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1. Inputs
Name
*
Description
Primary dataset
*
Category column
Select Primary dataset first
Map
*
Secondary dataset
2. Pre-processing
Abundance threshold
Percent
Absolute value
Presence threshold
Percent
Absolute value
Category binning levels
Select Primary dataset first
Category binning function
Sum values within categories
Average values within categories
3. Transformations
Rarefaction (Primary dataset)
Sampling depth
N samplings
Transformation (Primary dataset)
No transformation
log2 of count per million
Log2
Percent
Rarefaction (Secondary dataset)
Sampling depth
N samplings
Transformation (Secondary dataset)
No transformation
log2 of count per million
Log2
Percent
4. Analysis
Experimental design
Basic
Advanced
Model
Select Map first
Statistics
ANOVA
t-test
Paired t-test
Kruskal-Wallis rank sum test
Friedman test
Wilcoxon rank sum test
Wilcoxon signed rank test
Model
Proportions
Diversity
Metric
Richness
Shannon Diversity Index
Simpson Diversity Index
inverse Simpson Diversity Index
Gini coefficient
chao1 estimator
Ricci-Schutz coefficient
Atkinson measure
Theil entropy measure
Kolm measure
Coefficient of variation
Compare groups
perMANOVA
Distance metric
Jaccard Index
Bray-Curtis Distance
Euclidean Distance
Manhattan Distance
Spearman Correlation Coefficient
Pearson Correlation Coefficient
Canberra Distance
Kulczynski Similarity Measure
Gower Similarity Coefficient
Morisita Overlap Index
Horn-Morisita Index
Mountford Index
Raup-Crick dissimilarity
Binomial distance
Chao Index
Model
Strata
PCA
PCoA
Distance metric
Jaccard Index
Bray-Curtis Distance
Euclidean Distance
Manhattan Distance
Spearman Correlation Coefficient
Pearson Correlation Coefficient
Canberra Distance
Kulczynski Similarity Measure
Gower Similarity Coefficient
Morisita Overlap Index
Horn-Morisita Index
Mountford Index
Raup-Crick dissimilarity
Binomial distance
Chao Index
Heatmap
Changes
Correlation network
Correlation method
Spearman Correlation Coefficient
Pearson Correlation Coefficient
Clustering
Algorithm
k-medoids algorithm
k-medoids algorithm run on Bray-Curtis dissimilarity
k-medoids algorithm run on Jaccard distance
k-means algorithm
Similarity network
Metric for primary dataset
Jaccard Index
Bray-Curtis Distance
Euclidean Distance
Manhattan Distance
Spearman Correlation Coefficient
Pearson Correlation Coefficient
Canberra Distance
Kulczynski Similarity Measure
Gower Similarity Coefficient
Morisita Overlap Index
Horn-Morisita Index
Mountford Index
Raup-Crick dissimilarity
Binomial distance
Chao Index
Metric for secondary dataset
Jaccard Index
Bray-Curtis Distance
Euclidean Distance
Manhattan Distance
Spearman Correlation Coefficient
Pearson Correlation Coefficient
Canberra Distance
Kulczynski Similarity Measure
Gower Similarity Coefficient
Morisita Overlap Index
Horn-Morisita Index
Mountford Index
Raup-Crick dissimilarity
Binomial distance
Chao Index
Clustering algorithm
Short random walks algorithm
Greedy optimization of modularity algorithm
Multi-level optimization of modularity algorithm
Label propagation method of Raghavan et al.
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