Differential expression data

Differential Expression Analysis with limma in R

John Blischak

Instructor

The experimental data

Study of breast cancer
- Bioconductor package "breastCancerVDX"
- Published in Wang et al., 2005 and Minn et al., 2007
- 344 patients: 209 ER+, 135 ER-
Study of chronic lymphocytic leukemia (CLL)
- Bioconductor package "CLL"
- Drs. Sabina Chiaretti and Jerome Ritz
- 22 patients: 8 stable, 14 progressive

Data in R

Expression matrix (x)
Feature data (f) - feature attributes
Phenotype data (p) - sample attributes

Expression matrix

rows = features, columns = samples

class(x)

"matrix"

dim(x)

22283   344

x[1:5, 1:5]

              VDX_3     VDX_5     VDX_6
1007_s_at 11.965135 11.798593 11.777625
1053_at    7.895424  7.885696  7.949535
117_at     8.259272  7.052025  8.225930

Feature data

rows = features, columns = any number of attributes

class(f)

"data frame"

dim(f)

22283   3

f[1:3, ]

          symbol entrez   chrom
1007_s_at   DDR1    780  6p21.3
1053_at     RFC2   5982 7q11.23
117_at     HSPA6   3310    1q23

Phenotype data

rows = samples, columns = any number of attributes

class(p)

"data frame"

dim(p)

344   3

# er = +/- for Estrogen Receptor
p[1:3, ]

      id age       er
VDX_3  3  36 negative
VDX_5  5  47 positive
VDX_6  6  44 negative

Visualize gene expression with a boxplot

boxplot(<y-axis> ~ <x-axis>, main = "<title>")

boxplot(<gene expression> ~ <phenotype>, main = "<feature>")

boxplot(x[1, ] ~ p[, "er"], main = f[1, "symbol"])

Let's practice!

Differential Expression Analysis with limma in R