Characteristic Direction
Computational Tool
FAIR Metrics
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General Information
DescriptionA geometrical multivariate approach to identify differentially expressed genes
Homepagehttp://www.maayanlab.net/CD/
Publications
The characteristic direction: a geometrical approach to identify differentially expressed genes
Neil R Clark; Kevin S Hu; Axel S Feldmann; Yan Kou; Edward Y Chen; Qiaonan Duan; Avi Ma’ayan
Background: Identifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.
Results: Here we present a new geometrical multivariate approach to identify DEG called the Characteristic Direction. We demonstrate that the Characteristic Direction method is significantly more sensitive than existing methods for identifying DEG in the context of transcription factor (TF) and drug perturbation responses over a large number of microarray experiments. We also benchmarked the Characteristic Direction method using synthetic data, as well as RNA-Seq data. A large collection of microarray expression data from TF perturbations (73 experiments) and drug perturbations (130 experiments) extracted from the Gene Expression Omnibus (GEO), as well as an RNA-Seq study that profiled genome-wide gene expression and STAT3 DNA binding in two subtypes of diffuse large B-cell Lymphoma, were used for benchmarking the method using real data. ChIP-Seq data identifying DNA binding sites of the perturbed TFs, as well as known drug targets of the perturbing drugs, were used as prior knowledge silver-standard for validation. In all cases the Characteristic Direction DEG calling method outperformed other methods. We find that when drugs are applied to cells in various contexts, the proteins that interact with the drug-targets are differentially expressed and more of the corresponding genes are discovered by the Characteristic Direction method. In addition, we show that the Characteristic Direction conceptualization can be used to perform improved gene set enrichment analyses when compared with the gene-set enrichment analysis (GSEA) and the hypergeometric test.
Conclusions: The application of the Characteristic Direction method may shed new light on relevant biological mechanisms that would have remained undiscovered by the current state-of-the-art DEG methods. The method is freely accessible via various open source code implementations using four popular programming languages: R, Python, MATLAB and Mathematica, all available at: http://www.maayanlab.net/CD.
Metrics:
Tool Parameters(required parameters are marked in bold, optional parameters in italic)
Perturbed SamplesUnique Identifiers of the perturbed samples used to compute the differential gene expression signature using Characteristic Direction.
Control SamplesUnique Identifiers of the control samples used to compute the differential gene expression signature using Characteristic Direction.
Gene Set SizeSize of the gene set extracted from the differential gene expression signature.
Gene SetDirection of the gene set extracted from the gene expression signature. Either "upregulated", "downregulated" or "combined".
Canned Analyses generated by the tool

Dataset Accession
Tool Name
Organism
Geneset
Direction
Cell Type
Disease Name
Drug Name
Hs Gene Symbol
Pert Type
Mm Gene Symbol

Enrichment analysis of genes downregulated in acute myocardial infarction
An enrichment analysis was performed on the top 500 most downregulated genes identified by...
Enrichment analysis of genes upregulated in acute myocardial infarction
An enrichment analysis was performed on the top 500 most upregulated genes identified by a...
Enrichment analysis of genes downregulated in acute myocardial infarction
An enrichment analysis was performed on the top 500 most downregulated genes identified by...
Enrichment analysis of genes upregulated in acute myocardial infarction
An enrichment analysis was performed on the top 500 most upregulated genes identified by a...
Small molecules which mimic acute myocardial infarction
The L1000 database was queried in order to identify small molecule perturbations which mim...
Small molecules which reverse acute myocardial infarction
The L1000 database was queried in order to identify small molecule perturbations which rev...
Small molecules which mimic acute myocardial infarction
The L1000 database was queried in order to identify small molecule perturbations which mim...
Small molecules which reverse acute myocardial infarction
The L1000 database was queried in order to identify small molecule perturbations which rev...
Enrichment analysis of genes dysregulated in acute myocardial infarction
An enrichment analysis was performed on the top most dyresgulated genes determined by appl...
Interaction network and enrichment analysis of genes downregulated in acute myocardial infarction
The analysis explores the gene interaction network and pathway enrichment of the top 50 mo...
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Datasets analyzed by the tool

Keyword
Tool Name

GSE50588
The Functional Consequences of Variation in Transcription Factor Binding
294
GSE6930
Cytosine arabinoside effect on Ewing's sarcoma cell line: time course and dose response
119
GSE47856
Expression data from cultured human ovarian carcinoma cell lines with and without Cisplatin treatment
119
GSE7002
Formaldehyde effect on nasal epithelium: dose response and time course
119
GSE47150
Embryonic primary cortical neuron response to knockdown of multiple autism candidate genes
112
GSE490
Pharmacogenomic effect of corticosteroid in skeletal muscle
112
GSE35366
Models of Neuronal Migration Defects: time course
112
GSE15129
Coenzyme Q10-dependent gene expression in SAMP1 mice tissues
105
GSE60413
Parkinson Phenotype in Aged PINK1-Deficient Mice Is Accompanied by Progressive Mitochondrial Dysfunction in Absence of Neurodegeneration
105
GSE18344
Nrf2-deficient lung response to cigarette smoke: dose response and time course
98
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