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@nuno-agostinho nuno-agostinho released this 12 Nov 00:10
1.4
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Learn about vigilant mode.

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:
    • getL1000perturbationTypes() -> getCMapPerturbationTypes()
    • getL1000conditions() -> getCMapConditions()
    • downloadL1000data() -> loadCMapData()
    • filterL1000metadata() -> filterCMapMetadata()
    • loadL1000perturbations() -> prepareCMapPerturbations()
    • compareAgainstL1000() -> rankSimilarPerturbations()
    • plotL1000comparison() -> plot()
  • 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
  • Include license and copyright text for cmapR code
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