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1 Intro

This report provides some basic description of the protein(groups) identification.

Users can use this to quickly check the overal quality of the experiment

2 Quick Message

  • Number of contaminant: 0

  • Number of reversed: 0

  • Number of qualified proteingroups: 13

  • Number of experiment: 13

  • All experiments: F01_Fraction1, F01_Fraction2, F01_Fraction3, F01_Fraction4, F02_FractionCyto, F02_FractionEnv, F04, F05, F06, F07_Fraction1, F07_Fraction2, F07_Fraction3, F07_Fraction4

  • Grouping 1: group1; group2; group7

  • Grouping 2: control; treamtment

3 Protein identification profiling

3.1 Intensity

About this plot
  • A distribution of the density across samples/experiment
    • A direct and rough evidence to tell if needed to normalize across samples

3.2 Unique peptide number

About this plot
  • The more unique peptide per protein, the better quality

3.3 Protein Score distribution

About this plot
  • The higher score the better quality
    • Different search engine usually have different score range

3.4 Sparsity

Columns starting with “LFQ_intensity_” will be selected if exist (meaning LFQ option was checked for label free quantification, which is deliberately processed by Maxquant already), otherwise Intensity_ columns will be used instead for protein experession.

Figure shows the number of peptide in total with more than N presence, which helps to set the presence cutoff

4 Heatmap and Hierarchical clustering

  • There are 18167 features.
  • With 1510 100% presence across experiments (Q100).

4.1 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

4.2 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

4.3 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 PCA Analysis

5.1 PCA Contribution

  • Screeplot

  • Contribution

5.2 PCA without Grouping

5.2.1 2D plot

5.2.2 3D Scatterplot

5.3 Grouping: group1; group2; group7

5.3.1 2D plot

5.3.2 3D plot

5.3.3 All PC pairs

5.4 Grouping: control; treamtment

5.4.1 2D plot

5.4.2 3D plot

5.4.3 All PC pairs

6 Differential Analysis

About this section
  • t-test will be performed for two samples setting, while annova will be performed for 3 and more samples
  • Note that this Differential Analysis is performed on only Q100 peptides for quick profiling
  • Only do analysis 1 with more than (including) two groups, 2: each group has replicates.

6.1 Grouping: group1; group2; group7

  • test

6.2 Grouping: control; treamtment

  • test