seqgendiff::EigenDiff()
since cate is no longer on CRAN.cate
as imports because it is not longer on CRAN.{optmatch}
as a suggested package since it is no longer on CRAN.LazyData: true
from DESCRIPTION since there is no ‘data’ directory.Fixes a lot of things for CRAN resubmission.
seqgendiff::EigenDiff()
. Replaces its usage with cate::est.factor.num()
. This is fine since it was only used in the now defunct seqgendiff::poisthin()
.{optmatch}
package is now only suggested rather than imported. This is because the {optmatch}
package is under a super weird license that I didn’t previously know about.{clue}
package, seems to work just as well as {optmatch}
, and so I added it as an option. However, since I used {optmatch}
in the simulations for the paper, I have kept permute_method = "optmatch"
as the default option.select_counts()
, a function that will subsample the rows (genes) and columns (samples) of a RNA-seq count matrix. It is generally recommended that you do this subsampling each iteration of a simulation study so that your results do not depend on the specific structure of your data. The samples are just selected randomly. There are four different criteria for selecting the genes.thin_all()
, a function that uniformly thins all counts.This has been a massive rewrite of the {seqgendiff}
package.
poisthin()
is now defunct. The two-group model is now implemented in the thin_2group()
function. I’ll keep it around since some of my old simulation code depends on it.thin_diff()
, thin_2group()
, thin_lib()
, and thin_gene()
.poisthin()
, do not have functionality to subset count matrices. This is on purpose. I wanted the functionality of these thinning functions to be simpler.poisthin()
, which can only handle the two-group model, thin_diff()
can handle generically any design, while still controlling the level of correlation between the design variables and the surrogate variables.ThinDataToSummarizedExperiment()
and ThinDataToDESeqDataSet()
.corassign()
lets you make group assignment that is correlated with hidden factors.poisthin()
, the group_assign = "cor"
option uses corassign()
to make group assignments.