Bioinfo/Stat545/Biostat646 Lab Notes
Dongxiao Zhu
zhud@umich.edu Copyright©2005
http://www-personal.umich.edu/~zhud
Lab I. R/Bioconductor Basics
1. R/Bioconductor Information
• R information and download website
– http://cran.r-project.org
• Bioconductor information and download website
– http://www.bioconductor.org/
• R references and books
– http://www.r-project.org/doc/bib/Rpublications.html
• R packages for gene expression analysis
– http://www.stat.unimuenchen.de/~strimmer/notes/rexpress.html
2. R language basics
• Basic data types
– Scalars: character, integer, float, factor,
complex
– Vectors: vector, list
– Special: formula
• Complex Datatypes
– Data Objects: matrix, data.frame
– Complex Objects: S3 classes, S4 classes
• Check datatypes using function class()
• Assignment Operators: <-, =, or function assign(var,
data)
• Mathematical Operators: +,-,/,*
• Vector Operators: *, %*%, %o%
• Logical Operators: &, |, !
• Boolean Operators: ==, &&, ||, !=
3. Vectors and Matrices
• Vector construction is done with c( )
mv <- c(1,1,8,3,1,6,4,9,8)
mv
[1] 1 1 8 3 1 6 4 9 8
• Vector multiplication
– Element-wise multiplication “*”
• Try a <- c(1,2,1) , b <- c(2,0,1) , a*b
– Inner product “%*%” and outer product “%o%”
• Try a%*%b and a%o%b, what’s the difference?
• Useful functions for vector arithmetic
ab <- c(1:9) #initialize a vector
mean(ab) #5
length(ab) #9
compare “sum(ab)” and “sum(ab>1)” #45, 8
compare “var(ab)” and “sd(ab)” #7.5, 2.738613
which(ab > 8) #9
any(ab>1) #T
all(ab>1) #F
• Matrix de novo construction
C <- matrix(NA, 3, 4)
• Matrix construction from several vectors with cbind( ),
or rbind()
mm <- cbind(c(1,1,8), c(3,1,6), c(4,9,8), c(2,1,3))
mm
[,1] [,2] [,3] [,4]
[1,]
1
3
4
2
[2,]
1
1
9
1
[3,]
8
6
8
3
• Matrix construction from one vector with array() or
with matrix()
array(mv, dim = c(3,3))
matrix(1:12, nrow=3, byrow=T)
4. Matrix Computation
• Matrix multiplication
– If not square matrices, transpose is needed.
• t(mm)
Matrix Transpose
• mm%*%mm
• Matrix decomposition
– chol() #check spd simultaneously, square matrix
– svd() #singular value decomposition, rectangular matrix
– schur() #schur decomposition, square matrix
5. Factors and Lists
•
Categorical variables are specified as factor in R. For the
character variable, R treats as factor automatically. For numeric
value, you have to let R know.
pain <- c(0,3,2,2,1)
fpain <- factor(pain, levels = 0:3)
levels(fpain) <- c(“none”, “mild”, “medium”, “severe”)
fpain #factor object
as.numeric(fpain)
levels(fpain)
•
List is a combination of objects in R
Use “$” to access objects in a list
Use function list() and unlist(), see below.
6. R functions
• R package contains a list of related functions
• All functions return something, usually the result of
the last statement in the function
• Pass-by-value paradigm
• A very simple function that returns single object
power <- function(x,y)
x^y #the single object returned
power(2,3) #call function
• If more than one object is return, use list()
xy <- function(x,y)
list(o1 =x^y, o2=x+y, o3=x*y, o4=x-y)
#four objects are returned
Os <- xy(2,3)
names(Os) #see the contents of the list
Os$o2
#access each object
7. Basic I/O
• R manual file import/export manual
• Import from delimited table
– Function “read.table()” or “read.delim()” or
“scan” or “read.csv”, “read.csv2” or
“read.delim2”.
– “read.delim()” is often more useful for massy
dataset
• Import from Software/DBMS
• Import from internet
• File export
– Function “write.table()”
8. An I/O example
• Download “gal.tsv” from
– http://www-personal.umich.edu/~zhud/teach.htm
• Use the following command to input data and output
data.
gal <- read.table(“gal.tsv”, h = T, row.names = 1)
class(gal)
dim(gal)
write.table(gal, sep = “\t”, file = “galout.tsv”)
9. Data visualization
#boxplot to check data distributions
boxplot(gal, xlab = “different experimental conditions”, ylab =
"log2 ratio of cy3/cy5 intensities", main = “galactose gene
expression data”)
#plot one gene expression profile(s)
plot(as.numeric(gal[1,]), type = "l", col = 2, lwd = 2, lty = 1,
xlab = "different experimental conditions", ylab = "log2 ratio of
cy3/cy5 intensities", main = "GAL7 gene expression profile")
#plot many gene expression profiles
plot(as.numeric(gal[1,]), type = "n", col = 2, lwd = 2, lty = 1,
xlab = "different experimental conditions", ylab = "log2 ratio of
cy3/cy5 intensities", main = "All gene expression profile")
for(i in 1:nrow(gal))
points(as.numeric(gal[i,]), type = “l”, col = i, lwd = 2, lty = i)
10. Indexing and conditional selection
• Can be applied to both matrix and data.frame objects
gal1<- gal[1:10,] #extract first ten rows
gal2<- gal[,11:20]#extract right half matrix
• Conditional selection
idx1 <- apply(gal, 1, sd)
gal3 <- gal[idx1 > 0.1,] #extract only specific rows
#the code remove some values in a matrix using a “filter”
idx2 <- apply(gal, c(1,2), function(x) (abs(x)>0.5))
idx2*t(gal)
#a more complex example, find genes that are only on in one
#condition/time point
idx3 <- apply(gal, 1, function(x) abs((max(x[-which(x
== max(x))]) - x[which(x == max(x))])) > 0.5)
11. Sort/Order and Missing Value
• sort() and order()
#only sort the first column, other columns does not change
sort(as.numeric(gal[1,]))
#the code below sort genes according to its fold change
fc <- apply(gal, 1, function(x)
max(abs(x))/min(abs(x)))
ggal <- cbind(gal, fc)
gggal <- ggal[order(ggal[,21]), decreasing = T]
• Missing value is represented as NA
– Initialize matrix with NA – good habit
– NA op anything = = NA
– NA related functions: is.na(), na.omit()
– NA imputation methods, such as k-nearest neighbor,
maximum liklihood.
12. Bioconductor
• Bioconductor tutorial
– http://www.bioconductor.org/workshop.html
• Bioconductor packages
– Preprocessing for Affymetrix data
• affy, vsn.
– Preprocessing for cDNA data
• marrayClasses, marrayInput, marrayNorm,,
marrayPlots, vsn, sma
– Differential expression
• edd, genefilter, multtest, ROC
– Clustering and classification. e1071
– Network inference. GeneTS, GeneNT
13. How to use?
• Short courses
• Vignettes
– Problem-oriented “How-To”s
• R demos
– e.g. demo(marrayPlots)
• R help system
– interactive with browser or printable manuals;
– detailed description of functions and examples;
– e.g. help(maNorm), ? marrayLayout.
• Search Mailing list archives; Google
• Post to mailing list
• All on WWW
References:
William N. Venables and Brian D. Ripley. Modern Applied Statistics with
S. Fourth Edition. Springer, 2002. ISBN 0-387-95457-0
William N. Venables and Brian D. Ripley. S Programming. Springer, 2000.
ISBN 0-387-98966-8.
Peter Dalgaard. Introductory Statistics with R. Springer, 2002. ISBN 0387-95475-9.