Microbial Informatics

• Start thinking about your project for the first half of the semester
  • Emphasis on data analysis
  • Due 10/24/2104 (friday)
  • Feel free to come to office hours to discuss project ideas
  • I have some ideas for microbial ecology analysis projects
• When you upload your assignments, upload the README.Rmd and README.md files generated by RStudio/knitr

• Everything in R is some form of a vector
• Vectors
• List
• Matrix
• Table
• Data table
• Factors

• Appreciate the diversity of different data structures
• Understand how to calculate descriptive variables
• Begin to develop data visualization skills in R

• Defining categorical variables
• In a genome we might think of the forward/reverse orientation, reading frame, dna/protein sequence designation, or annotation category as categorical variables.
  
• Create factors

metadata <- read.table(file="wild.metadata.txt", header=T)
rownames(metadata) <- metadata$Group
metadata <- metadata[,-1]

factor(metadata$ET)
metadata$ET <-factor(metadata$ET)
summary(metadata)
levels(metadata$ET)

• What other variables here would be a factor?

• Similar to data fames, but not necessarily read across rows and not all variables have the same length
• Could hold a genome's data within a list:
  • name: Character with organism name
  • genome.size: Number with number of bases
  • start.pos: Vector of start positions for each gene
  • end.pos: Vector of end positions for each gene
  • gene.name: Name of each gene
  • hydrolases: Names of genes that are hydrolases
• Allow one to create complex data structures
• We'll use these only in passing

• Let's get the mean weight for each sex of mouse

aggregate(metadata$Weight, by=metadata$Sex, mean)
aggregate(metadata$Weight, by=list(metadata$Sex), mean)
sex.weight <- aggregate(metadata$Weight, by=list(metadata$Sex), mean)
sex.weight$x

• Let's get the mean weight for each sex and species of mouse

aggregate(metadata$Weight, by=list(metadata$Sex, metadata$SP), mean)

• With large datasets it's easy to get lost and not know what the data look like
• Need to devlop strategies to get a handle on the data
• Three approaches:
  • Visualize data tables
  • Summary statistics
  • Visualize data

• How do we get the column/variable names?
• How do we know the data type for each column?
• Which columns in metadata should be
  
  • Continuous?
  • Categorical?

• Homework asked you to print out the first 5 lines of a data table
• There ares everal ways to do this...

datatable[c(1,2,3,4,5),]    # explicit method   
datatable[1:5,]             # bracket method
head(datatable, n=5)                # first N lines
tail(datatable, n=5)                # last N lines

What types of data structures do these output?

summary(metadata)           # how do we get a summary of the overall metadata table?
summary(metadata$Weight)    # how do we get a summary of the weights?
median(metadata$Weight)     # median - what is a median?
w.summary <- summary(metadata$Weight)
w.summary["Median"]

sd(metadata$Weight)         # standard deviation
var(metadata$Weight)        # variance
min(metadata$Weight)        # minimum weight
max(metadata$Weight)        # maximum weight
quantile(metadata$Weight)   # weight distribution by quantiles                          
quantile(metadata$Weight, probs=seq(0,1,0.1))   # weight distribution by deciles

aggregate(metadata$Weight, by=list(metadata$Sex), mean)
aggregate(Weight~Sex, data=metadata, mean)  #Notice the difference in output

aggregate(metadata$Weight, by=list(metadata$Sex, metadata$SP), mean)
aggregate(Weight~Sex+SP, data=metadata, mean)

aggregate(Weight~Sex+SP, data=metadata, summary)

w.sex_sp <- aggregate(Weight~Sex+SP, data=metadata, mean)   # what type of variable is w.sex_sp?

• This is a huge area of explorataion
• R is tremendously powerful for generating plots and data visualizaiton tools
• Can generally tell someone used MS Excel by how bad the plots look
• Can certainly generate crap in R, but upside is greater
• Numerous packages available, but we will focus on base package until the end of the semester:
  • Lattice
  • ggplot2
  • rgl

• Start working on new assignment that will be posted this weekend
• Assignment due Friday
• Read Introduction to Statistics with R (Chapter 3 and 4)