Changes in version 2.3
Changes
- ulm and mlm are now faster but consume more memory.
- mat is now transformed to matrix automatically.
New features
- Added get_collectri wrapper to easily access the CollecTRI Gene
Regulatory Network Network from Omnipath.
- Added get_ksn_omnipath wrapper to easily access the Kinase-Substrate
network from Omnipath.
Changes in version 2.2
Changes
- Changed example mat and net to toy examples.
- Changed test data to toy data.
Bugfixes
- ora now selects correctly the top and bottom genes for p-value
estimation.
- wmean and wsum now handle named matrices with only one sample.
Changes in version 2.1
Changes
- 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:
- Now properly handles weights in mor by normalizing them to -1
and +1.
- ulm/mlm/udt/mdt methods:
- Changed how they processed the input network. Before the model
matrix only contained the intersection of features between mat
and network's targets, now it incorporates all features coming
from mat ensuring a more robust prediction. Prediction values
may change slightly from older versions.
- Deprecated sparse argument.
- ora method:
- Now takes top 5% features as default input instead of 300 up and
bottom features.
- Added seed to randomly break ties
- consensus method:
- No longer based on 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:
- Kinase inference example
- Graphical abstract
- Manuscript and citation
- New vignette style
- Updated documentation for all methods.
New features
- 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).
Bugfixes
- 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.
Changes in version 2.0
Changes
- 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 features
- New methods added:
- Fast Gene Set Enrichment Analysis (fgsea).
- AUCell.
- Univariate Decision Tree (udt).
- Multivariate Decision Tree (mdt).
- Multivariate Linear Model (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.
Changes in version 1.1
New features
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.
Methods
- 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:
- New run_gsva() incorporate a convinient wrapper for
GSVA::gsva().
- New run_mean() calculates both the unnormalized regulatory
activity and the normalized (i.e. z-score) one based on an
empirical distribution.
- New run_ora() fisher exact test to calculate the regulatory
activity.
- New run_pscira() uses a logic equivalent to run_mean() with the
difference that it does not accept a column of likelihood.
- New run_scira() calculates the regulatory activity through the
coefficient $\beta_1$ of an adjusted linear model.
- New run_viper() incorporate a convinient wrapper for
viper::viper().
Converters
- New functions family convert_to_ variants that allows the conversion
of data to a standard format.
- New convert_to_() return the entry without modification.
- New convert_to_gsva() return a list of regulons suitable for
GSVA::gsva().
- New convert_to_mean() return a tibble with four columns: tf,
target, mor and likelihood.
- New convert_to_ora() returns a named list of regulons; tf with
associated targets.
- New convert_to_pscira() returns a tibble with three columns: tf,
target and mor.
- New convert_to_scira() returns a tibble with three columns: tf,
target and mor.
- New convert_to_viper() return a list of regulons suitable for
viper::viper()