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1 Intro

This report provides some basic description and visualization of the MetaLab function results.

Users can use this to quickly check the functional profile of the input data set.

2 Sample overview

Protein groups annotation Number (percentage)
Protein groups in your sample 19411
Protein groups with COG annotation: 19411 ( 100% )
Unique COG accessions annotated: 1876
Protein groups with NOG annotation: 19411 ( 100% )
Unique NOG accessions annotated: 5186
Protein groups with KEGG ko annotation: 19411 ( 100% )
Protein groups with GO annotation: 19411 ( 100% )
Protein groups with EC annotation: 19411 ( 100% )
Protein groups with CAZy annotation: 19411 ( 100% )
  • Grouping 1: group1; group2; group7

  • Grouping 2: control; treamtment

3 Overview with voronoi plots

About this plot
  • COG: Clusters of Orthologous Genes
  • More about COG
  • The eggNOG database is a database of biological information hosted by the EMBL. It is based on the original idea of COGs (clusters of orthologous groups) and expands that idea to non-supervised orthologous groups constructed from numerous organisms.[4] The database was created in 2007[5] and updated to version 4.5 in 2015.[1] eggNOG stands for evolutionary genealogy of genes: Non-supervised Orthologous Groups. More about NOG
  • Areas is based on the total intensity across samples
  • Graph is by voronoiTreemap

3.1 COG categories

3.2 eggNOG categories

4 Overview with composition bar plots

About this plot
  • COG = Clusters of Orthologous Genes
  • Composition calculation is on the category level, based on the total intensity, or intensity normalized to 1
  • More about COG, NOG

4.1 COG

4.1.1 Raw

4.1.2 Normalized

4.2 NOG

4.2.1 Raw

4.2.2 Normalized

5 Clustering of COGs

  • There are 1877 features.
  • With 623 100% presence across experiments (Q100).

5.1 Interactive heatmap

About this plot
  • If there are more than 100 Q100 features, only the top100 will be used for this interactive plot
  • if there are no Q100 features, top100 will be used with +1
  • data is transformed by log10
  • Scale on feature wise to be mean as 0, and sd as 1
  • plot was generated by heatmaply::heatmaply

5.2 Static Heatmap

About this plot
  • All Q100 features are used for this static plot, if there are no Q100, Q50 will be used, by +1
  • transform by log10
  • Scale on feature wise to be mean as 0, and sd as 1
  • Plot was generated by complexheatmap::pheatmap

5.3 Hierarchical cluster

About this plot
  • All intensity values +1
  • transform by log10
  • Scale on feature wise to be mean as 0, and sd as 1
  • Calculate the distance between features, using euclidean method
  • Do Hierarchical cluster analysis, with method = “complete”

5.4 PCA Contribution

  • Screeplot

  • Contribution

5.5 PCA without Grouping

5.5.1 2D plot

5.5.2 3D Scatterplot

5.6 PCA with Grouping: group1; group2; group7

5.6.1 2D plot

5.6.2 3D plot

5.6.3 All PC pairs

5.7 PCA with Grouping: control; treamtment

5.7.1 2D plot

5.7.2 3D plot

5.7.3 All PC pairs

6 Clustering of NOGs

  • There are 5187 features.
  • With 903 100% presence across experiments (Q100).

6.1 Interactive heatmap

About this plot
  • If there are more than 100 Q100 features, only the top100 will be used for this interactive plot
  • if there are no Q100 features, top100 will be used with +1
  • data is transformed by log10
  • Scale on feature wise to be mean as 0, and sd as 1
  • plot was generated by heatmaply::heatmaply

6.2 Static Heatmap

About this plot
  • All Q100 features are used for this static plot, if there are no Q100, Q50 will be used, by +1
  • transform by log10
  • Scale on feature wise to be mean as 0, and sd as 1
  • Plot was generated by complexheatmap::pheatmap

6.3 Hierarchical cluster

About this plot
  • All intensity values +1
  • transform by log10
  • Scale on feature wise to be mean as 0, and sd as 1
  • Calculate the distance between features, using euclidean method
  • Do Hierarchical cluster analysis, with method = “complete”

6.4 PCA Contribution

  • Screeplot

  • Contribution

6.5 PCA without Grouping

6.5.1 2D plot

6.5.2 3D Scatterplot

6.6 Grouping: group1; group2; group7

6.6.1 2D plot

6.6.2 3D plot

6.6.3 All PC pairs

6.7 Grouping: control; treamtment

6.7.1 2D plot

6.7.2 3D plot

6.7.3 All PC pairs

7 Binary comparsion

7.1 COGs

7.1.1 meta1

7.1.2 meta2

7.2 NOGs

7.2.1 meta1

7.2.2 meta2