Changes in version 1.14.1
- 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
Changes in version 1.12.0
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
Changes in version 1.10.1
- Fix crash when opening CMap's visual interface functions with
shiny 1.7.0
Changes in version 1.10.0
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
Changes in version 1.8
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
Changes in version 1.6.1
- 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() nows 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
Changes in version 1.4
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
Changes in version 1.0.3
- Add tag ImmunoOncology to BiocViews
Changes in version 1.0.2
- Fix comparison against CMap perturbations using gene set enrichment
analysis (the resulting score was the additive inverse of the real
scores)
Changes in version 1.0.1
- 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