1 Sample Information

Analysis from the results of a label free mass spectrometry experiment on paraffin embedded samples stratified as follows:

1.1 Sample groups

Sample Etiology Cellular Composition
BH-321-P1 LAA RBC
GOTH-168-P5 LAA PLT/FIB
GOTH-172-P5 LAA PLT/FIB
ATH-012-P2 LAA PLT/FIB
ATH-011-P1 LAA RBC
BH-316-P1 LAA RBC
NICN-213-P2 LAA PLT/FIB
BH-323-P3 LAA RBC
NICN-167-P2 LAA PLT/FIB
NICN-196-P1 LAA PLT/FIB
BH-215-P2* LAA PLT/FIB
BH-308-P2 CEE PLT/FIB
GOTH-158-P2 CEE PLT/FIB
BH-287-P1 CEE RBC
BH-278-P3 CEE PLT/FIB
BH-326-P2 CEE PLT/FIB
BH-364-P2 CEE PLT/FIB
NICN-193-P4 CEE PLT/FIB
ATH-018-P1 CEE PLT/FIB
NICN-198-P1 CEE PLT/FIB

2 Protein counts per sample

The plot below demonstrates no relationship between the groups and the protein content of the samples.

3 Venn diagrams

The Venn Diagram generated using the forth data set shows that 77 proteins are overlapping between the CE and LAA Groups - and this intersection is used for comparison between groups, 33 proteins are unique to the LAA samples, and 34 proteins are unique to CE samples.

# Data Processing

4 Principal Component Analysis

After processing the data with log-2 transformation and normalization, I performed an exploratory data analysis with principal component analysis. The results show that there is no clear separation between groups.

5 Pearson correlation

The correlation matrices with the co-abundant proteins were used to create the adjacency matrices necessary for the network analysis. The correlation analysis was done using the following data sets:

  1. LAA samples with proteins exclusive to the LAA group (33 proteins);
  2. CE samples with proteins exclusive to the CE group (34 proteins);

5.1 LAA Samples

5.2 CE Samples

6 Network Analysis

Using the correlation matrices above to extract information on the abundancy profile of the highly correlated proteins with a threshold of +/- 0.80, the network analysis was carried out and resulted in the graphs below. The node size is proportional with the degree (how connected the protein is), the red edges represent positive correlation and blue edges represent negative correlation. The optimal community structure was calculated for the graph, in using the maximal modularity score.

6.1 LAA Samples





6.2 CE Samples







7 Differential Analysis

Here I used a linear model approach to assess differential abundance/expression between the two groups - this analysis resulted in no differentially abundant proteins.