Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D loci of players in the FIFA 2021 dataset obtained by the UMAP with the distances: ( a ) d A r ; ( b ) d C a ; ( c ) d C o ; ( d ) d L o .

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques:

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D loci obtained by the UMAP with the Canberra distance d C a for the FIFA 2021 dataset. The colormap is proportional to the attributes: ( a ) ln ( overall ) ; ( b ) ln ( value _ eur ) ; ( c ) ln ( wage _ eur ) ; ( d ) ln ( release _ clause _ eur ) .

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques:

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D loci of players in the groups {Defenders, Centre Midfielders, Wingers, Strikers} the FIFA 2021 dataset obtained by the UMAP with the distances: ( a ) d A r ; ( b ) d C a ; ( c ) d C o ; ( d ) d L o .

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques:

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D loci obtained by the UMAP with the Canberra distance for the FIFA 2021 dataset: ( a ) Goalkeepers; ( b ) Strikers. The colormap is proportional to the attribute ln ( value _ eur ) .

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques:

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable goalkeepers in the FIFA 2021 dataset. The reference is J. Oblak and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable defenders in the FIFA 2021 dataset. The reference is V. van Dijkand and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable midfielders in the FIFA 2021 dataset. The reference is K. De Bruyne and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable wingers in the FIFA 2021 dataset. The reference is Neymar Jr and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable strikers in the FIFA 2021 dataset. The reference is L. Messi and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Cell reports

Article Title: AP-1 transcription factor network explains diverse patterns of cellular plasticity in melanoma cells

doi: 10.1016/j.celrep.2022.111147

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: This paper N/A Software and algorithms Source code for analyses This paper https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma CellProfiler (3.1.9) ( McQuin et al., 2018 ) https://cellprofiler.org/ ImageJ (2.3.0) Public Domain Software https://imagej.nih.gov/ij/index.html MATLAB (2020b) Mathworks https://matlab.mathworks.com/ R (4.0.4) The Comprehensive R Archive Network (CRAN) https://www.r-project.org/ stats R package (4.1.2) The R Project https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html AUCell R package (1.16.0) (Aibaretal., 2017) https://bioconductor.org/packages/release/bioc/vignettes/AUCell/inst/doc/AUCell.html umap R package (0.2.7.0) CRAN https://cran.r-project.org/web/packages/umap/ SCopeLoomR R package (0.13.0) Aerts Lab https://github.com/aertslab/SCopeLoomR Python (3.9.2) N/A https://www.python.org/downloads/ Scanpy (1.7.1) ( Wolf et al., 2018 ) https://github. com/scverse/scanpy pySCENIC (0.11.0) ( Van de Sande et al., 2020 ) https://github.com/aertslab/SCENICprotocol Nextflow (20.10.0) Seqera Labs https://www.nextflow.io/ Scikit-learn Library (0.24.1) ( Pedregosa et al., 2018 ) https://scikit-learn.org/stable/ SHAP ( Lundberg and Lee, 2017 ) https://github.com/slundberg/shap Other 96-well plates Corning Cat# 3904 Differentiation signature gene sets ( Tsoi et al., 2018 ) N/A Proliferative and Invasive phenotype gene sets ( Hoek et al., 2006 ) http://www.jurmo.ch/work_97.php List of bZIP transcription factor genes ( Vinson et al., 2002 ) https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma List of transcription factor genes ( Van de Sande et al., 2020 ) https://raw.githubusercontent.com/aertslab/pySCENIC/master/resources/hs_hgnc_tfs.txt Nextflow pipeline adapted for running SCENIC iteratively ( Wouters et al., 2020 ) https://github.com/aertslab/singlecellRNA_melanoma_paper R scripts adapted for extracting and filtering regulons from multiple SCENIC runs ( Wouters et al., 2020 ) https://github.com/aertslab/singlecellRNA_melanoma_paper Homo sapiens whole-genome motif ranking databases for SCENIC (motif collection v9) Aerts Lab https://resources.aertslab.org/cistarget/ Motif annotation file for SCENIC (motif collection v9) Aerts Lab https://resources.aertslab.org/cistarget/ Open in a separate window KEY RESOURCES TABLE The original codes for data analysis performed in this paper are publicly available at GitHub: https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma ( https://doi.org/10.5281/zenodo.6741989 ).

Techniques: Recombinant, Blocking Assay, Expressing, Activity Assay, Software

Journal: Cell reports

Article Title: AP-1 transcription factor network explains diverse patterns of cellular plasticity in melanoma cells

doi: 10.1016/j.celrep.2022.111147

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: This paper N/A Software and algorithms Source code for analyses This paper https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma CellProfiler (3.1.9) ( McQuin et al., 2018 ) https://cellprofiler.org/ ImageJ (2.3.0) Public Domain Software https://imagej.nih.gov/ij/index.html MATLAB (2020b) Mathworks https://matlab.mathworks.com/ R (4.0.4) The Comprehensive R Archive Network (CRAN) https://www.r-project.org/ stats R package (4.1.2) The R Project https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html AUCell R package (1.16.0) (Aibaretal., 2017) https://bioconductor.org/packages/release/bioc/vignettes/AUCell/inst/doc/AUCell.html umap R package (0.2.7.0) CRAN https://cran.r-project.org/web/packages/umap/ SCopeLoomR R package (0.13.0) Aerts Lab https://github.com/aertslab/SCopeLoomR Python (3.9.2) N/A https://www.python.org/downloads/ Scanpy (1.7.1) ( Wolf et al., 2018 ) https://github. com/scverse/scanpy pySCENIC (0.11.0) ( Van de Sande et al., 2020 ) https://github.com/aertslab/SCENICprotocol Nextflow (20.10.0) Seqera Labs https://www.nextflow.io/ Scikit-learn Library (0.24.1) ( Pedregosa et al., 2018 ) https://scikit-learn.org/stable/ SHAP ( Lundberg and Lee, 2017 ) https://github.com/slundberg/shap Other 96-well plates Corning Cat# 3904 Differentiation signature gene sets ( Tsoi et al., 2018 ) N/A Proliferative and Invasive phenotype gene sets ( Hoek et al., 2006 ) http://www.jurmo.ch/work_97.php List of bZIP transcription factor genes ( Vinson et al., 2002 ) https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma List of transcription factor genes ( Van de Sande et al., 2020 ) https://raw.githubusercontent.com/aertslab/pySCENIC/master/resources/hs_hgnc_tfs.txt Nextflow pipeline adapted for running SCENIC iteratively ( Wouters et al., 2020 ) https://github.com/aertslab/singlecellRNA_melanoma_paper R scripts adapted for extracting and filtering regulons from multiple SCENIC runs ( Wouters et al., 2020 ) https://github.com/aertslab/singlecellRNA_melanoma_paper Homo sapiens whole-genome motif ranking databases for SCENIC (motif collection v9) Aerts Lab https://resources.aertslab.org/cistarget/ Motif annotation file for SCENIC (motif collection v9) Aerts Lab https://resources.aertslab.org/cistarget/ Open in a separate window KEY RESOURCES TABLE The original codes for data analysis performed in this paper are publicly available at GitHub: https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma ( https://doi.org/10.5281/zenodo.6741989 ).

Techniques: Recombinant, Blocking Assay, Expressing, Activity Assay, Software