Original article:

Nuno Saraiva-Agostinho and Nuno L. Barbosa-Morais (2019). psichomics: graphical application for alternative splicing quantification and analysis. 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), the Genotype-Tissue Expression (GTEx) project, Sequence Read Archive (SRA) and user-provided data.

psichomics interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included.

Differential splicing analysis in psichomics

Install and start running

To install the package from Bioconductor, type the following in RStudio or in an R console:



The following case studies and tutorials are available and were based on our original article:

Another tutorial was published as part of the Methods in Molecular Biology book series (the code for performing the analysis can be found here):

Nuno Saraiva-Agostinho and Nuno L. Barbosa-Morais (2020). Interactive Alternative Splicing Analysis of Human Stem Cells Using psichomics. In: Kidder B. (eds) Stem Cell Transcriptional Networks. Methods in Molecular Biology, vol 2117. Humana, New York, NY


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 recount2 package

Other SRA and user-provided data can be manually aligned and loaded. Please read Loading SRA and user-provided RNA-seq data.

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).

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 (hg19 and hg38 assemblies) annotation is provided and custom annotations can be used.
  2. Quantification of RNA-Seq reads aligning to splice junctions (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 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 gene 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 (nunoagostinho@medicina.ulisboa.pt)

Disease Transcriptomics Lab, Instituto de Medicina Molecular (Portugal)


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. Nature 456 (7221): 470–76.