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3 different types of leukemias: ALL, AML, CML
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 <- 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
Tests:
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
# 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
Down 898 3401 1890
NotSig 18323 13194 16408
Up 951 3577 1874
sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] limma_3.38.3 leukemiasEset_1.18.0 Biobase_2.42.0
[4] BiocGenerics_0.28.0
loaded via a namespace (and not attached):
[1] workflowr_1.2.0.9000 Rcpp_1.0.1 digest_0.6.18
[4] rprojroot_1.2 backports_1.1.3 git2r_0.25.1
[7] magrittr_1.5 evaluate_0.13 stringi_1.4.3
[10] fs_1.2.7 whisker_0.3-2 rmarkdown_1.12
[13] tools_3.5.3 stringr_1.4.0 glue_1.3.1
[16] xfun_0.6 yaml_2.2.0 compiler_3.5.3
[19] htmltools_0.3.6 knitr_1.22.6