From 2b7fc83cb9dc9292b24ae51b1340f5c870867703 Mon Sep 17 00:00:00 2001 From: Frederic Bertrand Date: Sun, 21 Mar 2021 02:09:12 +0100 Subject: [PATCH] Update github actions with magick --- .github/workflows/R-CMD-check.yaml | 6 +++ README.Rmd | 69 +++++++++++++++--------------- README.md | 69 +++++++++++++++--------------- docs/index.html | 26 ++++++----- docs/pkgdown.yml | 2 +- 5 files changed, 92 insertions(+), 80 deletions(-) diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 918b6c6..e3ebbfd 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -23,6 +23,12 @@ jobs: - name: Install XQuartz on macOS if: runner.os == 'macOS' run: brew install xquartz --cask + - name: Install ghostscript on macOS + if: runner.os == 'macOS' + run: brew install ghostscript + - name: Install Magick on macOS + if: runner.os == 'macOS' + run: brew install imagemagick - name: Install dependencies run: | install.packages(c("remotes", "rcmdcheck")) diff --git a/README.Rmd b/README.Rmd index 1ba8237..ae70711 100644 --- a/README.Rmd +++ b/README.Rmd @@ -56,37 +56,6 @@ The weights are viewed as a penalty factors in the penalized regression model: i -![Infered F matrix of the network (General shape).](docs/reference/figures/README-Fresults-1.png) - - - -![Infered coefficient matrix of the network (General shape).](docs/reference/figures/README-heatresults-1.png) - - - -![Infered F matrix of the network (cascade shape).](docs/reference/figures/README-FresultsLC-1.png) - - - -![Infered coefficient matrix of the network (cascade shape).](docs/reference/figures/README-heatresultsLC-1.png) - - - -![Reverse-engineered network.](docs/reference/figures/README-plotnet2-1.png) - - - -![Evolution of a reverse-engineered network with increasing cut-off values.](docs/reference/animation.gif) - - - -![Plot of simulated data for cascade networks featuring cluster membership.](docs/reference/figures/README-plotsimuldata-1.png) - - - -![Plot of simulated data for cascade networks featuring subject membership.](docs/reference/figures/README-plotsimuldata-2.png) - - A word for those that have been using our seminal work, the `Cascade` package that we created several years ago and that was a very efficient network reverse engineering tool for cascade networks (Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014), , , and ). @@ -512,12 +481,12 @@ destdir = "~/Github/Patterns/docs/reference/evolution/" evolution(network,sequence,type.ani = "gif",outdir=destdir) evolution(network,sequence,type.ani = "html",outdir=destdir) ``` -![Evolution as .gif.](docs/reference/evolution/animation.gif) +![Evolution as .gif.](https://fbertran.github.io/Patterns/reference/evolution/animation.gif) -[Evolution as .html.](docs/reference/evolution/index.html) +[Evolution as .html.](https://fbertran.github.io/Patterns/reference/evolution/index.html) Evolution of some properties of a reverse-engineered network with increasing cut-off values. -![Evolution of some properties of a reverse-engineered network with increasing cut-off values.](docs/reference/compare-methods-1.png) +![Evolution of some properties of a reverse-engineered network with increasing cut-off values.](https://fbertran.github.io/Patterns/reference/compare-methods-1.png) We switch to data that were derived from the inferrence of a real biological network and try to detect the optimal cutoff value: the best cutoff value for a network to fit a scale free network. The `cutoff` was validated only single group cascade networks (number of actors groups = number of timepoints) and for genes dataset. Instead of the `cutoff` function, manual curation or the stability selection or the selectboost algorithm should be used. @@ -598,4 +567,36 @@ This process could be improved by retrieve a real gene_ID using the `bitr` funct +### Examples of outputs + +![Infered F matrix of the network (General shape).](https://fbertran.github.io/Patterns/reference/figures/README-Fresults-1.png) + + + +![Infered coefficient matrix of the network (General shape).](https://fbertran.github.io/Patterns/reference/figures/README-heatresults-1.png) + + + +![Infered F matrix of the network (cascade shape).](https://fbertran.github.io/Patterns/reference/figures/README-FresultsLC-1.png) + + + +![Infered coefficient matrix of the network (cascade shape).](https://fbertran.github.io/Patterns/reference/figures/README-heatresultsLC-1.png) + + + +![Reverse-engineered network.](https://fbertran.github.io/Patterns/reference/figures/README-plotnet2-1.png) + + + +![Evolution of a reverse-engineered network with increasing cut-off values.](https://fbertran.github.io/Patterns/reference/evolution/animation.gif) + + + +![Plot of simulated data for cascade networks featuring cluster membership.](https://fbertran.github.io/Patterns/reference/figures/README-plotsimuldata-1.png) + + + +![Plot of simulated data for cascade networks featuring subject membership.](https://fbertran.github.io/Patterns/reference/figures/README-plotsimuldata-2.png) + diff --git a/README.md b/README.md index b6c5d6e..846faa2 100644 --- a/README.md +++ b/README.md @@ -47,37 +47,6 @@ The weights are viewed as a penalty factors in the penalized regression model: i -![Infered F matrix of the network (General shape).](docs/reference/figures/README-Fresults-1.png) - - - -![Infered coefficient matrix of the network (General shape).](docs/reference/figures/README-heatresults-1.png) - - - -![Infered F matrix of the network (cascade shape).](docs/reference/figures/README-FresultsLC-1.png) - - - -![Infered coefficient matrix of the network (cascade shape).](docs/reference/figures/README-heatresultsLC-1.png) - - - -![Reverse-engineered network.](docs/reference/figures/README-plotnet2-1.png) - - - -![Evolution of a reverse-engineered network with increasing cut-off values.](docs/reference/animation.gif) - - - -![Plot of simulated data for cascade networks featuring cluster membership.](docs/reference/figures/README-plotsimuldata-1.png) - - - -![Plot of simulated data for cascade networks featuring subject membership.](docs/reference/figures/README-plotsimuldata-2.png) - - A word for those that have been using our seminal work, the `Cascade` package that we created several years ago and that was a very efficient network reverse engineering tool for cascade networks (Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014), , , and ). @@ -1419,12 +1388,12 @@ evolution(network,sequence,type.ani = "html", outdir=getwd()) #> Error in setwd(outdir): impossible de changer de répertoire de travail #> Error in setwd(outdir): impossible de changer de répertoire de travail ``` -![Evolution as .gif.](docs/reference/evolution/animation.gif) +![Evolution as .gif.](https://fbertran.github.io/Patterns/reference/evolution/animation.gif) -[Evolution as .html.](docs/reference/evolution/index.html) +[Evolution as .html.](https://fbertran.github.io/Patterns/reference/evolution/index.html) Evolution of some properties of a reverse-engineered network with increasing cut-off values. -![Evolution of some properties of a reverse-engineered network with increasing cut-off values.](docs/reference/compare-methods-1.png) +![Evolution of some properties of a reverse-engineered network with increasing cut-off values.](https://fbertran.github.io/Patterns/reference/compare-methods-1.png) We switch to data that were derived from the inferrence of a real biological network and try to detect the optimal cutoff value: the best cutoff value for a network to fit a scale free network. The `cutoff` was validated only single group cascade networks (number of actors groups = number of timepoints) and for genes dataset. Instead of the `cutoff` function, manual curation or the stability selection or the selectboost algorithm should be used. @@ -1736,4 +1705,36 @@ This process could be improved by retrieve a real gene_ID using the `bitr` funct +### Examples of outputs + +![Infered F matrix of the network (General shape).](https://fbertran.github.io/Patterns/reference/figures/README-Fresults-1.png) + + + +![Infered coefficient matrix of the network (General shape).](https://fbertran.github.io/Patterns/reference/figures/README-heatresults-1.png) + + + +![Infered F matrix of the network (cascade shape).](https://fbertran.github.io/Patterns/reference/figures/README-FresultsLC-1.png) + + + +![Infered coefficient matrix of the network (cascade shape).](https://fbertran.github.io/Patterns/reference/figures/README-heatresultsLC-1.png) + + + +![Reverse-engineered network.](https://fbertran.github.io/Patterns/reference/figures/README-plotnet2-1.png) + + + +![Evolution of a reverse-engineered network with increasing cut-off values.](https://fbertran.github.io/Patterns/reference/evolution/animation.gif) + + + +![Plot of simulated data for cascade networks featuring cluster membership.](https://fbertran.github.io/Patterns/reference/figures/README-plotsimuldata-1.png) + + + +![Plot of simulated data for cascade networks featuring subject membership.](https://fbertran.github.io/Patterns/reference/figures/README-plotsimuldata-2.png) + diff --git a/docs/index.html b/docs/index.html index e95b4bd..649440a 100644 --- a/docs/index.html +++ b/docs/index.html @@ -141,14 +141,6 @@

  • Examples of use with microarray or RNA-Seq data are provided.

    • The weights are viewed as a penalty factors in the penalized regression model: it is a number that multiplies the lambda value in the minimization problem to allow differential shrinkage, Friedman et al. 2010, equation 1 page 3. If equal to 0, it implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables. Infinity means that the variable is excluded from the model. Note that the weights are rescaled to sum to the number of variables.

    -

    Infered F matrix of the network (General shape).

    -

    Infered coefficient matrix of the network (General shape).

    -

    Infered F matrix of the network (cascade shape).

    -

    Infered coefficient matrix of the network (cascade shape).

    -

    Reverse-engineered network.

    -

    Evolution of a reverse-engineered network with increasing cut-off values.

    -

    Plot of simulated data for cascade networks featuring cluster membership.

    -

    Plot of simulated data for cascade networks featuring subject membership.

    A word for those that have been using our seminal work, the Cascade package that we created several years ago and that was a very efficient network reverse engineering tool for cascade networks (Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014), https://doi.org/10.1093/bioinformatics/btt705, https://cran.r-project.org/package=Cascade, https://github.com/fbertran/Cascade and https://fbertran.github.io/Cascade/).

    The Patterns package is more than (at least) a threeway major extension of the Cascade package :

      @@ -1270,9 +1262,9 @@

      evolution(network,sequence,type.ani = "html", outdir=getwd()) 
      #> Error in setwd(outdir): impossible de changer de répertoire de travail
 #> Error in setwd(outdir): impossible de changer de répertoire de travail
      -

      Evolution as .gif.

      -

      Evolution as .html.

      -

      Evolution of some properties of a reverse-engineered network with increasing cut-off values. Evolution of some properties of a reverse-engineered network with increasing cut-off values.

      +

      Evolution as .gif.

      +

      Evolution as .html.

      +

      Evolution of some properties of a reverse-engineered network with increasing cut-off values. Evolution of some properties of a reverse-engineered network with increasing cut-off values.

      We switch to data that were derived from the inferrence of a real biological network and try to detect the optimal cutoff value: the best cutoff value for a network to fit a scale free network. The cutoff was validated only single group cascade networks (number of actors groups = number of timepoints) and for genes dataset. Instead of the cutoff function, manual curation or the stability selection or the selectboost algorithm should be used.

       data("networkCascade")
@@ -1536,6 +1528,18 @@ 
      
 
      plot of chunk microselection7plot of chunk microselection7plot of chunk microselection7plot of chunk microselection7plot of chunk microselection7plot of chunk microselection7plot of chunk microselection7plot of chunk microselection7
      
 
      This process could be improved by retrieve a real gene_ID using the bitr function of the ClusterProfiler package or by performing independent filtering using jetset package to only keep at most only probeset (the best one, if there is one good enough) per gene_ID.
      +
      +

      +Examples of outputs

      +

      Infered F matrix of the network (General shape).

      +

      Infered coefficient matrix of the network (General shape).

      +

      Infered F matrix of the network (cascade shape).

      +

      Infered coefficient matrix of the network (cascade shape).

      +

      Reverse-engineered network.

      +

      Evolution of a reverse-engineered network with increasing cut-off values.

      +

      Plot of simulated data for cascade networks featuring cluster membership.

      +

      Plot of simulated data for cascade networks featuring subject membership.

      +
      diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 859bbcf..fd38dbc 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -4,5 +4,5 @@ pkgdown_sha: ~ articles: ExampleCLL: ExampleCLL.html IntroPatterns: IntroPatterns.html -last_built: 2021-03-21T00:17Z +last_built: 2021-03-21T01:04Z