1 Sample Information

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

2 Coverage Analysis

On the previously conducted analysis, we could see that filtering the data based on the coverage score was not a good idea as the majority of the proteins had low coverage values. The same analysis was conducted here and the summary statistics of the coverage score for each sample is shown on the table below, and based on that and the plots we can see a similar behaviour regarding the coverage.

Fib1 Fib2 Fib3 Fib4 Fib5 RBC1 RBC2 RBC3 RBC4 RBC5
Min. 0.18000 0.07000 0.0600 0.28000 0.19000 0.19000 0.04000 0.08000 0.30000 0.13000
1st Qu. 4.73000 4.53500 4.3800 4.38000 4.30000 5.03000 5.01000 4.71000 5.61750 5.02000
Median 10.17000 10.07500 9.9800 9.52500 9.42000 10.94000 11.69000 10.95000 11.06500 10.90000
Mean 15.71651 15.68055 15.3679 15.47617 14.15617 16.54819 15.68757 15.77537 16.03192 15.75879
3rd Qu. 21.55000 23.03000 20.6500 21.46000 19.35000 22.54000 20.87000 21.42250 21.89000 20.62000
Max. 97.28000 91.55000 94.3700 93.88000 94.37000 100.00000 100.00000 100.00000 100.00000 100.00000



3 Overlapping proteins

The Venn Diagram shows that 101 proteins are overlapping between the RBC (Set_1) and FIB (Set_2) Groups - and this intersection is used for comparison between groups, 161 proteins are common to the RBC samples, and 216 proteins are common to the FIB samples.

3.1 Protein datasets

The correlation matrices with the co-abundant proteins were used to create the adjacency matrices necessary for the network analysis. Here we have three distinct datasets: i) Fibrin samples with overlapping proteins within the group, ii) Red Blood cells samples with with overlapping proteins within the group, and iii) All samples with the 101 proteins that are common to all samples.

4 RBC Analysis

4.1 Pearson correlation





Using the correlation matrix above to extract information on the abundancy profile of the top 50 highly correlated proteins with a threshold of +/- 0.80, the network analysis was carried out and resulted in the graph 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.This analysis resulted in the 4 communities represented as the node colours and shown in the tables below.

4.2 Network Analysis



4.3 Pathway Enrichment Analysis

Pathway enrichment analysis was conducted on communities 1, 2 and 4 using the InterMineR R package. Here the pathways were tested for over-representation in each of the communities relative to what is expected by chance and a p-value is computed for each pathway. The plots below represent the top 10 enriched pathways for the aforementioned communities - you can hover the bars for p-value information.









Community 3 resulted in no significant enriched pathways.

5 FIB Analysis

5.1 Pearson correlation analysis





5.2 Network analysis

The same method for constructing the co-expression network used on the RBC samples was applied to the FIB dataset here, in this case using the 216 overlapping proteins. The optimal community analysis resulted in four communities that were analysed for pathway enrichment below.

5.3 Pathway enrichment analysis





6 Differential Analysis

Here I used a linear model approach to assess differential abundance/expression between the two groups - this analysis resulted in 58 differentially abundant proteins. The table below shows the metrics of the top-ranked proteins from the linear model fit.

6.1 Co-expression network

The differentially abundant protein scores were used to perform the coexpression analysis on the comparison between groups. Here the node colours represent the logFC values, and only edges with correlation greater than +/- 0.80 were used. An adjusted p-value of 0.05 was selected for assuming significance.

## Step 1 ...computing correlation
## Step 2 ...computing null distribution
## ================================================================================
## Step 3 ...computing probs
## Step 4 ...adjusting pvals

6.2 Pathway Enrichment Analysis

Pathway enrichment analysis were conducted on statistically significant positive and negative associated proteins.