Publications by Alex Lewis, Diana Murray, Jeffrey Chang, and Philip Moos
Module 7: Breast Cancer Cell Lines: Part 2
About this activity Until now, we worked with expression data and clinical information from breast cancer patient samples (TCGA). We found patterns in genes and in samples by color-coding the expression data in a heat map and then clustering the samples and the genes in the heat map based on how similar they are to each other. We will continue wo...
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Student Project
Introduction The function ‘heatmap()’ is a great way to look at data in a more interesting and fun way. In this activity we will use the gene expression data from breast cancer patients in The Cancer Genome Atlas (TCGA) and we explore whether clinical features of these patients(mainly age_at_diagnosis) correlate with gene expression patterns....
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Module 2: Breast Cancer Patient Data
About this activity In this activity, we will put our new skills in R to use with a large real-life dataset! You will load and examine an R data frame that contains clinical information from over 1,000 breast cancer patients from The Cancer Genome Atlas (TCGA). The Cancer Genome Atlas or TCGA characterized over 20,000 cancer samples spanning 33 c...
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Module 6: Breast Cancer Cell Lines: Part 1
About this activity Until now, we worked with expression data and clinical information from breast cancer patient samples (TCGA). We found patterns in genes and in samples by color-coding the expression data in a heat map and then clustering the samples and the genes in the heat map based on how similar they are to each other. In this module, we ...
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Module 3: Breast Cancer Expression Data
About this activity You will load and examine R dataframe objects that contain data from over 1,000 breast cancer (BRCA) patients from The Cancer Genome Atlas (TCGA). The objects include: clinical measurements on the patients and the patients’ tumors, such as gender, age, estrogen, progesterone, and her2 receptor status. We examined this data ...
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Module 5: Genes for Enrichment Analysis
About this activity In Module 4, we used clustering to look for clinically relevant patterns in the TCGA breast cancer gene expression data matrix. We found a cluster that corresponds to patient samples with Triple Negative Breast Cancer. In this Module, we will look at the composition of clusters on the other dimension of our data matrix: The ge...
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