psichomics case study: command-line interface (CLI)

Nuno Saraiva-Agostinho


psichomics is an interactive R package for integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA) (containing molecular data associated with 34 tumour types), the Genotype-Tissue Expression (GTEx) project (containing data for multiple normal human tissues), Sequence Read Archive and user-provided data. The data from GTEx, TCGA and select SRA projects include subject/sample-associated information and transcriptomic data, such as the quantification of RNA-Seq reads aligning to splice junctions (henceforth called junction quantification) and exons.

Installing and starting the program

Install psichomics by typing the following in an R console (the R environment is required):


After the installation, load psichomics by typing:


Available functions: quick reference

Data retrieval




Custom and/or local files

Gene expression pre-processing

PSI quantification

Custom alternative splicing annotation preparation

Data Grouping

Dimensionality reduction

Principal component analysis (PCA)

Independent component analysis (ICA)

Survival analysis

Differential analyses

Gene expression and alternative splicing correlation

Annotation retrieval

Exploration of clinically-relevant, differentially spliced events in breast cancer

The following case study was adapted from psichomics’ original article:

Nuno Saraiva-Agostinho and Nuno L. Barbosa-Morais (2019). psichomics: graphical application for alternative splicing quantification and analysis. Nucleic Acids Research.

Breast cancer is the cancer type with the highest incidence and mortality in women (Torre et al., 2015) and multiple studies have suggested that transcriptome-wide analyses of alternative splicing changes in breast tumours are able to uncover tumour-specific biomarkers (Tsai et al., 2015; Danan-Gotthold et al., 2015; Anczuków et al., 2015). Given the relevance of early detection of breast cancer to patient survival, we can use psichomics to identify novel tumour stage-I-specific molecular signatures based on differentially spliced events.

Downloading and loading TCGA data

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).

To estimate this value for each splicing event, both alternative splicing annotation and junction quantification are required. While alternative splicing annotation is provided by the package, junction quantification may be retrieved from TCGA, GTEx, SRA or user-provided files.

Data is downloaded from Firebrowse, a service that hosts proccessed data from TCGA, as required to run the downstream analyses. Before downloading data, check the following options:

Note there is also the option for Gene expression (normalised by RSEM). However, we recommend to load the raw gene expression data instead, followed by filtering and normalisation as demonstrated afterwards.

After deciding on the options to use, download and load breast cancer data as follows:

Data is only downloaded if the files are not present in the given folder. In other words, if the files were already downloaded, the function will just load the files, so it is possible to reuse the code above just to load the requested files.

Windows limitations: If you are using Windows, note that the downloaded files have huge names that may be over Windows Maximum Path Length. A workaround would be to manually rename the downloaded files to have shorter names, move all downloaded files to a single folder and load such folder. Read how in section Load unspecified local files at the end of this document.

Filtering and normalising gene expression

As this package does not focuses on gene expression analysis, we suggest to read the RNA-seq section of limma’s user guide. Nevertheless, we present the following commands to quickly filter and normalise gene expression:

Quantifying alternative splicing

After loading the clinical and alternative splicing junction quantification data from TCGA, quantify alternative splicing by clicking the green panel Alternative splicing quantification.

As previously mentioned, alternative splicing is quantified from the previously loaded junction quantification and an alternative splicing annotation file. To check current annotation files available:

##                        Human hg19/GRCh37 (2017-10-20) 
## "annotationHub_alternativeSplicingEvents.hg19_V2.rda" 
##                        Human hg19/GRCh37 (2016-10-11) 
##    "annotationHub_alternativeSplicingEvents.hg19.rda" 
##                               Human hg38 (2018-04-30) 
## "annotationHub_alternativeSplicingEvents.hg38_V2.rda"

Custom splicing annotation: Additional alternative splicing annotations can be prepared for psichomics by parsing the annotation from programs like VAST-TOOLS, MISO, SUPPA and rMATS. Note that SUPPA and rMATS are able to create their splicing annotation based on transcript annotation. For more information, read this tutorial.

To quantify alternative splicing, first select the junction quantification, alternative splicing annotation and alternative splicing event type(s) of interest:

##                                          Skipped exon 
##                                                  "SE" 
##                               Mutually exclusive exon 
##                                                 "MXE" 
##                            Alternative 5' splice site 
##                                                "A5SS" 
##                            Alternative 3' splice site 
##                                                "A3SS" 
##                                Alternative first exon 
##                                                 "AFE" 
##                                 Alternative last exon 
##                                                 "ALE" 
## Alternative first exon (exon-centred - less reliable) 
##                                            "AFE_exon" 
##  Alternative last exon (exon-centred - less reliable) 
##                                            "ALE_exon"

Afterwards, quantify alternative splicing using the previously defined parameters:

## [1] "SE_3_+_13661331_13663275_13663415_13667945_FBLN2"   
## [2] "SE_3_+_57908750_57911572_57911661_57913023_SLMAP"   
## [3] "ALE_3_+_57908750_57911572_57913023_SLMAP"           
## [4] "SE_3_-_37136283_37133029_37132958_37125297_LRRFIP2" 
## [5] "SE_12_-_56558432_56558152_56558087_56557549_SMARCC2"
## [6] "AFE_4_+_56755098_56750094_56756389_EXOC1"

Note that the event identifier (for instance, SE_1_-_2125078_2124414_2124284_2121220_C1orf86) is composed of:

Warning: all examples shown in this case study are performed using a small, yet representative subset of the available data. Therefore, values shown here may correspond to those when performing the whole analysis.

Data grouping

Let us create groups based on available samples types (i.e. Metastatic, Primary solid Tumor and Solid Tissue Normal) and tumour stages. As tumour stages are divided by sub-stages, we will merge sub-stages so as to have only tumour samples from stages I, II, III and IV (stage X samples are discarded as they are uncharacterised tumour samples).

Principal component analysis (PCA)

PCA is a technique to reduce data dimensionality by identifying variable combinations (called principal components) that explain the variance in the data (Ringnér, 2008). Use the following commands to perform PCA:

As PCA cannot be performed on data with missing values, missing values need to be either removed (thus discarding data from whole splicing events or genes) or impute them (i.e. attributing to missing values the median of the non-missing ones). Use the argument missingValues within function performPCA to select the number of missing values that are tolerable per event (i.e. if a splicing event or gene has less than N missing values, those missing values will be imputed; otherwise, the event is discarded from PCA).