Load, filter, normalise and perform log2-transformation on gene expression data from TCGA
Perform principal component analysis based on gene expression data, survival analysis by gene expression cutoff and pairwise differential gene expression analysis
Correlate gene expression of a given gene against PSI values of multiple alternative splicing events
Data loading:
Add step-wise instructions about loading of user-provided files
Filter GTEx junction quantification based on tissues of interest (all tissues are loaded by default)
Quantify splicing based on a list of genes (splicing events within all genes are quantified by default)
Generate TCGA sample metadata when loading TCGA junction quantification
Present data summary after loading the data
Data grouping:
Redesigned group creation and selection
Create groups based on genes and alternative splicing events
Assign a customisable colour per data group
Export or import patient and sample identifiers of data groups
Add new set operations when grouping (such as complement, subtraction and symmetric difference)
Suggest attributes of interest when creating groups
Allow to retrieve the universe of patient and sample identifiers by performing the complement group without any group selected
Statistically analyse group independence (useful to assess the overlap between a PCA cluster and groups derived from clinical and sample attributes, for instance)
Differential analysis:
Label points based on top differentially spliced events or genes, selected alternative splicing events and/or selected genes
Create AS event and gene groups based on filtered or selected AS events and genes in the tables
Dimensionality reduction techniques:
Subset data based on groups of AS events and genes before performing dimensionality reduction
Create data groups based on the partitioning clustering of PCA scores
Perform independent component analysis (ICA) on alternative splicing quantification and gene expression data
Survival analysis:
Add p-value plot to visually infer the significance of survival analyses based on multiple alternative splicing quantification cutoffs
Gene, transcript and protein information:
Information retrieval is now only dependent on a user-defined gene, instead of requiring alternative splicing quantification data to be loaded
Bug fixes and other improvements
Show progress bar when running in the command-line interface
Fix inconsistent browser history navigation
Updated the CLI vignette with information on analysing gene expression data and a quick reference for functions
Update minimum version required of shiny (1.0.3)
Avoid replacing selected groups when manipulating new ones
Differential splicing analysis:
Fix data not being rendered in the table when zooming in the plot after data transformation was applied
Return p-value of NA instead of 0 when the value of Fligner-Killeen’s Test for Homogeneity of Variance is infinite
Discard value transformations that may return invalid data for the values chosen for the X and Y axes
Fix point that remains highlighted in the plot after deselecting the only selected row of the table
Improve readability of plot’s tooltip
Improve survival curves based on the optimal alternative splicing quantification cutoff:
Include the survival curve previews in 3 new columns within the differential splicing analyses table, instead of below that table; those columns consist of the survival curves, the optimal PSI cutoff and the respective log-rank test’s p-value
Allow to use survival data when plotting and table sorting
Include the optimal PSI cutoff and the respective log-rank test’s p-value in exported tables
Fix link to survival analyses using the previously calculated PSI cutoff
Principal component analysis:
When clicking on a alternative splicing event in the loadings plot, the appropriate differential splicing analyses will now be automatically rendered with the respective options, as expected
Survival analysis:
Properly set the title of survival curves based on the selected splicing event’s quantification
Improve readability of Cox PH models
When performing survival analyses by alternative splicing cutoff, each patient is assigned the PSI value from the respective sample; for patients with more than one sample, the assigned sample is chosen based on the most frequent sample type across all patients (before, the first matched non-normal or non-control samples were used)
Multiple other bug fixes and visual improvements
psichomics 1.4.1 (14 Dec, 2017)
GTEx data loading:
Add input elements to allow GTEx gene expression loading in the graphical interface
Fix bug that did not allow to select tissues to load GTEx v7 data (graphical interface)
Fix splicing events not being quantified based on GTEx v7 junction reads
Gene expression normalisation:
Fix misleading gene expression (non-)normalisation by converting reads to counts per million (CPM) using edgeR::cpm() after normalisation using edgeR::calcNormFactor()
Alternative splicing quantification:
Updated support to properly parse new notation of alternative splicing annotation from Bioconductor (backwards compatible with older notation)
Raise error when no splicing events after quantification
Fix warning following the quantification of splicing events or its loading (incorrect parsing of gene information from splicing events)
Dimensionality reduction:
Use the number (instead of the percentage) of tolerated missing values per sample as the argument to impute data from the remaining samples for those values before performing dimensionality reduction; by default, missing values are tolerated for 10 samples
Update file description and README
Minor bug fixes and improvements
psichomics 1.4.2 (19 Dec, 2017)
Fix error when trying to load alternative splicing annotation (given updated hg19 and hg38 annotation that is now available for use with psichomics)
psichomics 1.4.3 (12 Jan, 2018)
Alternative splicing quantification:
Improve speed and memory usage when dealing with larger datasets
Improve quantification of alternative first and last exons: quantify alternative first and last exons based on all exon-exon junction reads that support each of the alternative exons
Print progress bar in R console
Principal component analysis:
Change tolerated missing values per event to 5% by default
Show/save table with the contribution of events (for alternative splicing quantification) or genes (for gene expression) to the selected principal components
Add extra information when hovering variables in loading plot
Allow to plot top 100 variables that most contribute to the selected principal components (faster rendering of and interaction with loading plots)
Differential analysis:
Allow to input a list of groups for the “group” argument of the functions diffAnalyses() and plotDistribution() (command-line interface)
Allow to input a non-numeric vector or a row of a matrix/data frame in the “data” argument of the function plotDistribution() (command-line interface)
Correlation between gene expression and alternative splicing:
Perform correlation between gene expression of multiple genes and quantification of multiple alternative splicing events
Bug fixes
Fix unnamed events when only one event for a event type is returned
Update vignettes to include a case study based on the aforementioned article
Gene expression pipeline:
Perform limma-trend by default
Alternative splicing quantification:
Quantification of alternative first and last exons: following more thorough testing, the new exon-centred method was considered to be less relevant to exploratory analysis (specially when compared with other types of events); as such, both methods are now available for quantification
Dimensionality reduction:
Change tolerated missing values per event to 5% by default for both PCA and ICA (in both visual and command-line interfaces)
PCA: In the table that shows the events that most contribute to the selected principal components, show the rank
Groups:
Automatically set sample type groups (i.e. normal, primary solid tumour, metastatic, etc.) for TCGA samples (visual interface only)
Differential analysis:
Use numeric fields instead of sliders to precisely filter data
Fix table for differential expression not being filtered based on highlighted genes
Filter splicing events or genes to use when performing exploratory differential analyses
Survival analysis:
Allow to stratify patients based on optimal gene expression cutoff
Select samples to be used for survival analysis
Bug fixes
Fix problems related with DT versions >= 0.3:
Groups displayed as having the same attributes as last created group
Table not being updated in differential analyses according to event filtering based on the volcano plot
psichomics 1.4.5 (4 Apr, 2018)
Mention psichomics manuscript throughout psichomics
Copy-edit graphical user interface and respective tutorial