Thursday, 4 October 2012

CummeRbund 2.0.0 released

---------- Forwarded message ----------
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  As always, thank you for your support.

Stable public release (Bioconductor 2.11)

- 'annotation' and "annotation<-" generics were moved to BiocGenerics 0.3.2. Now using appropriate generic function, but requiring BiocGenerics >= 0.3.2

- 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.

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.  

- Fixed diffData 'features' argument so that it now does what it's supposed to do.
- added DB() with signature(object="CuffSet") to NAMESPACE

- 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).

- 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

- 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.

- 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).

- Numerous random bug fixes to improve consistency and improve performance for large datasets.

-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)).
- Added requirement for 'fastcluster' package.  There is very little footprint, and it makes a significant improvement in speed for the clustering analyses.



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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