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New features
Predict targeting drugs (predictTargetingDrug()):
Based on expression and drug sensitivity associations derived from NCI60, CTRP and GDSC data (see loadExpressionDrugSensitivityAssociation())
Compare user-provided differential expression profile with gene expression and drug sensitivity associations to predict targeting drugs and their targeted genes
Compounds are ranked based on their relative targeting potential
Plot candidate targeting drugs against ranked compound perturbations using plotTargetingDrugsVSsimilarPerturbations(), highlighting compounds that selectively select against cells with a similar differential gene expression profile
Analyse drug set enrichment (performDSEA()):
Prepare drug sets based on a table with compound identifiers and respective 2D and 3D molecular descriptors using prepareDrugSets()
Test drug set enrichment on results from rankSimilarPerturbations() (when ranking against compound perturbations) and predictTargetingDrugs()
Convert ENSEMBL identifiers to gene symbols using convertENSEMBLtoGeneSymbols()
Major changes
Update the tutorial and function documentation
Remove most L1000 instances, including in function names:
Improve loading of ENCODE samples (loadENCODEsamples()):
Rename function from downloadENCODEsamples() to loadENCODEsamples()
Load ENCODE samples regarding multiple cell lines and experiment targets
using loadENCODEsamples()
Improve CMap data and metadata retrieval:
By default, do not return control perturbation types when using getCMapPerturbationTypes() (unless if using argument control = TRUE)
Parse CMap identifiers using parseCMapID()
Load CMap's compound metadata using loadCMapData()
Ask to download CMap perturbations z-scores file for differential expression if not found (avoiding downloading a huge file without user consent)
Improve preparation of CMap perturbations (prepareCMapPerturbations()):
Allow to load CMap metadata directly from files when using file paths as arguments of prepareCMapPerturbations()
Significantly decrease memory required to use cTRAP by loading chunks of z-scores from CMap perturbations on-demand (a slight decrease in time performance is expected), unless prepareCMapPerturbations() is run with argument loadZscores = TRUE
Display summary of loaded perturbations after running prepareCMapPerturbations()
Improve ranking of similar perturbations (rankSimilarPerturbation()):
Redesigned output: long (instead of wide) table
By default, calculate mean across cell lines if there is more than one cell line available; disabled if argument cellLineMean = FALSE
Allow to rank (or not) individual cell line perturbations (argument rankIndividualCellLinePerturbations) when the mean is calculated
Allow to perform multiple comparison methods if desired (by providing a vector of supported methods via the method argument)
Calculate the rank product's rank to assess ranks across multiple methods
Sort results based on rank product's rank (or the rank of the only comparison method performed, otherwise)
Include information for calculated means across cell lines in metadata
Include run time as an attribute
Improve metadata display for a similarPerturbations object, obtained after running rankSimilarPerturbations():
Show further metadata information (including compound data, if available) related with a given perturbation by calling print() with a similarPerturbations object and a specific perturbation identifier
Show a complete table with metadata (and compound information, if available) when calling as.table() with a similarPerturbations object
Improve plotting (plot()):
Plot comparison results against all compared data by calling plot() with the results obtained after running rankSimilarPerturbations() or predictTargetingDrugs(); non-ranked compared data can also be plotted with argument plotNonRankedPerturbations = TRUE
Render scatter and Gene Set Enrichment Analysis (GSEA) plots between differential expression results and a single perturbation by calling plot() with a perturbationChanges object (if an identifier regarding the summary of multiple perturbations scores across cell lines is given, the plots are coloured by cell line)
When displaying GSEA plots, plot results for most up- and down-regulated user-provided differentially expressed genes (by default)
Improve GSEA plot style, including rug plot in enrichment score plot (replacing the gene hit plot)
Bug fixes and minor changes
CMap metadata minor improvements:
Improve list returned by getCMapConditions(), including sorting of dose and time points
Correctly set instances of -666 in CMap metadata as missing values and fix specific issues with metadata (such as doses displayed as 300 ng|300 ng)
In compound metadata, fix missing values showing as literal "NA" values
CMap perturbation minor improvements:
Fix error when subsetting a perturbationChanges object with only one row
Improve performance when subsetting perturbationChanges objects
Minor improvements to rankSimilarPerturbations():
Correctly set name of perturbations depending on their type (genes, biological agents or compounds)
Improve performance when correlating against multiple cell lines
Remove cellLine argument (please filter conditions with upstream functions such as filterCMapMetadata())
Fix incorrect label of first column identifiers
Report run time and settings used
Perform comparisons against perturbations disregarding their cell lines (faster runtime)
Fix error when trying to calculate the mean for cell lines with no intersecting conditions available
Clearly state to the user when no intersecting genes were found between input dataset and CMap data
Minor improvements to plot():
Improve rendering performance of the GSEA plot
Fix disproportionate height between top and bottom enrichment score panels in GSEA plots
Update demo datasets:
Update the cmapPerturbationsCompounds and cmapPerturbationsKD datasets according to new internal changes and fix their respective code in the documentation