Last updated: 2019-01-09

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Rmd d2c5c34 John Blischak 2018-08-08 Organize analysis of leukemiasEset used in slides to demo study with 3 groups.

3 different types of leukemias: ALL, AML, CML

  • Bioconductor package: leukemiasEset
  • Kohlmann et al. 2008, Haferlach et al. 2010

Setup

library(Biobase)
library(leukemiasEset)
library(limma)
data("leukemiasEset")
eset <- leukemiasEset
dim(eset)
Features  Samples 
   20172       60 
head(fData(eset))
data frame with 0 columns and 6 rows
featureData(eset) <- AnnotatedDataFrame(data.frame(ensembl = rownames(exprs(eset)),
                                                   stringsAsFactors = FALSE))
head(fData(eset))
          ensembl
1 ENSG00000000003
2 ENSG00000000005
3 ENSG00000000419
4 ENSG00000000457
5 ENSG00000000460
6 ENSG00000000938
exprs(eset)[1:5, 1:5]
                GSM330151.CEL GSM330153.CEL GSM330154.CEL GSM330157.CEL
ENSG00000000003      3.386743      3.687029      3.360517      3.459388
ENSG00000000005      3.539030      3.836208      3.246327      3.063286
ENSG00000000419      9.822758      7.969170      9.457491      9.591018
ENSG00000000457      4.747283      4.866344      4.981642      5.982854
ENSG00000000460      3.307188      4.046402      5.529369      4.619444
                GSM330171.CEL
ENSG00000000003      3.598589
ENSG00000000005      3.307543
ENSG00000000419      9.863687
ENSG00000000457      5.779449
ENSG00000000460      3.352696
head(pData(eset))
              Project     Tissue LeukemiaType         LeukemiaTypeFullName
GSM330151.CEL   Mile1 BoneMarrow          ALL Acute Lymphoblastic Leukemia
GSM330153.CEL   Mile1 BoneMarrow          ALL Acute Lymphoblastic Leukemia
GSM330154.CEL   Mile1 BoneMarrow          ALL Acute Lymphoblastic Leukemia
GSM330157.CEL   Mile1 BoneMarrow          ALL Acute Lymphoblastic Leukemia
GSM330171.CEL   Mile1 BoneMarrow          ALL Acute Lymphoblastic Leukemia
GSM330174.CEL   Mile1 BoneMarrow          ALL Acute Lymphoblastic Leukemia
                                      Subtype
GSM330151.CEL c_ALL/Pre_B_ALL without t(9 22)
GSM330153.CEL c_ALL/Pre_B_ALL without t(9 22)
GSM330154.CEL c_ALL/Pre_B_ALL without t(9 22)
GSM330157.CEL c_ALL/Pre_B_ALL without t(9 22)
GSM330171.CEL c_ALL/Pre_B_ALL without t(9 22)
GSM330174.CEL c_ALL/Pre_B_ALL without t(9 22)
table(pData(eset)[, "LeukemiaType"])

ALL AML CLL CML NoL 
 12  12  12  12  12 
# Subset to only include ALL, AML, and CML
eset <- eset[, pData(eset)[, "LeukemiaType"] %in% c("ALL","AML", "CML")]
dim(eset)
Features  Samples 
   20172       36 
# Clean up names
phenoData(eset) <- AnnotatedDataFrame(data.frame(type = as.character(pData(eset)[, "LeukemiaType"]),
                                                 stringsAsFactors = FALSE))
head(pData(eset))
  type
1  ALL
2  ALL
3  ALL
4  ALL
5  ALL
6  ALL
exprs(eset)[1:5, 1:5]
                GSM330151.CEL GSM330153.CEL GSM330154.CEL GSM330157.CEL
ENSG00000000003      3.386743      3.687029      3.360517      3.459388
ENSG00000000005      3.539030      3.836208      3.246327      3.063286
ENSG00000000419      9.822758      7.969170      9.457491      9.591018
ENSG00000000457      4.747283      4.866344      4.981642      5.982854
ENSG00000000460      3.307188      4.046402      5.529369      4.619444
                GSM330171.CEL
ENSG00000000003      3.598589
ENSG00000000005      3.307543
ENSG00000000419      9.863687
ENSG00000000457      5.779449
ENSG00000000460      3.352696
colnames(eset) <- sprintf("sample_%02d", 1:ncol(eset))
exprs(eset)[1:5, 1:5]
                sample_01 sample_02 sample_03 sample_04 sample_05
ENSG00000000003  3.386743  3.687029  3.360517  3.459388  3.598589
ENSG00000000005  3.539030  3.836208  3.246327  3.063286  3.307543
ENSG00000000419  9.822758  7.969170  9.457491  9.591018  9.863687
ENSG00000000457  4.747283  4.866344  4.981642  5.982854  5.779449
ENSG00000000460  3.307188  4.046402  5.529369  4.619444  3.352696
dim(eset)
Features  Samples 
   20172       36 
head(pData(eset), 3)
          type
sample_01  ALL
sample_02  ALL
sample_03  ALL
table(pData(eset)[, "type"])

ALL AML CML 
 12  12  12

Design matrix

design <- model.matrix(~0 + type, data = pData(eset))
head(design, 3)
          typeALL typeAML typeCML
sample_01       1       0       0
sample_02       1       0       0
sample_03       1       0       0
colSums(design)
typeALL typeAML typeCML 
     12      12      12

Contrasts matrix

Tests:

  • AML v. ALL: \(\beta_2 - \beta_1 = 0\)
  • CML v. ALL: \(\beta_3 - \beta_1 = 0\)
  • CML v. AML: \(\beta_3 - \beta_2 = 0\)
cm <- makeContrasts(AMLvALL = typeAML - typeALL,
                    CMLvALL = typeCML - typeALL,
                    CMLvAML = typeCML - typeAML,
                    levels = design)
cm
         Contrasts
Levels    AMLvALL CMLvALL CMLvAML
  typeALL      -1      -1       0
  typeAML       1       0      -1
  typeCML       0       1       1

Differential expression

# Fit coefficients
fit <- lmFit(eset, design)
# Fit contrasts
fit2 <- contrasts.fit(fit, contrasts = cm)
# Calculate t-statistics
fit2 <- eBayes(fit2)
# Summarize results
results <- decideTests(fit2)
summary(results)
   AMLvALL CMLvALL CMLvAML
-1     898    3401    1890
0    18323   13194   16408
1      951    3577    1874

sessionInfo()
R version 3.3.3 (2017-03-06)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X Yosemite 10.10.5

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] limma_3.30.13        leukemiasEset_1.10.0 Biobase_2.34.0      
[4] BiocGenerics_0.20.0 

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1.9001 Rcpp_1.0.0           digest_0.6.13       
 [4] rprojroot_1.3-2      backports_1.1.2      git2r_0.23.0        
 [7] magrittr_1.5         evaluate_0.10.1      stringi_1.2.3       
[10] fs_1.2.6             whisker_0.3-2        rmarkdown_1.10.14   
[13] htmldeps_0.1.1       tools_3.3.3          stringr_1.3.1       
[16] glue_1.2.0.9000      yaml_2.2.0           htmltools_0.3.6     
[19] knitr_1.20