![]() ![]() ![]() BCR/ABL BCR/ABL NEG BCR/ABL BCR/ABL NEG ALL1/AF4 BCR/ABL ALL1/AF4 BCR/ABL BCR/ABL NEG E2A/PBX1 NEG BCR/ABL NEG BCR/ABL BCR/ABL BCR/ABL NEG BCR/ABL ALL1/AF4 NEG NEG NEG NEG NEG We can looks at the results of molecular biology testing for the 128 samples: > ALL$mol.biol Transplant: did the patient receive a bone marrow transplant? Derived from f.uĭate last seen: date patient was last seen Derived from citogĬyto.normal: Was cytogenetic test normal? Derived from citogĬitog: original citogenetics data, deletions or t(4 11), t(9 22) statusįusion protein: which of p190, p210 or p190/210 for bcr/ableĬcr: Continuous complete remission? Derived from f.u T(9 22): did the patient have t(9 22) translocation. T(4 11): did the patient have t(4 11) translocation. Derived from CRĭate.cr: Date complete remission if achieved Remission: Complete remission(CR), refractory(REF) or NA. PhenoData object with 21 variables and 128 casesīT: does the patient have B-cell or T-cell ALL If you inspect the resulting ALL variable, it contains 128 samples with 12625 genes, and associated phenotypic data. With that out of the way, you should be able to start R and load this package with the library and data commands: > library("ALL") On Linux, download ALL_1.0.2.tar.gz (or similar) and use sudo R CMD INSTALL ALL_1.0.2.tar.gz at the command prompt. Then from within the R program, use the menu option "Packages", "Install package(s) from local zip files." and select the ZIP file. If you are using Windows, download ALL_1.0.2.zip (or similar) and save it. Running bioCLite version 0.1 with R version 2.1.1Īlternatively, you can download the package by hand from here or here. You should be able to install this from within R as follows: > source("") Their Figure 2 Heatmap, which we recreate, is reproduced here:Īssuming you have a recent version of R (from The R Project) and BioConductor (see Windows XP installation instructions), the example dataset can be downloaded as the BioConductor ALL package. The analysis is a "step by step" recipe based on this paper, Bioconductor: open software development for computational biology and bioinformatics, Gentleman et al. The original citation for the raw data is "Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival" by Chiaretti et al. There is a follow on page dealing with how to do this from Python using RPy. The first section of this page uses R to analyse an Acute lymphocytic leukemia (ALL) microarray dataset, producing a heatmap (with dendrograms) of genes differentially expressed between two types of leukemia.
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