Addresses #270 and #259

version 0.30.0

This version integrates p-value aggregation as described in Yi et al.. The behavior of gene-level differential expression testing now follows this procedure:

1. Isoform-level testing.
2. P-value aggregation at the gene level (using target_mapping) by the lancaster method.

Thank you to Lynn Yi for implementing p-value aggregation. Please see pull request #148 for details.

The API has also slightly changed. Particularly, for sleuth_prep, several options have been moved to optional arguments via .... See pull request #168 for more information or ?sleuth_prep in R.

A fair amount of speed up and bug fixes have also been implemented.

• Patch: bugs in sleuth_results & other miscellaneous fixes
• Fix behavior of sleuth_results when gene_mode is TRUE (and error reporting)
• Shiny and Plot Fixes / Enhancements
• Quick Patch: UseMethod typo
• Update write_kallisto_hdf5 function and add ability to subset kallisto object (address #131)
• extend sleuth to model TPMs
• Fixes to various miscellaneous issues (#73, #84, #97, #122, #135, #142)
• Improvements to shiny and plot functions (solving several open issues)
• Possible solution to NAs in sleuth_lrt, addressing #68
• bug fix patches
• address #113 - patch bug where TPM bootstrap summary target_ids are moved
• New tests for ".N" target mappings
• Misc bug fixes + Allow sleuth_prep to process just one sample

A major thanks to Warren McGee for doing the majority of the heavy lifting on all of the bug fixes.

This version has numerous bug fixes and several performance upgrades. Most notably, memory usage has been decreased greatly by no longer storing the bootstraps in memory. Additionally, speed has been improved in numerous areas — particularly sleuth_prep — by changing several of the computations as well as changing the order of the parallelization (special thanks to Warren McGee for his contributions to this).

Below is an incomplete list of new features:

• The full model no longer has to be specified in sleuth_prep.
• A new function extract_model allow users to extract the effect sizes for a model in a tidy format similar to broom.
• An arbitrary transformation can be specified/used in sleuth_prep (see argument transformation_function).

A big thanks to our users for fixing and reporting bugs. A special thanks to Warren McGee for making several of the performance improvements as well as fixing several bugs. Below is a partial list of many of the upgrades and the pull requests by the community.

• Memory overhaul to reduce overall usage (@psturmfels)
• Bugfix to drop unused factors (@roryk and @warrenmcg)
• Reduce memory footprint and improve parallelization (@warrenmcg)
• Add gene annotations when using sleuth_results (@warrenmcg)
• Improve sample name handling (@warrenmcg)
• Reconcile memory overhaul and gene aggregation and allow arbitrary transformations (@warrenmcg)
• Do not parallelize when in RStudio (@warrenmcg)
• Remove warning in sliding_window_grouping (@warrenmcg)
• Bug fix in sleuth_live in gene mode (@warrenmcg)

• Gene aggregation can now be performed via aggregation_column in sleuth_prep.
• Documentation has improved
• Vignette is updated
• A few bugs have been fixed (plot_pca)

There have been several changes including a very important bug fix. It is recommended that all users upgrade.

new features in the model

• support for likelihood ratio test
• support for arbitrary design matrices
• support for arbitrary normalizations

bug fixes

• sometimes the samples would get reordered based off of the sample names. This has been fixed.

miscellaneous

• the interface of sleuth_prep has changed to encourage users to correctly specify the connection between samples, kallisto results, and covariates
• imports fragment length distribution from kallisto
• view the bias weights learned from kallisto
• generalize reading from h5 allowing filename or directory to be specified

visualization and sleuth_live

• most figures and tables can be saved directly from sleuth_live
• fragment length distribution in sleuth_live
• variance explained from principal components can be visualized
• PCA loadings can now be visualized

• various bug fixes
• gene table, gene viewer, transcript heatmap, and volcano plot by Pascal Sturmfels