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.