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decoupleR 2.0.0

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.

decoupleR 1.1.0

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()