Make sure to not delete or alter the questions Follow the instructions on the code and answer the questions. Use the link of lab 4, lab 6, Lab 7, Lab 8, Lab 9, and exam 2 as a reference for what was wrong before. Give me the Rcode when done
Exam III Outline – Gene Expression Analysis
1. Different ways of coding explanatory variables in linear models
2. Relationship between two-sample t-test and a linear model with coded
variables using treatment contrasts (x = 0 and x = 1).
3. RNA-seq biology (read counts)
4. Processing of RNA-seq data: FPKM/RPKM, TMM, and TMM
5. Retrieving data using the UCSCXenaTools R package
6. Understanding the format of RNA-seq data and accessing clinical
(phenotype) variables
7. Constructing side-by-side boxplots comparing the gene expression across
groups
8. Identifying differentially expressed probes/genes using the limma package
and understanding the false discovery rate (FDR)
9. Generating heatmaps
10. Converting a probe name to the corresponding gene symbol and vice versa
using the probe map data provided by UCSC Xena
11. Classification using k-nearest neighbors (knn), including leave-one-out crossvalidation, optimization, and making predictions in a test dataset.
12. Perform a gene set enrichment analysis using DAVID, based on a list of genes
DescriptionIn this final assignment, the students will demonstrate their ability to apply two ma
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1 Project 1 Introduction - the SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of
1 Project 2 Introduction - the SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of