CHANGES IN VERSION 1.30.0
-------------------------

    o Major overhaul of dispersion estimation and GLM estimation
      functions from Constantin Ahlmann-Eltze, which will allow use of
      the glmGamPoi package from within DESeq2, in particular relevant
      for single-cell datasets.  DESeq() can be directed to use
      glmGamPoi for dispersion and GLM fitting by specifying
      fitType="glmGamPoi". The glmGamPoi estimation is much faster
      than original DESeq2 estimation for single-cell datasets,
      e.g. for 30,000 cells, calling glmGamPoi was 13x faster than
      original DESeq2. In addition, the dispersion estimation is more
      accurate for genes with many small counts, as found in
      single-cell datasets.
      See glmGamPoi manuscript for details on methods,
      doi: 10.1101/2020.08.13.249623.
    o Added integrateWithSingleCell(), written by Kwame Forbes,
      which directs user to a menu of single-cell datasets
      available on Bioconductor and downloads/loads the one
      chosen by the user for further analysis visualization.
      (Interactive only)

CHANGES IN VERSION 1.28.0
-------------------------

    o For lfcShrink(), changed order of `type` options:
      "normal" will no longer be first, as it under-performed
      "apeglm" and "ashr" in Zhu et al (2018). The new order
      is type=c("apeglm", "ashr", "normal").
    o Added arguments to estimateDispersions: useCR (logical),
      and weightThreshold (numeric, default of 1e-2). The first
      argument optionally avoid the calculation of Cox-Reid
      adjustment term. The second argument sets the threshold for
      subsetting the design matrix and GLM weights when calculating
      the adjustment term. In addition, baseMean uses weights
      when calculating the mean of normalized counts, if weights
      matrix is provided.

CHANGES IN VERSION 1.27.12
--------------------------

    o For lfcShrink(), changed order of `type` options:
      "normal" will no longer be first, as it under-performed
      "apeglm" and "ashr" in Zhu et al (2018). The new order
      is type=c("apeglm", "ashr", "normal").

CHANGES IN VERSION 1.27.9
-------------------------

    o Added arguments to estimateDispersions: useCR (logical),
      and weightThreshold (numeric, default of 1e-2). The first
      argument optionally avoid the calculation of Cox-Reid
      adjustment term. The second argument sets the threshold for
      subsetting the design matrix and GLM weights when calculating
      the adjustment term. In addition, baseMean uses weights
      when calculating the mean of normalized counts, if weights
      matrix is provided.

CHANGES IN VERSION 1.26.0
--------------------------

    o Incorporation of fast code from Constantin Ahlmann-Eltze
      which speeds up DESeq2 for large sample sizes (n > 100)
      by at least an order of magnitude. In fact the speed is
      now linear with number of samples whereas previously
      DESeq2 would scale quadratically. The critical merge
      commits were:
      c96c1c0ad43280c82403d3e6bc3501332a62e7b8 (2019-07-16)
      0a47a0c750aa5c31df759a171c737d6ed782d6c2 (2019-07-30)
    o Fixed a bug where rbind() in parallel=TRUE would
      proliferate metadata items.
    o Updated vignette to discuss tximeta (workflow also updated
      to show use of tximeta instead of read counting).

CHANGES IN VERSION 1.22.0
-------------------------

    o No replicate designs no longer supported (previous
      version began deprecation with a warning).
    o unmix() now optionally will return the correlation
      (in the variance stabilized space)
      of the fitted data to the original data, and the
      matrix of fitted data (format="list"). Argument
      'loss' was changed to 'power'. Will give warning
      if the columns of 'pure' have high correlation
      (in the variance stabilized space).      

CHANGES IN VERSION 1.21.21
--------------------------

    o Improved code for 'linearModelMu' (an internal fitting function
      used in dispersion estimation for some models) contributed 
      by Wolfgang Huber speeds up an internal step by 2 orders
      of magnitude.

CHANGES IN VERSION 1.21.15
--------------------------

    o Rows of the weights matrix which would produce a degenerate
      design matrix, instead of giving an error, will produce a
      warning, and these rows will be treated as if they contained
      all zeros (mcols(dds)$allZero and mcols(dds)$weightsFail will
      be set to TRUE).

CHANGES IN VERSION 1.21.14
--------------------------

    o The nbinom{WaldTest,LRT} functions will not stop if the
      design produces a model matrix that is not full rank
      and betaPrior=FALSE (default). This was assumed by the
      DESeq2 code, because errors are produced at object
      construction and at dispersion estimation, but it was
      possible to call nbinomLRT() from DEXSeq after dispersion
      estimation and end up with a full model matrix that was
      not full rank. Instead testForDEU() should be called from
      DEXSeq.

CHANGES IN VERSION 1.21.13
--------------------------

    o Adding back a feature from version 1.15, where contrasts
      of two groups where both had all zero counts would have
      the LFC zero-ed out, rather than output a small but
      non-zero value. It's preferable for the Wald test that
      the LFC be set to zero for such contrasts.

CHANGES IN VERSION 1.21.9
-------------------------

    o DESeq() now only says one time 'using supplied model matrix',
      previously this was repeated three times from sub-functions.
      Sub-functions therefore no longer print this message.
    o Fixed bug when lfcShrink run directly after LRT
      with supplied model matrices.
    o Added heuristic to prevent Cook's outlier based filtering
      when the max Cook's sample has lower counts than 3 other
      samples. Restricted to two group comparison datasets.

CHANGES IN VERSION 1.20.0
--------------------------

    o Added 'lfcThreshold' argument to lfcShrink() for use
      with type="normal" and type="apeglm". For the latter,
      lfcShrink() will compute FSOS s-values, for bounding
      when the LFC will be "false sign or small", where
      small is defined by lfcThreshold.
    o Switching to a ~10x faster apeglm implementation for
      use in the lfcShrink() function.    
    o Beginning the deprecation of exploratory analysis of
      designs without replicates. Analysis of designs without
      replicates will be removed in the Oct 2018 release:
      DESeq2 v1.22.0, after which DESeq2 will give an error.
    o Elevate 'minmu' to DESeq() as this proves useful for
      single cell applications and certain zero-inflated data.
    o Elevate 'useT' to DESeq(), which will use (n - p) for the
      degrees of freedom of the t distribution, and if weights
      are provided, it will use the sum of weights as 'n'.

CHANGES IN VERSION 1.18.0
-------------------------

    o lfcShrink() offers alternative estimators type="apeglm"
      and type="ashr", making use of shrinkage estimators
      in the 'apeglm' and 'ashr' packages, respectively.
      See ?lfcShrink for more details and appropriate
      references. The integration of these alternative
      shrinkage estimators is still in development.
      Additionally, the DESeqResults object gains priorInfo(res),
      which passes along details of the fitted prior on LFC.
    o Factor levels using characters other than letters,
      numbers, '_' and '.' will print a message (not a warning
      or error) that it is recommended to restrict to these
      "safe characters". This follows a suggestion from the
      Bioconductor support site to avoid user errors.

CHANGES IN VERSION 1.16.0
-------------------------

    o DESeq() and nbinomWaldTest() the default setting
      will be betaPrior=FALSE, and the recommended pipeline
      will be to use lfcShrink() for producing shrunken LFC.
    o Added a new function unmix(), for unmixing samples
      according to linear combination of pure components,
      e.g. "tissue deconvolution".
    o Added a new size factor estimator, "poscounts",
      which evolved out of use cases in Paul McMurdie's
      phyloseq package.
    o Ability to specify observation-specific weights,
      using assays(dds)[["weights"]]. These weights are
      picked up by dispersion and NB GLM fitting functions.

CHANGES IN VERSION 1.15.40
--------------------------

    o Adding a new function unmix(), for
      unmixing samples according to pure components,
      e.g. "tissue deconvolution". The pure components
      are added on the gene expression scale
      (either normalized counts or TPMs), and the loss
      is calculated in a variance stabilized space.

CHANGES IN VERSION 1.15.39
--------------------------

    o Added a new size factor estimator, "poscounts",
      which evolved out of use cases in Paul McMurdie's
      phyloseq package.

CHANGES IN VERSION 1.15.36
--------------------------

    o Ability to specify observation-specific weights,
      using assays(dds)[["weights"]]. These weights are
      picked up by dispersion and NB GLM fitting functions.

CHANGES IN VERSION 1.15.28
--------------------------

    o Remove some code that would "zero out" LFCs
      when both groups involved in a contrast had zero counts.
      This lead to inconsistency when similarly contrasts
      were performed by refactoring.

CHANGES IN VERSION 1.15.12
--------------------------

    o DESeq() and nbinomWaldTest() the default setting
      will be betaPrior=FALSE, and the recommended pipeline
      will be to use lfcShrink() for producing shrunken
      log2 fold changes for visualization and ranking.
      Explanation for the change is presented in the
      vignette section:
      "Methods changes since the 2014 DESeq2 paper"

CHANGES IN VERSION 1.15.9
-------------------------

    o Adding prototype function lfcShrink().

    o Vignette conversion to Rmarkdown / HTML.

CHANGES IN VERSION 1.15.3
-------------------------

    o Removing betaPrior option for nbinomLRT, in an effort
      to clean up and reduce old un-used functionality.

CHANGES IN VERSION 1.13.8
-------------------------

    o Use a linear model to estimate the expected counts
      for dispersion estimation in estDispGeneEst()
      if the number of groups in the model matrix
      is equal to the number of columns of the model
      matrix. Should provide a speed-up for dispersion
      estimation for model matrices with many samples.

CHANGES IN VERSION 1.13.3
-------------------------

    o Fixed bug: fpm() and fpkm() for tximport.
    o Fixed bug: normalization factors and VST.
    o Added an error if tximport lengths have 0.
    o Added an error if user matrices are not full rank.
    o More helpful error for constant factor in design.

CHANGES IN VERSION 1.12.0
-------------------------

    o Added DESeqDataSetFromTximport() to import
      counts using tximport.
    o Added vst() a fast wrapper for the VST.
    o Added support for IHW p-value adjustment.

CHANGES IN VERSION 1.11.42
--------------------------

    o Update summary() to be IHW-results-aware.

    o Small change to fitted mu values to improve fit stability
      when counts are very low. Inference for high count genes
      is not affected.

    o Galaxy script inst/script/deseq2.R moves to Galaxy repo.

CHANGES IN VERSION 1.11.33
--------------------------

    o Changed 'filterFun' API to accommodate IHW:
      independent hypothesis weighting in results(),
      see vignette for example code. 
      Thanks to Nikolaos Ignatiadis, maintainer of IHW package.

CHANGES IN VERSION 1.11.18
--------------------------

    o Added a function vst(), which is a fast wrapper for
      varianceStabilizingTransformation(). The speed-up
      is accomplished by subsetting to a smaller number
      of genes for the estimation of the dispersion trend.

CHANGES IN VERSION 1.11.5
-------------------------

    o Adding in functionality to import estimated counts and
      average transcript length offsets from tximport,
      using DESeqDataSetFromTximport().

CHANGES IN VERSION 1.10.0
-------------------------

    o Added MLE argument to plotMA().

    o Added normTransform() for simple log2(K/s + 1) transformation,
      where K is a count and s is a size factor.

    o When the design contains an interaction, DESeq() will use
      betaPrior=FALSE. This makes coefficients easier to interpret.

    o Independent filtering will be less greedy, using as a
      threshold the lowest quantile of the filter such that the
      number of rejections is within 1 SD from the maximum.
      See ?results.

    o summary() and plotMA() will use 'alpha' from results().

CHANGES IN VERSION 1.9.42
-------------------------

    o New function 'normTranform', for making DESeqTransform objects
      from normalized counts plus a pseudocount (default 1) then
      applying a transformation (default log2).

    o Added MLE argument to plotMA(), if results() was run with
      addMLE=TRUE, this allows for comparison of shrunken and
      unshrunken estimates of fold change.

    o summary() and plotMA() use the 'alpha' which was specified
      in results() rather than defaulting to 0.1.

    o Removed rlog's fast option, and instead recommending VST for
      very large matrices of counts (100s of samples).

CHANGES IN VERSION 1.9.17
-------------------------

    o Independent filtering: results() no longer uses the maximum
      of the number of rejections as calculated by the filter_p() function
      from the genefilter package. Small numbers of rejections at a
      high quantile of the filter threshold could result in
      a high filter threshold. Instead, now the results() function
      will use the lowest quantile of the filter for which the
      number of rejections is close to the peak of a lowess curve fit
      through the number of rejections over the filter quantiles.
      'Close to' is defined as within 1 residual standard deviations.

CHANGES IN VERSION 1.9.16
-------------------------

    o When the design formula contains interaction terms, the DESeq()
      function will by default not use a beta prior (betaPrior=FALSE).
      The previous implementation of a log fold change prior for
      interaction terms returned accurate inference, but was confusing
      for users to interpret. New instructions on building results tables
      for designs with interactions will be included in the software
      vignette.

CHANGES IN VERSION 1.8.0
------------------------

    o Added support for user-supplied model matrices to DESeq(),
      estimateDispersions() and nbinomWaldTest(). This helps
      when the model matrix needs to be edited by the user.

CHANGES IN VERSION 1.7.45
-------------------------

    o Added a test in rlog for sparse data, mostly zero and some 
      very large counts, which will give a warning and suggestion 
      for alternate transformations.

    o Added plotSparsity() which will help diagnose issues for using rlog:
      data which do not resemble negative binomial due to many genes 
      with mostly zeros and a few very large counts.

CHANGES IN VERSION 1.7.43
-------------------------

    o Added 'replaced' argument to counts() and plotCounts() such
      that the assay in "replaceCounts" will be used if it exists.
      Raised a minimum dispersion value used in Cook's calculation, 
      so that other counts in a group with an outlier won't get extreme
      Cook's distances themselves.

CHANGES IN VERSION 1.7.32
-------------------------

    o Added logic to results() which will zero out the LFC, Wald
      statistic and set p-value to 1, for 'contrast' argument
      results tables where the contrasted groups all have zero count.
      Non-zero LFCs were otherwise occuring due to large differences
      in the size factors.

CHANGES IN VERSION 1.7.11
-------------------------

    o Added support for user-supplied model matrices to DESeq(),
      estimateDispersions() and nbinomWaldTest().

CHANGES IN VERSION 1.7.9
------------------------

    o Added Genome Biology citation for the DESeq2 methods.

    o Introduced type="iterate" for estimateSizeFactors, 
      an alternative estimator for the size factors, which
      can be used even when all genes have a sample with a 
      count of zero. See man page for details.

CHANGES IN VERSION 1.7.3
------------------------

    o Fixed two minor bugs: 
      DESeq() with parallel=TRUE was dropping rows with all zero 
      counts, instead of propogating NAs.
      nbinomLRT() with matrices provided to 'full' and 'reduced' and 
      a design of ~ 1, the matrices were being ignored.

CHANGES IN VERSION 1.6.0
------------------------

    o DESeq() and results() gets a 'parallel' argument.

    o results() gets an 'addMLE' argument.

    o results() gets a 'test' argument, for constructing Wald tests
      after DESeq() was run using the likelihood ratio test.

    o results() argument 'format' for GRanges or GRangesList results.

    o new plotCounts() function.

    o Less outlier calling from Cook's distance for analyses with 
      many samples and many conditions.

    o More robust beta prior variance and log fold change shrinkage.

CHANGES IN VERSION 1.5.70
-------------------------

    o Added 'parallel' also for results(), which can be slow if run with
      100s of samples.

CHANGES IN VERSION 1.5.54
-------------------------

    o Added 'parallel' argument to DESeq() which splits up the analysis
      over genes for those steps which are easily done in parallel, 
      leveraging BiocParallel's bplapply.

CHANGES IN VERSION 1.5.50
-------------------------

    o A matrix can be provided to rlog or to the VST and will return 
      a matrix. Also 'fitType' argument is included, in case dispersions
      are not estimated which is passed on to estimateDispersions.

CHANGES IN VERSION 1.5.49
-------------------------

    o The fast=TRUE implementation of rlog is even faster, subsetting
      genes along the range of base mean to estimate the dispersion
      trend and for fitting the optimal amount of shrinkage.

CHANGES IN VERSION 1.5.40
-------------------------

    o Further improved code behind the robust estimation of variance
      for Cook's cutoff, resulting in less outlier calls due to 
      an individual condition with few samples and high variance.

CHANGES IN VERSION 1.5.28
-------------------------

    o New results() argument 'addMLE' adds the unshrunken fold changes
      for simple contrasts or interaction terms to the results tables.

CHANGES IN VERSION 1.5.27
-------------------------

    o Applied the beta prior variance calculation from v1.5.22 to the 
      regularized logarithm.

    o Added MLE coefficients as MLE_condition_B_vs_A columns to mcols(dds).

    o Fixed the statistic which is returned when lfcThreshold is used.
      Previously, only the p-value and adjusted p-value was changed.

    o plotPCA() with argument 'returnData' will return a data.frame 
      which can be used for custom plotting.

CHANGES IN VERSION 1.5.25
-------------------------

    o Improved the robust variance estimate used for calculating
      Cook's distances. The previous estimate could lead to outlier 
      calls in datasets with many conditions, and when a single 
      condition had large, highly variable counts for all its samples.

CHANGES IN VERSION 1.5.22
-------------------------

    o Adding an alternate method for beta prior variance calculation
      in nbinomWaldTest. This helps to produce more robust prior 
      variance estimates when many genes have small counts and highly
      variable MLE log fold changes.

CHANGES IN VERSION 1.5.15
-------------------------

    o For likelihood ratio test, expanded model matrices not default.
      Some improvements in fit time from handling of genes with
      dispersions that do not converge using line search.

CHANGES IN VERSION 1.5.13
-------------------------

    o Adding test argument to results(), which allows users to perform
      a Wald test after DESeq(dds, test="LRT") / nbinomLRT has been run.

CHANGES IN VERSION 1.5.11
------------------------

    o Swapping in ggplot2 for lattice for the plotPCA function.

CHANGES IN VERSION 1.5.9
------------------------

    o Added a VST for fitType = mean. Allowed designs with ~ 0 
      and betaPrior = FALSE. Fixed some potential metadata
      column insertion bugs.

CHANGES IN VERSION 1.5.8
------------------------

    o Suppress the glm.fit convergence warning from parametric dispersion
      curve fitting procedure, instead use this for the iterative
      convergence test.

CHANGES IN VERSION 1.5.3
------------------------

    o Speeding up and reducing copying for DESeqDataSet construction.

CHANGES IN VERSION 1.5.2
------------------------

    o Added 'format' argument to results, which will attach results to
      GRangesList or GRanges if requested (default is DataFrame).

CHANGES IN VERSION 1.4.4
------------------------

    o Fixed a hang which could occur in the GLM fitting procedure.

CHANGES IN VERSION 1.4.3
------------------------

    o Fixed simple bug when using normalizationFactors and running 
      nbinomWaldTest, error was "no method for coercing this S4 class 
      to a vector".

CHANGES IN VERSION 1.4.2
------------------------

    o Fixed bugs: estimating beta prior for interaction between factor 
      and numeric; not returning row names for counts(); construction
      of DESeqDataSet gives wrong error when there are empty levels:
      instead now drops the levels for the user.


CHANGES IN VERSION 1.4.1
------------------------

    o Fixed bug where DESeqDataSetFromHTSeqCount() imported the special
      rows, "_ambiguous", etc.

CHANGES IN VERSION 1.4.0
------------------------

    o *** USAGE NOTE *** Expanded model matrices are now used when 
      betaPrior = TRUE (the default). Therefore, level comparison results 
      should be extracted using the 'contrast' argument to the results() 
      function. Expanded model matrices produce shrinkage of log
      fold changes that is independent of the choice of base level.
      Expanded model matrices are not used in the case of designs
      with an interaction term between factors with only 2 levels.

    o The order of the arguments 'name' and 'contrast' to the results()
      function are swapped, to indicate that 'contrast' should be used 
      for the standard comparisons of levels against each other.
      Calling results() with no arguments will still produce the 
      same comparison: the fold change of the last level of the last 
      design variable over the first level of the last design variable.
      See ?results for more details.

    o The DESeq() function will automatically replace count outliers
      flagged by Cook's distance when there are 7 or more replicates.
      The DESeq() argument 'minReplicatesForReplace' (default 7)
      is used to decide which samples are eligible for automatic 
      replacement. This default behavior helps to prevent filtering 
      genes based on Cook's distance when there are many degrees of 
      freedom.

CHANGES IN VERSION 1.3.58
-------------------------

    o Added a list() option to the 'contrast' argument of results().
      See examples in ?results.

CHANGES IN VERSION 1.3.24
-------------------------

    o rlogTransformation() gains an argument 'fast', which switches to
      an approximation of the rlog transformation. Speed-up is ~ 2x.

    o A more robust estimator for the beta prior variance is used:
      instead of taking the mean of squared MLE betas, the prior variance
      is found by matching an upper quantile of the absolute value of
      MLE betas with an upper quantile of a zero-centered Normal 
      distribution.

CHANGES IN VERSION 1.3.17
-------------------------

    o It is possible to use a log2 fold change prior (beta prior) 
      and obtain likelihood ratio test p-values, although the default 
      for test="LRT" is still betaPrior=FALSE.

CHANGES IN VERSION 1.3.15
-------------------------

    o The DESeq() function will automatically replace count outliers
      flagged by Cook's distance when there are 7 or more replicates.
      The DESeq() argument 'minReplicatesForReplace' (default 7)
      is used to decide which samples are eligible for automatic 
      replacement. This default behavior helps to prevent filtering 
      genes based on Cook's distance when there are many degrees of 
      freedom.

    o The results() function produces an object of class 'DESeqResults'
      which is a simple subclass of 'DataFrame'. This class allows for 
      methods to be written specifically for DESeq2 results. For example,
      plotMA() can be called on a 'DESeqResults' object.

CHANGES IN VERSION 1.3.12
-------------------------

    o Added a check in nbinomWaldTest which ensures that priors
      on logarithmic fold changes are only estimated for interactions 
      terms, in the case that interaction terms are present in the 
      design formula.

CHANGES IN VERSION 1.3.6
------------------------

    o Reduced the amount of filtering from Cook's cutoff:
      maximum no longer includes samples from experimental groups 
      with only 2 samples, the default F quantile is raised to 0.99,
      and a robust estimate of dispersion is used to calculate
      Cook's distance instead of the fitted dispersion.

CHANGES IN VERSION 1.3.5
------------------------

    o New arguments to results(), 'lfcThreshold' and 
      'alternativeHypothesis', allow for tests of log fold changes
      which are above or below a given threshold.

    o plotMA() function now passes ellipses arguments to the
      results() function.
    
CHANGES IN VERSION 1.1.32
-------------------------
    
    o By default, use QR decomposition on the design matrix X.
      This stabilizes the GLM fitting. Can be turned off with
      the useQR argument of nbinomWaldTest() and nbinomLRT().

    o Allow for "frozen" normalization of new samples using
      previous estimated parameters for the functions: 
      estimateSizeFactors(), varianceStabilizingTransformation(),
      and rlogTransformation(). See manual pages for details and
      examples.

CHANGES IN VERSION 1.1.31
-------------------------

    o The adjustment of p-values and use of Cook's distance
      for outlier detection is moved to results() function
      instead of nbinomWaldTest(), nbinomLRT(), or DESeq().
      This allows the user to change parameter settings 
      without having to refit the model.

CHANGES IN VERSION 1.1.24
-------------------------

    o The results() function allows the user to specify a 
      contrast of coefficients, either using the names of 
      the factor and levels, or using a numeric contrast 
      vector. Contrasts are only available for the Wald test
      differential analysis.

CHANGES IN VERSION 1.1.23
-------------------------

    o The results() function automatically performs independent
      filtering using the genefilter package and optimizing 
      over the mean of normalized counts.

CHANGES IN VERSION 1.1.21
-------------------------

    o The regularized log transformation uses the fitted
      dispersions instead of the MAP dispersions. This prevents
      large, true log fold changes from being moderated due to
      a large dispersion estimate blind to the design formula.
      This behavior is also more consistent with the variance
      stabilizing transformation.

CHANGES IN VERSION 1.0.10
-------------------------

    o Outlier detection: Cook's distances are calculated for each
      sample per gene and the matrix is stored in the assays list.
      These values are used to determine genes in which a single 
      sample disproportionately influences the fitted coefficients. 
      These genes are flagged and the p-values set to NA.
      The argument 'cooksCutoff' of nbinomWaldTest() and 
      nbinomLRT() can be used to control this functionality.


CHANGES IN VERSION 1.0.0
------------------------

    o Base class: SummarizedExperiment is used as the superclass 
      for storing the data.

    o Workflow: The wrapper function DESeq() performs all steps 
      for a differential expression analysis. Individual steps are 
      still accessible.

    o Statistics: Incorporation of prior distributions into the 
      estimation of dispersions and fold changes (empirical-Bayes 
      shrinkage). A Wald test for significance is provided as the 
      default inference method, with the likelihood ratio test of 
      the previous version also available.

    o Normalization: it is possible to provide a matrix of sample- 
      *and* gene-specific normalization factors
