Version 1.15 (2023-11-04) + Fix typo in `quasi_gamma_poisson_shrinkage` example (thanks to @nlubock) + Add `sample_fraction` argument to `loc_median_fit` (once again thanks @nlubock) Version 1.13 (2023-07-03) + Implement a likelihood ratio test based on the Chi-squared distribution, if `test_de` is called after setting `overdispersion_shrinkage = FALSE`. Note that this test is less reliable than than the quasi-likelihood F test that is run for `overdispersion_shrinkage = TRUE`. Version 1.11 (2023-01-03) + Breaking change: rename 'pseudobulk_sce' to 'pseudobulk' + Add a new vignette explaining how and why pseudobulking is a powerful concept for single cell data analysis + Depcreate 'pseudobulk_by' argument in 'test_de'. Use the 'pseudobulk' function instead. + Add a new argument 'max_lfc' to to 'test_de' to avoid impractically large log fold changes for lowly expressed genes. + Support rlang quosures for the contrast argument in 'test_de' + Add a helper function called 'fact' that simplifies specification of contrast for complex experimental designs + Add 'use_assay' argument to 'glm_gp' + Add `vctrs` as dependency. The package is necessary to replicate the `group_by` behavior from `dplyr`. + Add 'size_factors = "ratio"' to emulate the behavior of DESeq2's size factor calculation + Make sure that the 'ignore_degeneracy' argument is propagated to 'test_de' Version 1.9 + Breaking change to the way that non-standard evaluation parameters are handled. Variables in arguments such as 'pseudobulk_by' or 'subset_to' which evaluate to a single string are no longer interpreted as referring to a column. This change makes the handling of NSE more consistent. + Add new function 'pseudobulk_sce' to easily form pseudobulk samples Version 1.5 + Choose a more reasonable scale for global overdispersion estimate + Make code more robust accidental internal NA's + Add fallback mechanism in case the Fisher scoring fails to converge. Instead of returing NA, try again using the BFGS algorithm. + Better error message if the design contains NA's Version 1.4 (2021-05-19) + Ridge regularization framework. glmGamPoi now supports regularizing the coefficient estimates using a quadratic penalty function. Furthmore, more advanced regularization schemes, such as regularizing towards a specific value and full Tikhonov regularization are implemented. + New predict() function. Also supports estimating the standard error of the mean estimate. + Make sure that Fisher scoring does not converge to unrealistically large values of mu + Fix minor bug in test_de() concerning the calculation of the degree of freedom + Fix minor bug in calculation of working and Pearson residuals, which used to return NaN if mu was 0. Now, they are 0. + Improve vignette/Readme: add section on differential expression analysis with Kang et al. (2018) as example data + `glm_gp` returns the Offset matrix and bug fix for test_de() if a offset was specified + Add CITATION file + Make sure that residuals are pristine (when the input was a DelayedArray) + Set dimnames of residuals + Improve error message if input is a sparse matrix Version 1.2 (2020-11-09) + Remove dual likelihood functions for overdispersion estimation. Instead merge functionality into conventional_***. This should cause no user facing changes, however should make it easier to maintain the package + Make conventional_score_function_fast() more robust to extreme inputs. Avoid numerically imprecise subtractions and employ bounds based on series expansions for very small input + If dispersion estimate quits because there is no maximum or all y are 0, return iterations = 0 + Add limits (1e-16 / 1e16) for nlminb estimates of the dispersion. This protects against errors due to NA's in the conventional_likelihood_fast + Automatically set 'size_factors = FALSE' for input with 0 or 1 row. This will change the estimated beta, but not the mu's + Rename gampoi_overdispersion_mle() -> overdispersion_mle() + Store data in the object returned by glm_gp() + Remove Y from the interface of residuals.glmGamPoi, because I can just get it directly from fit$data + Add function test_de() that does a quasi-likelihood ratio test to detect differentially expressed genes + Add functionality to make a pseudobulk test directly from test_de() by aggregating the data around one column + In group-wise beta estimation, fall back to optimize() if the Newton method fails + Change the default size factor estimation method from "poscounts" to "normed_sum" and provide an easy way to call scran::calculateSumFactors() + New "global" mode for dispersion estimation Changes in version 0.0.99 (2020-03-23) + Submitted to Bioconductor