fastica matlab implementation Search Results


fastica matlab program  (MathWorks Inc)
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MathWorks Inc fastica matlab program
Fastica Matlab Program, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fastica matlab implementation  (MathWorks Inc)
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MathWorks Inc fastica matlab implementation
Fastica Matlab Implementation, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fastica toolbox  (MathWorks Inc)
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Fastica Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fastica  (MathWorks Inc)
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MathWorks Inc fastica
Summary of ICA software.
Fastica, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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compiled matlab fastica implementation  (MathWorks Inc)
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MathWorks Inc compiled matlab fastica implementation
Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of <t>fastICA</t> (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score
Compiled Matlab Fastica Implementation, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fastica package  (MathWorks Inc)
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MathWorks Inc fastica package
Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of <t>fastICA</t> (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score
Fastica Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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implementation of the fastica algorithm  (MathWorks Inc)
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MathWorks Inc implementation of the fastica algorithm
Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of <t>fastICA</t> (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score
Implementation Of The Fastica Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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ica routine function matlab implementation  (MathWorks Inc)
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MathWorks Inc ica routine function matlab implementation
Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of <t>fastICA</t> (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score
Ica Routine Function Matlab Implementation, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fastica version 2.1  (MathWorks Inc)
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MathWorks Inc fastica version 2.1
Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of <t>fastICA</t> (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score
Fastica Version 2.1, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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princomp function in  (MathWorks Inc)
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MathWorks Inc princomp function in
Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of <t>fastICA</t> (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score
Princomp Function In, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fastica package for  (MathWorks Inc)
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Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of <t>fastICA</t> (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score
Fastica Package For, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fastica function  (MathWorks Inc)
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MathWorks Inc fastica function
Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of <t>fastICA</t> (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score
Fastica Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Summary of ICA software.

Journal: BioMed Research International

Article Title: A Review of Feature Extraction Software for Microarray Gene Expression Data

doi: 10.1155/2014/213656

Figure Lengend Snippet: Summary of ICA software.

Article Snippet: FastICA is also implemented in MATLAB.

Techniques: Software, Extraction

Sources of ICA software.

Journal: BioMed Research International

Article Title: A Review of Feature Extraction Software for Microarray Gene Expression Data

doi: 10.1155/2014/213656

Figure Lengend Snippet: Sources of ICA software.

Article Snippet: FastICA is also implemented in MATLAB.

Techniques: Software

Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of fastICA (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score

Journal: BMC Genomics

Article Title: Determining the optimal number of independent components for reproducible transcriptomic data analysis

doi: 10.1186/s12864-017-4112-9

Figure Lengend Snippet: Analysis of component reproducibility in independent datasets. a Graph of reciprocal correlations showing the reproducibility of the metagenes of independent components in 6 independent breast cancer datasets. Each node here is an independent component, represented by a metagene, from an ICA decomposition with M = 100 components. Edges show only reciprocal correlations between metagenes with Pearson correlation >0.3. Triangles (on the right) show the components driven by the expression of a small group of genes (frequently, one gene). Node size reflects the rank of the component based on the stability in multiple runs of fastICA (larger nodes are more stable ones). The edge width and the color reflect the value of the correlation coefficient between two metagenes, with thicker edges showing larger correlation values. Several pseudo-cliques of highly reproducible components are annotated either by the dominating small group of genes (pseudo-cliques of triangle nodes), or by comparing to the results of the previously published large-scale ICA-based analysis of gene expression or by performing the hypergeometric test using the set of top-contributing genes (with projection larger than 5.0 onto the component). The analogous correlation graph computed for MSTD number of components is provided in Additional file : Figure SF3. b average reproducibility score (sum of reciprocal correlation coefficients for an independent component) for the correlation graph shown in a), as a function of the relative (component rank minus MSTD value for a given dataset, for stability-based ranking) or absolute (for other ranking types) component rank. It is clear that only stability-based ranking matches the reproducibility score

Article Snippet: Computational time for ICA decomposition of different orders from 2 to 100 with step 5, using compiled MATLAB fastICA implementation and stability analysis by re-computing fastICA from 100 various initial conditions.

Techniques: Expressing