Participant will understand how the choice of Genes with high confidence is made, learn how to make comparisons with results of old analyses or analyses by colleagues and understand the model that finds the most significant patterns in experiments

Session 1 - Fundamental Principles of Statistics; purpose of statistics, sampling and variable types

Session 2 - Numerical Descriptive Measures and Graphical Descriptive Techniques; Scattterplots.

Session 3 - Confidence Interval Estimation and Sample Size Determination; Statistical Power Analysis and Standard Error Explained.

Session 4 - Logic of Hypothesis Testing; t-test, Type I&II errors and p-values; Statistical Significance and Practical Importance.

Session 5 - Data Normalization and Data Pre-processing; Pearson Correlation and Other Similarity Measures; detecting skew-ness in your data.

Session 6 - Testing for Mean Differences Between Groups and Comparing Groups; Chi-Square test of independence.

Session 7 - Clustering; Gene trees (hierarchical dendrograms); K-means (non-hierarchical)

Session 8 - Principal Component Analysis; Find the Most Significant Patterns in Experiments.

Session 9 - A Look Forward: Classification; discriminant analysis and Bayesian models

Duration: 3 days

This course is designed for researchers joining the growing community using statistics in genetic and genomic research in support of good decision making.

Interested in our training? Please email the

Training Department
XLSolutions Corporation
sue@xlsolutions-corp.com