NEWS

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scDD 1.13.1 (2020-08-17)

• When testing for difference in proportion of zeroes, if the Bayesian logistic regression fails to converge for a given gene (for example, if there is perfect separation between conditions), then NA will be returned (instead of throwing an error.

scDD 1.3.4 (2018-03-26)

• The test for differences in proportion of zeroes now uses the Wald test p-value instead of the likelihood ratio. This will give very similar results, but the Wald test is slightly more conservative.

scDD 1.3.3 (2018-03-23)

• An option has been added to skip the categorization step if only intereseted significance of difference. This will speed up computation.
• The testing zeroes step and the KS test have been parallelized to speed up computation.

scDD 1.1.7 (2017-10-23)

• A parameter 'level' was added to the main 'scDD' function which allows the user to control the significance level used as the cutoff for considering a gene to be differentially distributed. Previously it was fixed at 0.05 (now the default value).
• Two columns were added to the results object that contain (1) an overall combined p-value (via Fisher's method) for nonzero and zero differences, and (2) Benjamini-Hochberg adjusted version of (1).
• If 'testZeroes' is FALSE, the columns with p-values for the test of differential dropout are no longer included. Previously they held NA values.
• If 'testZeroes' is TRUE, all zero test p-values are included in the output. Previously, only the tests where the nonzero test was not significant were reported.

scDD 1.1.5 (2017-09-27)

• The input and output object of the main scDD function has been changed from SummarizedExperiment to SingleCellExperiment to increase interoperability among other Bioconductor packages
• The simulateSet function now returns a SingleCellExperiment object (previously it returned an object in list format)
• The preprocess function now takes as input a SingleCellExperiment object instead of a list of data frames

scDD 1.0.0 (2017-04-23)

• This is the Bioconductor 3.5 Release version
• scDD is a package for identifying differentially distributed genes in single-cell RNA-seq data. It is designed to detect differences in expression that are more complex than a simple mean shift, including: - traditional differential expression (DE) - differential modality (DM) - differential proportion of cells in each state (DP) - both differential modality and differential proportion (DB)

scDD 0.99.0 (2016-12-07)

• Initial Bioconductor submission version.