1 Sample Information

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

1.1 Second Batch of Samples

  • Red Blood Cells rich clot analogues (RBC): n = 5
  • Fibrin rich clot analogues (FIB): n = 5

2 Coverage Analysis

The coverage analysis result was conducted here on both data sets, and the summary statistics of the coverage score for each sample is shown on the tables below, and based on that and the plots we can see a similar behaviour regarding the coverage.

Second batch of samples
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

The Venn Diagram generated using the second data set shows that 101 proteins are overlapping between the RBC and FIB Groups - and this intersection is used for comparison between groups, 60 proteins are unique to the RBC samples, and 115 proteins are unique to the FIB samples.

3 RBC Analysis

3.1 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 six communities represented as the node colours and shown in the table below.

3.2 Network Analysis



4 FIB Analysis

4.1 Pearson correlation

4.2 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 115 proteins unique to FIB samples from the second batch. The optimal community analysis resulted in three communities listed in the table below.

5 Differential Analysis

Here I used a linear model approach to assess differential abundance/expression between the two groups - this analysis resulted in 40 differentially abundant proteins. The tables below show the metrics of the top-ranked proteins from the linear model fit. The data was pre-processed with log-transformation and quantile normalisation to ensure that the expression distributions of each sample are similar across the entire experiment and not skewed.

6 Common proteins network

The differentially abundant protein scores were used to perform the coexpression analysis on the comparison between groups. The heatmaps below show differentially abundant proteins and resulted in clusters that are correspondent with the sample groups. A coexpression matrix was generated and used to construct the co-expression network. The node colours are based on the optimal community analysis, which is shown below in the tables. Only edges with a correlation greater than +/- 0.80 were used to plot the graphs.

7 Pathway Enrichment Analysis

Pathway enrichment analysis were conducted on differentially abundant proteins related RBC and FIB using the InterMineR R package. Here the pathways were tested for over-representation in each of the proteins with fold change related with RBC and FIB 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.