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