NEWS.md

In nuno-agostinho/cTRAP: Identification of candidate causal perturbations from differential gene expression data

cTRAP 1.20.1 (6 March, 2024)

• When running cTRAP(), raise error if commonPath does not exist
• Fix modular graphical interface functions not showing dropdown choices
• Update icon names for FontAwesome 6

cTRAP 1.14.1 (14 July, 2022)

• Add cTRAP version to welcome modal
• Fix crash when submitting Celery job (included missing floweRy function)
• Fix large JSON responses from DT blocked by reverse proxy default settings

cTRAP 1.12.0 (27 October, 2021)

Web server support (optimised to run in ShinyProxy)

• cTRAP(): new global interface with all cTRAP functionality in one place
  • Sessions can be created and loaded via a token or a RDS file
  • Session data is automatically saved in a RDS file to a folder in the working directory named based on the current session token
  • Long-running tasks can be performed in the background using the Celery task manager via Flower's REST API and their output is automatically loaded in the corresponding session
  • Loading icon in navigation menu when Shiny is busy
• Use the faster and efficient file format from R package qs instead of RDS:
  • Faster download and loading of pre-processed remote files (compound molecular descriptors and gene expression and drug sensitivity associations)

Bug fixes and minor improvements

• convertGeneIdentifiers() replaces convertENSEMBLtoGeneSymbols():
  • Use AnnotationHub to convert to gene symbols (instead of biomaRt that has been unstable)
• loadENCODEsamples():
  • New argument to select folder where to download data
• analyseDrugSetEnrichment():
  • Cross-match more compounds between datasets by discarding non-alphanumeric characters and ignoring case
  • Fix incorrect columns used for each dataset when merging datasets
• Visual interface:
  • Fix crash when plotting dataset comparison using values with too many zeroes for density estimation
  • Add progress bars for slower tasks
  • Fix crash when using shiny 1.7.0 (avoid malformed, custom UI elements)
• Drug set enrichment analysis interface:
  • Show all drug sets available to (down)load
  • Show loading indicator when loading different drug sets
  • Hide "leading edge" column of the results by default

cTRAP 1.10.1 (4 October, 2021)

• Fix crash when opening CMap's visual interface functions with shiny 1.7.0

cTRAP 1.10.0 (18 March, 2021)

Improvements to graphical interface functions:

• New launchDrugSetEnrichmentAnalysis() function to analyse drug set enrichment and visualize respective results
• launchCMapDataLoader():
  • Now allows to load multiple CMap perturbation types simultaneously
  • Keep selected timepoint, dosage and cell line options when selecting another perturbation type
  • Add bubble plot of CMap perturbation types
• launchResultPlotter():
  • Now allows to view tables below specific plots and drag-and-select those plots to filter data in those same tables
  • When plotting targeting drugs and similar perturbations, update available columns and correctly use user-selected column to plot
• launchMetadataViewer() now correctly parses values from Input attributes as numeric

Major changes

• prepareCMapPerturbations(): directly set perturbation type, cell line, timepoint and dosage conditions as arguments
• rankSimilarPerturbations() and predictTargetingDrugs():
  • Avoid redundant loading of data chunks, slightly decreasing run time
  • Lower memory footprint when using NCI60's gene expression and drug sensitivity association (now available in HDF5 files) by loading and processing data in chunks
  • Faster GSEA-based score calculation (up to 4-7 times faster)
  • New threads argument allows to set number of parallel threads (not supported on Windows)
  • New chunkGiB argument allows to set size of data chunks when reading from supported HDF5 files (decreases peak RAM usage)
  • New verbose argument allows to increase details printed in the console
• prepareDrugSets(): allow greater control on the creation of bins based on numeric columns, including the setting of maximum number of bins per column and minimum bin size
• analyseDrugSetEnrichment() and plotDrugSetEnrichment(): allow to select columns to use when comparing compound identifiers between datasets

Bug fixes and minor changes

• filterCMapMetadata(): allow filtering CMap metadata based on multiple perturbation types
• prepareDrugSets(): fix issues with 3D descriptors containing missing values
• plot():
  • Fix wrong labels when plotting targetingDrugs objects
  • Avoid printing "NA" in labels identifying metadata for perturbations
• plotTargetingDrugsVSsimilarPerturbations():
  • Fix highlighting of plot points depending whether drug activity is directly proportional to drug sensitivity
  • Include rug plot
• When subsetting a perturbationChanges or an expressionDrugSensitivityAssociation object, passing only one argument extracts its columns as in previous versions of cTRAP (similarly to when subsetting a data.frame)
• analyseDrugSetEnrichment(): for the resulting table, the name of the first column was renamed from pathway to descriptor

cTRAP 1.8 (23 October, 2020)

Interactive functions for loading data and analysing results

• New Shiny-based graphical interface functions:
  • launchDiffExprLoader(): load differential expression data
  • launchCMapDataLoader(): load CMap data
  • launchResultPlotter(): view and plot data results
  • launchMetadataViewer(): check metadata of a given object

Major changes

• downloadENCODEknockdownMetadata(): metadata is automatically saved to a file in order to avoid downloading metadata every time this function is run
• plotTargetingDrugsVSsimilarPerturbations():
  • automatically look for matching compounds in multiple columns of both datasets
  • allow to manually select columns on which to merge datasets
• prepareDrugSets(): drug sets based on numeric molecular descriptors are now prepared using evenly-distributed intervals
• Simplify tutorial

cTRAP 1.6.1 (17 August, 2020)

• listExpressionDrugSensitivityAssociation() lists available gene expression and drug sensitivity associations
• First argument of rankSimilarPerturbations() and predictTargetingDrugs() changed name from diffExprGenes to input and now accepts:
  • Named numeric vector containing differential gene expression values with gene symbols as names, as before;
  • Character vector containing a custom gene set to test for enrichment (only to use with GSEA).
• In rankSimilarPerturbations() and predictTargetingDrugs(), when performing gsea method, allow to set different gene set size for top up- and down-regulated genes with geneSize argument:
  • e.g. geneSize=c(100, 200) creates gene sets from the top 100 up- and top 200 down-regulated genes
  • using geneSize=c(150, 150) or geneSize=150 is equivalent
• Plotting:
  • plot() now supports plotting predictTargetingDrugs() results for a given drug, e.g. plot(targetingDrugs, "1425")
  • plot() now allows to set plot title with argument title
  • plot() now plots results based on available methods instead of trying to plot based on results from spearman method only
  • GSEA plots now support two or less gene hits
  • GSEA plots now support plotting of multiple perturbations
  • GESA plots now show the first and last values of ranked genes
  • plotDrugSetEnrichment() now returns a list whose names are drug set names
• as.table() improvements:
  • Return cell identifiers and gene information (if available and as needed)
  • Support predictTargetingDrugs() results
  • Return results ordered as found on input

Bug fixes and minor changes

• downloadENCODEknockdownMetadata() now correctly retrieves metadata following a change in the metadata content from ENCODE
• Fix bugs when rendering GSEA plots due to deprecated functions in ggplot2
• Improve tutorial
• Copy-edit CMap-related console messages
• Copy-edit function documentation

cTRAP 1.4 (25 October, 2019)

New features

• Predict targeting drugs (predictTargetingDrugs()):
  • 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 (analyseDrugSetEnrichment()):
  • 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 (rankSimilarPerturbations()):
  • 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

cTRAP 1.0.3 (3 December, 2018)

• Add tag ImmunoOncology to BiocViews

cTRAP 1.0.2 (11 November, 2018)

• Fix comparison against CMap perturbations using gene set enrichment analysis (the resulting score was the additive inverse of the real scores)

cTRAP 1.0.1 (2 November, 2018)

• Update title, author names, version and README
• Remove biomaRt dependency
• By default, getL1000conditions() now shows CMap perturbation types except for controls
• Compare against CMap perturbations (compareAgainstL1000()):
  • Remove "_t" from resulting column names (as the t-statistic may or may not be used)
  • Select p-value adjustment method when performing correlation analyses (Benjamini-Hochberg is set by default)
• Documentation:
  • Fix obsolete function calls in function documentation
  • Hide non-exported functions from reference PDF manual

nuno-agostinho/cTRAP documentation built on Jan. 2, 2025, 12:11 a.m.