LCS_Win_v2.9

OUTDATED:

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### Pls find LCS v3.0 here https://zenodo.org/records/20642365

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The Logical Clustering Suite (LCS) is a MATLAB-based application for the conceptual clustering of gene expression data. Instead of grouping genes by mutual similarity (as in hierarchical clustering or k-means), LCS defines clusters by experimentally derived logical phenotypes (IPs). Each IP is a binary vector (0/1) representing the expected activation pattern across groups. Genes are assigned to the IP they best match, and their assignments are supported by multiple statistical tests and an integrated ranking system.

Advantages:

Interpretability: clusters are defined by logical patterns of 1/0 responses, which directly reflect experimental concepts.
Statistical rigor: each gene/IP assignment is evaluated with correlation distance, negative-binomial testing (BH-FDR), one-way ANOVA, fold change, and a composite Z-score.
Flexible focusing: Boolean filters and IP keep-lists allow users to restrict analyses to IPs of interest.
Comprehensive outputs: tables, heatmaps, boxplots, enrichment analysis (sGEA), and network repre-sentations.

**Update notes:

• 'Build Network' button now allows to integrate not only different sGCA sheets (by gene name overlap) into a "syntactic network" but also different sGEA sheets (by function overlap) into a "semantic network". Just multiselect the sGCA or sGEA sheets to be integrated.
• corrections to GCA stats calculation.**

Philipp

Full Changelog: LCS_Win_v1.34...LCS_Win_v2.9

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### Pls find LCS v3.0 here https://zenodo.org/records/20642365

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Logical Clustering Suite for Win PCs (v 1.34)

### Simple Gene Correlation Analysis (sGCA) by Permutated Logical Clustering

This is the standalone version of the Logical Clustering Suite (v1.34) for Win PCs. Its installation requires download of MATLAB runtime (free) as wrapper.

• The Logical Clustering Suite (LCS) clusters gene expression profiles or similar data by permutated logical gating according to their “Ideal Phenotypes” (IPs), which are defined by all possible experimental outcomes.
• Logical clustering conceptually differs from K-means-, SOM, DBSCAN and alike clustering methods that cluster gene expression profiles just according to their mutual similarity without taking the experimental groups into account.
• When just comparing two experimental groups, logical clustering simplifies to something like DESeq2 with only two possible IPs, 0 1 for upregulation & 1 0 for downregulation. Thus, methods like DESeq2, may be conceptualized as a special instance of logical clustering.
• In summary, logical clustering assumes that the locations & number of all experimentally meaningful cluster centers are given by the experimental design. Gene expression profiles more similar to one IP than to all the other IPs, form a logical cluster.
• Logical clustering by simple (=logic) gene correlation analysis (sGCA) was introduced in Ma Y, Hui KL, Gelashvili Z, Niethammer P. Oxoeicosanoid signaling mediates early antimicrobial defense in zebrafish. Cell Rep. 2023 Jan 31;42(1):111974. doi: 10.1016/j.celrep.2022.111974. Epub 2023 Jan 10. PMID: 36640321; PMCID: PMC9973399. Please cite if you are using LCS.

To help improving this application, please send feedback to sgcafeedback@gmail.com.
If you like LCS, please follow us on https://twitter.com/NiethammerLab

LCSwin v1.2

### Simple Gene Correlation Analysis (sGCA) by Permutated Logical Clustering

This is the standalone version of the Logical Clustering Suite (v1.2) for Win PCs. Its installation requires download of MATLAB runtime as wrapper.

• The Logical Clustering Suite (LCS) clusters gene expression profiles or similar data by permutated logical gating according to their “Ideal Phenotypes” (IPs), which are defined by all possible experimental outcomes.
• Logical clustering conceptually differs from K-means-, SOM, DBSCAN and alike clustering methods that cluster gene expression profiles just according to their mutual similarity without taking the experimental groups into account.
• When just comparing two experimental groups, logical clustering simplifies to something like DESeq2 with only two possible IPs, 0 1 for upregulation & 1 0 for downregulation. Thus, methods like DESeq2, may be conceptualized as a special instance of logical clustering.
• In summary, logical clustering assumes that the locations & number of all experimentally meaningful cluster centers are given by the experimental design. Gene expression profiles more similar to one IP than to all the other IPs, form a logical cluster.
• Logical clustering by simple (=logic) gene correlation analysis (sGCA) was introduced in Ma Y, Hui KL, Gelashvili Z, Niethammer P. Oxoeicosanoid signaling mediates early antimicrobial defense in zebrafish. Cell Rep. 2023 Jan 31;42(1):111974. doi: 10.1016/j.celrep.2022.111974. Epub 2023 Jan 10. PMID: 36640321; PMCID: PMC9973399. Please cite if you are using LCS.

To help improving this application, please send feedback to sgcafeedback@gmail.com.
If you like LCS, please follow us on https://twitter.com/NiethammerLab

Logical Clustering Suite v1.1 (Win PCs)

### Simple Gene Correlation Analysis (sGCA) by Permutated Logical Clustering

This is the standalone version of the Logical Clustering Suite (v1.1) for Win PCs. Its installation requires download of MATLAB runtime as wrapper.

• The Logical Clustering Suite (LCS) clusters gene expression profiles or similar data by permutated logical gating according to their “Ideal Phenotypes” (IPs), which are defined by all possible experimental outcomes.
• Logical clustering conceptually differs from K-means-, SOM, DBSCAN and alike clustering methods that cluster gene expression profiles just according to their mutual similarity without taking the experimental groups into account.
• When just comparing two experimental groups, logical clustering simplifies to something like DESeq2 with only two possible IPs, 0 1 for upregulation & 1 0 for downregulation. Thus, methods like DESeq2, may be conceptualized as a special instance of logical clustering.
• In summary, logical clustering assumes that the locations & number of all experimentally meaningful cluster centers are given by the experimental design. Gene expression profiles more similar to one IP than to all the other IPs, form a logical cluster.
• Logical clustering by simple (=logic) gene correlation analysis (sGCA) was introduced in Ma Y, Hui KL, Gelashvili Z, Niethammer P. Oxoeicosanoid signaling mediates early antimicrobial defense in zebrafish. Cell Rep. 2023 Jan 31;42(1):111974. doi: 10.1016/j.celrep.2022.111974. Epub 2023 Jan 10. PMID: 36640321; PMCID: PMC9973399. Please cite if you are using LCS.

To help improving this application, please send feedback to sgcafeedback@gmail.com.
If you like LCS, please follow us on https://twitter.com/NiethammerLab

Logical Clustering Suite v0.5beta (Win PCs)

### Simple Gene Correlation Analysis (sGCA) by Permutated Logical Clustering

This is a standalone, beta version of the Logical Clustering Suite (v0.5) for Win PCs. Its installation requires download of MATLAB runtime as wrapper.

• The Logical Clustering Suite (LCS) clusters gene expression profiles or similar data by permutated logical gating according to their “Ideal Phenotypes” (IPs), which are defined by all possible experimental outcomes.
• Logical clustering conceptually differs from K-means-, SOM, DBSCAN and alike clustering methods that cluster gene expression profiles just according to their mutual similarity without taking the experimental groups into account.
• When just comparing two experimental groups, logical clustering simplifies to something like DESeq2 with only two possible IPs, 0 1 for upregulation & 1 0 for downregulation. Thus, methods like DESeq2, may be conceptualized as a special instance of logical clustering.
• In summary, logical clustering assumes that the locations & number of all experimentally meaningful cluster centers are given by the experimental design. Gene expression profiles more similar to one IP than to all the other IPs, form a logical cluster.
• Logical clustering by simple (=logic) gene correlation analysis (sGCA) was introduced in Ma Y, Hui KL, Gelashvili Z, Niethammer P. Oxoeicosanoid signaling mediates early antimicrobial defense in zebrafish. Cell Rep. 2023 Jan 31;42(1):111974. doi: 10.1016/j.celrep.2022.111974. Epub 2023 Jan 10. PMID: 36640321; PMCID: PMC9973399. Please cite if you are using LCS.

To help improving this application, please send feedback to sgcafeedback@gmail.com.
If you like LCS, please follow us on https://twitter.com/NiethammerLab