In this part we finish getting the top genes that the probe IDs matched a gene from 15 groups of 20 top genes in top 10 over expressed and top 10 under expressed using group avg/ group median, plus the available genes from the study's published article. We will use this to build the machine to predict the 3 classes of lymphoma within the 3 major group genes, study genes, and all genes by their subcategory within larger class, and by the new 4 classifications this study is proposing. The machine learning will be next part in Part 4.

This presentation demonstrates key statistical methods in immunology using a simulated vaccine study. It analyzes antibody response and seroconversion between two adjuvant groups (Alum vs. TLR), applying confidence intervals, hypothesis testing, and logistic regression. Data visualization with ggplot2 and plotly is used to illustrate trends across age and treatment groups. The dataset is simulated, but the analytical approach reflects real immunological research.

This presentation demonstrates simple linear regression using the mtcars dataset in R. It includes data visualization, model fitting, interpretation of results, and an interactive plot using ggplot2 and plotly.

Good project

A deep dive into the intersection of transit and probability distribution.