Biol20N02 2016

Analysis of Biological Data (BIOL 20N02, Spring 2015) Instructor: Dr Weigang Qiu, Associate Professor, Department of Biological Sciences Room: 1001B HN (North Building, 10th Floor, Mac Computer Lab) Hours: Tuesdays 10-1 Office Hours: Belfer Research Building (Google Map) BB-402; Wed 5-7 pm or by appointment Course Website: http://diverge.hunter.cuny.edu/labwiki/Biol20N2_2016

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Course Description

With rapid accumulation of genome sequences and digitalized health data, biomedicine is becoming a data-intensive science. This course is a hands-on, computer-based workshop on how to visualize and analyze large quantities of biological data. The course introduces R, a modern statistical computing language and platform. Students will learn to use R to make scatter plots, bar plots, box plots, and other commonly used data-visualization techniques. The course will review statistical methods including hypothesis testing, analysis of frequencies, and correlation analysis. Student will apply these methods to the analysis of genomic and health data such as whole-genome gene expressions and SNP (single-nucleotide polymorphism) frequencies.

This 3-credit experimental course fulfills elective requirements for Biology Major I. Hunter pre-requisites are BIOL100, BIOL102 and STAT113.

Learning Goals

• Be able to use R as a plotting tool to visualize large-scale biological data sets
• Be able to use R as a statistical tool to summarize data and make biological inferences
• Be able to use R as a programming language to automate data analysis

Textbooks

• R Studio (Required): Learning RStudio for R Statistical Computing
• Digital textbook (Required): Data Analysis for the Life Sciences

Exams & Grading

• Attendance (or a note in case of absence) is required
• In-Class Exercises (50 pts).
• Assignments. All assignments should be handed in as hard copies only. Email submission will not be accepted. Late submissions will receive 10% deduction (of the total grade) per day.
• Three Mid-term Exams (3 X 30 pts each = 90 pts)
• Comprehensive Final Exam (50 pts)
• Bonus for active participation in classroom discussions

Course Outline

Feb 2. Introduction & tutorials for R/R studio

1. Course overview
2. Install R & RStudio on your home computers (Chapter 1. pg. 9)
3. Tutorial 1: First R Session (pg. 12)
   1. Create a new project by navigating: File | New Project | New Directory. Name it project file "Abalone"
   2. Import abalone data set: Tools | Import DataSet | From Web URL, copy & paste this address: http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data
   3. Assign column names: colnames(abalone) <- c("Sex", "Length", "Diameter", "Height", "Whole_Weight", "Shucked_weight", "Viscera_weight", "Shell_weight", "Rings")
   4. Save data into a file: write.csv(abalone, "abalone.csv", row.names = FALSE)
   5. Create a new R script: File | New | R script. Type the following commands:abalone <- read.csv("abalone")
   6. Save as "abalone.R" using File | Save
   7. Execute R script: source("abalone.R")
   8. Install the notebook package: install.packages("knitr")
   9. Compile a Notebook: File | Compile Notebook | HTML | Open in Browser
4. Tutorial 2. Writing R Scripts (Chapter 2. pg. 21)
5. Tutorial 3. Vector

Assignment #1. Due 2/16, Tuesday (Finalized)

(2 pts) Install R & R Studio on your own computer (4 pts) Reproduce the "abalone" project (follow steps in Tutorial 1). Save & Print your notebook (consists of commands and a boxplot). If "Compile notebook" doesn't work, print a copy of your commands and export the boxplot in 4X6 PDF (print & submit) (4 pts) Vector operations. Create a new vector object of abalone height: ht <- abalone$Height Show commands for extracting the first item, first 10 items, items 20 through 30, the 1st, 2nd, and 5th items First, obtain the indices for items less than 0.5 using the which() function. Save as a new vector called "ht.idx". Then, obtain the actual items by combining the "ht" and "ht.idx" vectors. Apply the following functions: range(), min(), max(), mean(), var(). [Hint: use help(var), help(min) for help]