data.table instead of
dplyr/tidyr/purrr.shiny application. #36Full Changelog: https://github.com/mcanouil/NACHO/compare/v1.1.0...v2.0.0
DESCRIPTION,
ggplot2 version (>= 3.3.0).dplyr version (>= 1.0.2).R/autoplot.R,
ggplot2::expand_scale() with
ggplot2::expansion().R/load_rcc.R,
dplyr::progress_estimated().R/norm_glm.R,
dplyr::progress_estimated().tests,
DESCRIPTION,
R/normalise.R,
normalise() without removing outliers (#26).R/GSE74821.R,
data-raw root directory.R/-,
Rd files for internal
functions.R/load_rcc.R and
R/normalise.R.file.path() in examples and vignette.R/autoplot.R, reduce alpha for ellipses.inst/app/utils.R, set default point size (also for
outliers) to 1.R/load_rcc.R, use inherits() instead of
class().R/conflicts.R,
NACHO.nacho_conflicts() can be used to print conflicts.inst/app/, (#4, #5 & #14)
visualise(), to load "nacho" object
from load_rcc() (previous summarise()) or from
normalise().deploy() (R/deploy.R) function to
easily deploy (copy) the shiny app.inst/extdata/.NACHO-analysis, which describe how to use
limma or other model after using –NACHO–.DESCRIPTION,
summarise() and summarize() have been
deprecated and replaced with load_rcc(). (#12 &
#15)raw_counts and
normalised_counts) are no longer (directly) available,
-i.e.-, counts are available in a long format within the
nacho slot of a nacho object.visualise(), now uses a new shiny app
(inst/app/).R/visualise.R, R/render.R,
print(), R/load_rcc.R and
R/normalise.R,
check_outliers().R/visualise.R, replace datatable (render and output)
with classical table. (#13)R/autoplot.R,
show_outliers to show outliers differently on plots
(-i.e.-, in red).outliers_factor to highligth outliers with
different point size.outliers_labels to print labels on top of
outliers._S-) to remove duplicated QC
metrics.R/print.R, now print a table with outliers if any
(with echo = TRUE).R/GSE74821.R, dataset is up to date according to
NACHO functions.DESCRIPTION, add
"SystemRequirements: pandoc (>= 1.12.3) - http://pandoc.org, pandoc-citeproc".R/render.R,
opts_chunk::knitr in roxygen
documentation.sessioninfo::session_info in
roxygen documentation.tests/testthat/test-render.R, now checks if pandoc
is available.tests/testthat/test-summarise.R, fix tests when
connection to GEO is alternatively up/down between two tests.autoplot() allows to plot a chosen QC plot available in
the shiny app (visualise()) and/or in the HTML report
(render()).print() allows to print the structure or to print text
and figures formatted using markdown (mainly to be used in a Rmakrdown
chunk).render() render figures from visualise()
in a HTML friendly output.R/read_rcc.R, R/summarise.R,
tidyr 1.0.0 (#9).R/summarise.R,
autoplot().tidyr 1.0.0 (#9).R/normalise.R,
autoplot().outliers_thresholds component in returned
object.R/visualise.R,
app object in non-interactive session.vignettes/NACHO.Rmd,
autoplot(), print() and
render() (#7).results = "asis").normalise() call with custom housekeeping genes
(-i.e.-, set housekeeping_predict = FALSE) (#10).tests/testthat/test-summarise.R, add condition to
handle when GEOQuery is down and cannot retrieve online
data.vignettes/NACHO.Rmd, add condition to handle when
GEOQuery is down and cannot retrieve online data.R/summarise.R, put example in
if (interactive()) {...} instead of
\dontrun{...}.R/normalise.R, put example in
if (interactive()) {...} instead of
\dontrun{...}.R/visualise.R, put example in
if (interactive()) {...} instead of
\dontrun{...}.DESCRIPTION and README, description
updated for CRAN, by adding “messenger-RNA/micro-RNA”.R/normalise.R, add short running example for
normalise().R/visualise.R, add short running example for
visualise().DESCRIPTION, description updated for CRAN, by
removing some capital letters and put –NACHO– between single
quotes.DESCRIPTION, title and description updated for
CRAN.DESCRIPTION and
vignetteDESCRIPTION, title and description updated for
CRAN.summarise() imports and pre-process RCC files.normalise() allows to change settings used in
summarise() and exclude outliers.visualise() allows customisation of the quality
thresholds.summarise(), ssheet_csv can take a
data.frame or a csv file.normalise() (and internal
functions).visualise() replaces the Shiny app.summarise() and
normalise() (and all internal functions).