ulm and mlm are now faster but consume more memory.mat is now transformed to matrix automatically.get_collectri wrapper to easily access the CollecTRI Gene Regulatory
Network Network from Omnipath.get_ksn_omnipath wrapper to easily access the Kinase-Substrate network
from Omnipath.mat and net to toy examples.ora now selects correctly the top and bottom genes for p-value estimation.wmean and wsum now handle named matrices with only one sample.likelihood param is deprecated, from now on, weights (positive or negative)
should go to the mor column of network. Methods will still run if
likelihood is specified, however they will be set to 1.
Added minsize argument to all methods, set to 5 by default. Sources
containing less than this value of targets in the input mat will be removed
from the calculations.
Changed default behavior of the decouple function. Now if no methods are
specified in the statistics argument, the function will only run the top
performers in our benchmark (mlm, ulm and wsum). To run all methods like
before, set statistics to 'all'. Moreover, the argument consensus_stats has
been added to filter statistics for the calculation of the consensus score.
By default it only uses mlm, ulm and norm_wsum, or if statistics=='all'
all methods returned after running decouple.
viper method:
mor by normalizing them to -1 and +1.ulm/mlm/udt/mdt methods:
sparse argument.ora method:
consensus method:
RobustRankAggreg. Now the consensus score is the mean of the
activities obtained after a double tailed z-score transformation.Discarded filter_regulons function.
Moved major dependencies to Suggest to reduce the number of dependencies needed.
Updated README by adding:
Updated documentation for all methods.
Added wrappers to easily query Omnipath, one of the largest data-bases
collecting prior-knowledge resources. Added these functions:
show_resources: shows available resources inside Omnipath.get_resource: gets any resource from Omnipath.get_dorothea: gets the DoRothEA gene regulatory network for
transcription factor (TF) activity estimation. Note: this version is
slightly different from the one in the package dorothea since it contains
new edges and TFs and also weights the interactions by confidence levels.get_progeny: gets the PROGENy model for pathway activity estimation.Added show_methods function, it shows how many statistics are currently
available.
Added check_corr function, it shows how correlated regulators in a network
are. It can be used to check for co-linearity for mlm and mdt.
Added new error for mlm when co-variables are co-linear (regulators are too
correlated to fit a model).
wmean and wsum now return the correct empirical p-values.
ulm, mlm, mdt and udt now accept matrices with one column as input.
Results from ulm and mlm now correctly return un-grouped.
Methods correctly run when mat has no column names.
Some method's names have been changed to make them easier to identify:
pscira now is called Weighted Sum (wsum).mean now is called Weighted Mean (wmean).scira now is called Univariate Linear Model (ulm).The column name for tf in the output tibbles has been changed to source.
Updated documentation for all methods.
Updated vignette and README.
decouple function now accepts order mismatch between the list of methods and
the list of methods's arguments.
Moved benchmark branch to a separate repository as its own package: https://github.com/saezlab/decoupleRBench
New methods added:
fgsea).AUCell.udt).mdt).mlm).New decoupleR manuscript repository: https://github.com/saezlab/decoupleR_manuscript
New consensus score based on RobustRankAggreg::aggregateRanks() added when
running decouple with multiple methods.
New statistic corr_wmean inside wmean.
Methods based on permutations or statistical tests now return also a p-value
for the obtained score (fgsea, mlm, ora, ulm, viper, wmean and
wsum).
New error added when network edges are duplicated.
New error added when the input matrix contains NAs or Infs.
All new features allow for tidy selection. Making it easier to evaluate different types of data for the same method. For instance, you can specify the columns to use as strings, integer position, symbol or expression.
New decouple() integrates the various member functions of the
decoupleR statistics for centralized evaluation.
New family decoupleR statists for shared documentation is made up of:
run_gsva() incorporate a convinient wrapper for GSVA::gsva().run_mean() calculates both the unnormalized regulatory activity
and the normalized (i.e. z-score) one based on an empirical distribution.run_ora() fisher exact test to calculate the regulatory activity.run_pscira() uses a logic equivalent to run_mean() with the
difference that it does not accept a column of likelihood.run_scira() calculates the regulatory activity through the coefficient
$\beta_1$ of an adjusted linear model.run_viper() incorporate a convinient wrapper for viper::viper().convert_to_ variants that allows the conversion
of data to a standard format.
convert_to_() return the entry without modification.convert_to_gsva() return a list of regulons suitable for GSVA::gsva().convert_to_mean() return a tibble with four columns:
tf, target, mor and likelihood.convert_to_ora() returns a named list of regulons; tf with
associated targets.convert_to_pscira() returns a tibble with three columns:
tf, target and mor.convert_to_scira() returns a tibble with three columns:
tf, target and mor.convert_to_viper() return a list of regulons suitable for
viper::viper()