR, type either of these commands into the console:

devtools::install_github("derektoms/receptoR")

...or...

source("https://install-github.me/derektoms/receptoR")

Packages imported by receptoR come from CRAN ('dplyr', 'dbplyr', 'tidyr', 'ggplot2', 'RColorBrewer', 'readr', 'stringr', 'shiny', 'shinythemes', 'shinyjs', 'DT', 'pool', 'writexl', 'BiocManager') and from Bioconductor ('affy', 'limma', 'annotate', 'pheatmap', 'mixOmics', 'cowplot'). Occasionally, there are problems installing Bioconductor packages on installation. If this is the case, install the following packages via BiocManager:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c('affy', 'limma', 'annotate', 'mixOmics')) ## these are usually the offending packages, but others should be installed as needed

Run the Shiny app

There's only one exported function in the package and it runs the Shiny app:

receptoR::launchApp()

This will open a browser window where you can interact with the receptoR application. Compiled read tables can be annotated in the "Upload Transcriptome Data" tab and ultimately processed to R data files (.RDA) that can be used by the app for analysis. This is performed on the "Load Expression Datasets" tab. Currently the package comes with an RNA-seq example dataset comparing expression between mouse retina and retinal pigment epithelium using control animal data from three publicly available GEO datasets (accession: GSE114945, GSE121858, and GSE131954).

Uploading read tables

To search and characterize publicly available microarray data, have a look at the online version of receptoR. For RNA-seq or unpublished data, you are able to upload a read count table and perform the same analysis. Processing raw transcriptome data is beyong the scope of this document, but ultimately the format is a matrix of samples (columns) by features (rows). A properly formatted count table should be free of missing values, and not be normalised (i.e. raw counts). Gene symbols are the required identifier for features and should be based on human (HGNC) and mouse (MGI) nomenclature. For the example dataset included with the package, the top of the raw count table looks like this (NB only the first nine columns are shown):

  GeneSymbol CFG2279 CFG2280 CFG2281 CFG2282 CFG2283 CFG2284 CFG2285 CFG2286
1  Zfp85-rs1      45      45      65      55      51      56      51      36
2       Scap       4       7      11       9       6       5       7       9
3     Zfp458     318     359     477     348     310     391     440     407
4     Fbxo41     138     142     172     170     133     148     205     204
5      Taf9b       0       0       0       1       0       0       0       0
6   BC051142       0       0       2       3       1       2       2       0

This table, saved as a CSV, can be uploaded to receptoR.

Loading expression datasets

Once you have processed datasets, these can be uploaded to the application for analysis. From the drop-down menu, select "upload processed data file (.rda)" and locate the saved file on your computer. When this has been loaded, receptor gene lists can be selected and individual gene expression analysed. From the "Gene-level Expression" and "Sample-level Expression" tabs, clustering and discriminate analysis can be performed on the entire list.

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License

This package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, version 3, as published by the Free Software Foundation.

This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details.

A copy of the GNU General Public License, version 3, is available at https://www.r-project.org/Licenses/GPL-3