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Data standardization
Data normalization
sum, mean, median, etc. standardize sample data to eliminate background errors
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Data quality control
Principal component analysis
calculation of the spatial distribution of samples by data dimensionality reduction
Cluster analysis
calculation of distance between samples by correlation algorithm such as pearson
Heat map analysis
partitioning sample expression values by different color blocks
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Difference calculation
Statistical T test
repeated samples of each group more than 3 times for independent sample t test
Limma algorithm
linear models and differential expression for microarray data
Deseq2 algorithm
Build a model for integer data to filter for differences
Paired T test
sample paired t test for different treatments of the same subject
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Multiple sets of differences analysis
Venn diagram
analysis of intersections, unions, specificities, etc. between multiple sets of data sets
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Functional Analysis
Biofunctional enrichment analysis(free)
GO Term, KEGG pathway significance by hypergeometric algorithm analysis
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Co-expression analysis
WGCNA Threshold Calculation
calculates the WGCNA model to meet the threshold-free threshold
WGCNA clustering
WGCNA model different color modules for clustering
WGCNA module merge
WGCNA model similar color module for merging
Correlation analysis
Pearson et al. correlation algorithm calculates the correlation of expression of different markers
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Machine learning
LASSO analysis
maximum likelihood fitting fits linear models to screen for signatures
Logistic Regression Analysis
Linear Logistic Regression Model Screening for Feature Markers
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Data standardization
Data normalization
sum, mean, median, etc. standardize sample data to eliminate background errors
-
Data quality control
Principal component analysis
calculation of the spatial distribution of samples by data dimensionality reduction
Cluster analysis
calculation of distance between samples by correlation algorithm such as pearson
Heat map analysis
partitioning sample expression values by different color blocks
-
Difference calculation
Statistical T test
repeated samples of each group more than 3 times for independent sample t test
Limma algorithm
linear models and differential expression for microarray data
Deseq2 algorithm
Build a model for integer data to filter for differences
Paired T test
sample paired t test for different treatments of the same subject
-
Multiple sets of differences analysis
Venn diagram
analysis of intersections, unions, specificities, etc. between multiple sets of data sets
-
Functional Analysis
Biofunctional enrichment analysis(free)
GO Term, KEGG pathway significance by hypergeometric algorithm analysis
-
Co-expression analysis
WGCNA Threshold Calculation
calculates the WGCNA model to meet the threshold-free threshold
WGCNA clustering
WGCNA model different color modules for clustering
WGCNA module merge
WGCNA model similar color module for merging
Correlation analysis
Pearson et al. correlation algorithm calculates the correlation of expression of different markers
-
Machine learning
LASSO analysis
maximum likelihood fitting fits linear models to screen for signatures
Logistic Regression Analysis
Linear Logistic Regression Model Screening for Feature Markers
PLSDA
Partial least squares discriminant analysis is a multivariate statistical analysis method for discriminant analysis based on the value of several variables observed or measured to determine how the subject is classified.
OPLSDA
Multivariate statistical analysis method for discriminant analysis
GEO2Matrix
Convert Microarray data in SOFT format from GEO database To Matrix
GEOFormat
Probe conversion gene name in GEO expression matrix
DataMerge2
Cutoff and Merge multiple Samples File To Matrix
TCGA Survival plot
Survival analysis examines the impact of gene expression on prognosis
TCGA gene search
Study on survival analysis and mutation analysis of genes in TCGA pancreatic cance
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