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# psichomics <!-- badges: start --> [![GitHub Actions Build status][ghActionsIcon]][ghActions] [![codecov][codecovIcon]][codecov] <!-- badges: end --> > **Original article:** > > Nuno Saraiva-Agostinho and Nuno L. Barbosa-Morais (2019). [psichomics: graphical application for alternative splicing quantification and analysis][article]. *Nucleic Acids Research*. 47(2), e7. Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on [The Cancer Genome Atlas (TCGA)][TCGA], the [Genotype-Tissue Expression (GTEx) project][GTEx], [Sequence Read Archive (SRA)][SRA] and user-provided data. *psichomics* interactively performs: - Dimensionality reduction - Median- and variance-based differential splicing and gene expression analyses - Survival analysis - Correlation analysis - Grouping by clinical and molecular features (such as tumour stage or survival) - Genomic mapping and functional annotation of alternative splicing events and genes ![Differential splicing analysis in *psichomics*](man/figures/screenshot.png) ## Table of Contents * [Install and start running](#install-and-start-running) * [Bioconductor](#bioconductor) * [GitHub](#github) * [Docker](#docker) * [Tutorials](#tutorials) * [Workflow](#workflow) * [Data input](#data-input) * [Alternative splicing quantification](#alternative-splicing-quantification) * [Gene expression processing](#gene-expression-processing) * [Data grouping](#data-grouping) * [Data analyses](#data-analyses) * [Feedback and support](#feedback-and-support) * [References](#references) ## Install and start running ### Bioconductor To install the package from [Bioconductor][], type the following in [RStudio][] or in an R console: ```r install.packages("BiocManager") BiocManager::install("psichomics") library("psichomics") ``` 3. RStudio is now accessible via the web browser at https://localhost:8787 4. Enter RStudio with user `rstudio` and password `bioc` 5. Load psichomics using `library(psichomics)` 6. Start the visual interface of psichomics with `psichomics()` Start the visual interface of psichomics with `psichomics()` ### GitHub Install from GitHub (specify a branch or tag via the `ref` argument): ```r install.packages("remotes") remotes::install_github("nuno-agostinho/psichomics", ref="master") library("psichomics") ``` Start the visual interface of psichomics with `psichomics()` ### Docker The Docker images are based on [Bioconductor Docker][biocDocker] and contain psichomics and its dependencies. 1. Pull the latest Docker image: ``` docker pull ``` 2. Start RStudio Web from the Docker image: ``` docker run -e PASSWORD=bioc -p 8787:8787 ``` 3. Go to RStudio Web via the web browser at https://localhost:8787 4. Log in RStudio with user `rstudio` and password `bioc` 5. Load psichomics using `library(psichomics)` 6. Start the visual interface of psichomics with `psichomics()` ## Tutorials The following case studies and tutorials are available and were based on our [original article][article]: * [Visual interface][tutorial-gui] * [Command-line interface][tutorial-cli] * [Loading user-provided data][tutorial-custom-data] * [Preparing alternative splicing annotations][tutorial-prep-AS-annotation] Another tutorial was published as part of the Methods in Molecular Biology book series (the code for performing the analysis can be found [here][chapter-code]): > Nuno Saraiva-Agostinho and Nuno L. Barbosa-Morais (2020). **[Interactive Alternative Splicing Analysis of Human Stem Cells Using psichomics][chapter]**. In: Kidder B. (eds) Stem Cell Transcriptional Networks. *Methods in Molecular Biology*, vol 2117. Humana, New York, NY ## Workflow ### Data input Automatic retrieval and loading of pre-processed data from the following sources: * [TCGA][] data of given tumours, including subject- and sample-associated information, junction quantification and gene expression data * [GTEx][] data of given tissues, including subject- and sample-associated information, junction quantification and gene expression data * [SRA][] data from select SRA projects via the [recount][] package Other SRA, [VAST-TOOLS][] and user-provided data can also be manually loaded. Please read [Loading user-provided data][tutorial-custom-data] for more information. #### Alternative splicing quantification The quantification of each alternative splicing event is based on the proportion of junction reads that support the inclusion isoform, known as percent spliced-in or PSI [(Wang *et al.*, 2008)][Wang2008]. An estimate of this value is obtained based on the the proportion of reads supporting the inclusion of an exon over the reads supporting both the inclusion and exclusion of that exon. To measure this estimate, we require: 1. **Alternative splicing annotation**: human annotation is provided and custom annotations can be prepared for use in psichomics. 2. Quantification of RNA-Seq reads aligning to exon-exon splice junctions (**exon-exon junction quantification**), either user-provided or retrieved from [TCGA][], [GTEx][] and [SRA][]. #### Gene expression processing Gene expression can be normalised, filtered and log2-transformed in-app or provided by the user. ### Data grouping Molecular and clinical sample-associated attributes allow to establish groups that can be explored in data analyses. For instance, [TCGA][] data can be analysed based on smoking history, gender and race, among other attributes. Groups can also be manipulated (e.g. merged, intersected, etc.), allowing for complex attribute combinations. Groups can also be saved and loaded between different sessions. ### Data Analyses * **Dimensionality reduction** via principal and independent component analysis (PCA and ICA) on alternative splicing quantification and gene expression. * **Differential splicing and gene expression analysis** based on variance and median parametric and non-parametric statistical tests. * **Correlation between gene expression and splicing quantification**, useful to correlate the expression of a given event with the expression of RNA-binding proteins, for instance. * **Survival analysis** via Kaplan-Meier curves and Cox models based on sample-associated features. Additionally, we can study the impact of a splicing event (based on its quantification) or a gene (based on its expression) on patient survivability. * **Gene, transcript and protein annotation**, including relevant research articles. ## Feedback and support Please send any feedback and questions on psichomics to: > Nuno Saraiva-Agostinho ([][email]) > > [Disease Transcriptomics Lab, Instituto de Medicina Molecular (Portugal)][NMorais] ## References Wang, E. T., R. Sandberg, S. Luo, I. Khrebtukova, L. Zhang, C. Mayr, S. F. Kingsmore, G. P. Schroth, and C. B. Burge. 2008. [*Alternative isoform regulation in human tissue transcriptomes.*][Wang2008] Nature 456 (7221): 470–76. [email]: [TCGA]: [Bioconductor]: [R]: [RStudio]: [NMorais]: [conduct]: [Wang2008]: [ghActionsIcon]: [ghActions]: [codecovIcon]: [codecov]: [GTEx]: [article]: [chapter]: [chapter-code]: [SRA]: [VAST-TOOLS]: [tutorial-gui]: [tutorial-cli]: [tutorial-custom-data]: [tutorial-prep-AS-annotation]: [recount]: [biocDocker]: