This repository analyzes data from collected from two subjects over two time points. This repository also compares the data to data from two CLL proteomics papers.
Our experimental data was loaded using the longitudinal CLL package. Data was filtered to include relevant cell types and proteins seen in at least half the replicates. The data was normalized by log 2 transformation. T tests were run on the data and upregualted proteins were identified. The seaborn library allowed for creation of a volcano plot. Data was plotted on a volcano plot with upregulated proteins in orange.
Relevant supplementary tables from Mayer et al. and Johnston et al. were downloaded. Data was parsed and sorted into differential and total proteins based on the qualifications from each paper. The matplotlib_venn library allowed creation of a venn diagram. Venn diagrams were created using total proteomic data and differential proteomic data from the two papers. The seaborn library was used to create a scatterplot. P-values for differential proteins in each paper were plotted on a scatterplot.
Relevant supplementary tables from Mayer et al. and Johnston et al. were downloaded. Data was parsed and sorted into differential and total proteins based on the qualifications from each paper. Our experimental data was loaded using the longitudinal CLL package. The matplotlib_venn library allowed creation of a venn diagram. A venn diagrams was created using total proteomic data from the two papers and our total proteomic data. We calculated the ratio of the differential proteins we identified to the total differnetial proteins of the two studies.
Our experimental data was loaded using the longitudinal CLL package. Data was then saved as an excel document.
Our experimental data was loaded using the longitudinal CLL package. Data was filtered to include B and T cells and proteins seen in at least half the replicates. The data was normalized by log 2 transformation. T tests were run on the data and upregualted proteins were identified. Using GProfiler, a KEGG analysis was run and the output was saved as a table.
Relevant supplementary tables from Mayer et al. and Johnston et al. were downloaded. Data was parsed and sorted into upregulated and downregulated proteins based on the qualifications from each paper. Using GProfiler we determined the KEGG pathways for each paper's upregulated and downregulated proteins.
Our experimental data was loaded using the longitudinal CLL package. The relevant supplementary table from Mayer et al. was downloaded. The data from Mayer et al. was filtered to only include healthy subjects. Abundance values were averaged for the 3 healthy subjects. The table was sorted based on the average abundance value. The sorted table was split into 4 dataframes based to separate the data into quartiles. We then found the intersection for each quartile and the ratio of common protiens to the total number of proteins in the quartile.