---------- Forwarded message ----------
From: Loyal Goff
From: Loyal Goff
CummeRbund 2.0.0 release 10/3/2012
Following the release of Bioconductor 2.11, CummeRbund 2.0.0 is now available and is a recommended update for all CummeRbund users.
Version 2.0.0 adds a host of new options/features/bugfixes and is the first stable release version to fully support Cuffdiff2.
More information, details, vignettes, and downloads can be found at http://compbio.mit.edu/cummeRbund/. As always, thank you for your support.
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CHANGE LOG
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v2.0.0
Stable public release (Bioconductor 2.11)
v1.99.6
Notes:
- 'annotation' and "annotation<-" generics were moved to BiocGenerics 0.3.2. Now using appropriate generic function, but requiring BiocGenerics >= 0.3.2
v1.99.5
Bugfixes:
- Added replicates argument to csDistHeat to view distances between individual replicate samples.
- Appropriately distinguish now between 'annotation' (external attributes) and features (gene-level sub-features).
- csHeatmap now has 'method' argument to pass function for any dissimilarity metric you desire. You must pass a function that returns a 'dist' object applied to rows of a matrix. Default is still JS-distance.
v1.99.3
New Features:
- Added diffTable() method to return a table of differential results broken out by pairwise comparison. (more human-readable)
- Added sigMatrix() method to CuffSet objects to draw heatmap showing number of significant genes by pairwise comparison at a given FDR.
- A call to fpkm() now emits calculated (model-derived) standard deviation field as well.
- Can now pass a GTF file as argument to readCufflinks() to integrate transcript model information into database backend
* Added requirement for rtracklayer and GenomicFeatures packages.
* You must also indicate which genome build the .gtf was created against by using the 'genome' argument to readCufflinks.
- Integration with Gviz:
* CuffGene objects now have a makeGeneRegionTrack() argument to create a GeneRegionTrack() from transcript model information
* Can also make GRanges object
* ONLY WORKS IF YOU READ .gtf FILE IN WITH readCufflinks()
- Added csScatterMatrix() and csVolcanoMatrix() method to CuffData objects.
- Added fpkmSCVPlot() as a CuffData method to visualize replicate-level coefficient of variation across fpkm range per condition.
- Added PCAplot() and MDSplot() for dimensionality reduction visualizations (Principle components, and multi-dimensional scaling respectively)
- Added csDistHeat() to create a heatmap of JS-distances between conditions.
Bugfixes:
- Fixed diffData 'features' argument so that it now does what it's supposed to do.
- added DB() with signature(object="CuffSet") to NAMESPACE
Notes:
- Once again, there have been modifications to the underlying database schema so you will have to re-run readCufflinks(rebuild=T) to re-analyze existing datasets.
- Importing 'defaults' from plyr instead of requiring entire package (keeps namespace cleaner).
- Set pseudocount=0.0 as default for csDensity() and csScatter() methods (This prevents a visual bias for genes with FPKM <1 and ggplot2 handles removing true zero values).
v1.99.2
Bugfixes:
- Fixed bug in replicate table that did not apply make.db.names to match samples table.
- Fixed bug for missing values in *.count_tracking files.
- Now correctly applying make.db.names to *.read_group_tracking files.
- Now correctly allows for empty *.count_tracking and *.read_group_tracking files
v1.99.1
- This represents a major set of improvements and feature additions to cummeRbund.
- cummeRbund now incorporates additional information emitted from cuffdiff 2.0 including:
- run parameters and information.
- sample-level information such as mass and scaling factors.
- individual replicate fpkms and associated statistics for all features.
- raw and normalized count tables and associated statistics all features.
New Features:
- Please see updated vignette for overview of new features.
- New dispersionPlot() to visualize model fit (mean count vs dispersion) at all feature levels.
- New runInfo() method returns cuffdiff run parameters.
- New replicates() method returns a data.frame of replicate-level parameters and information.
- getGene() and getGenes() can now take a list of any tracking_id or gene_short_name (not just gene_ids) to retrieve
a gene or geneset.
- Added getFeatures() method to retrieve a CuffFeatureSet independent of gene-level attributes. This is ideal for looking at sets of features
outside of the context of all other gene-related information (i.e. facilitates feature-level analysis)
- Replicate-level fpkm data now available.
- Condition-level raw and normalized count data now available.
- repFpkm(), repFpkmMatrix, count(), and countMatrix are new accessor methods to CuffData, CuffFeatureSet, and CuffFeature objects.
- All relevant plots now have a logical 'replicates' argument (default = F) that when set to TRUE will expose replicate FPKM values in appropriate ways.
- MAPlot() now has 'useCount' argument to draw MA plots using count data as opposed to fpkm estimates.
Notes:
- Changed default csHeatmap colorscheme to the much more pleasing 'lightyellow' to 'darkred' through 'orange'.
- SQLite journaling is no longer disabled by default (The benefits outweigh the moderate reduction in load times).
Bugfixes:
- Numerous random bug fixes to improve consistency and improve performance for large datasets.
v1.2.1
Bugfixes:
-Fixed bug in CuffFeatureSet::expressionBarplot to make compatible with ggplot2 v0.9.
New Features:
- Added 'distThresh' argument to findSimilar. This allows you to retrieve all similar genes within a given JS distance as specified by distThresh.
- Added 'returnGeneSet' argument to findSimilar. [default = T] If true, findSimilar returns a CuffGeneSet of genes matching criteria (default). If false, a rank-ordered data frame of JS distance values is returned.
- findSimilar can now take a 'sampleIdList' argument. This should be a vector of sample names across which the distance between genes should be evaluated. This should be a subset of the output of samples(genes(cuff)).
Notes:
- Added requirement for 'fastcluster' package. There is very little footprint, and it makes a significant improvement in speed for the clustering analyses.
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Cheers,
Loyal
_________________________________________________
Loyal A. Goff, Ph.D
NSF Postdoctoral Fellow
Computer Science and Artificial Intelligence Laboratory - MIT
Stem Cell and Regenerative Biology - Harvard
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