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DREAM-High

Diana Murray

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Module 5: Genes for Enrichment Analysis
The TCGA breast cancer data set contains the expression levels of 18K genes from 1K patient samples. We rearranged this data so that similar samples and similar genes are "clustered" together. We found interesting clinical properties for the sample clusters, such as ER+ and ER- samples. Here, we will use Enrichment Analysis to find biological themes for the gene clusters.
Module 4: Patterns in Breast Cancer Gene Expression
The TCGA breast cancer data set contains the expression levels of 18K genes from 1K patient samples. We will rearrange this data so that similar samples and similar genes are "clustered" together and look for clinical themes in the patient sample clusters.
Module 3: Breast Cancer Expression Data
The TCGA breast cancer data set contains the expression levels of 18K genes from 1K patient samples. We will learn how manipulate this data and perform preliminary analyses.
Module 7: Breast Cancer Cell Lines: Part 2
We previously worked with gene expression data from patient samples. We will apply what we learned to data from human cancer cell lines. Human cancer cell lines are widely used as experimental models of cancer and often provide additional information related to their physical properties.
Student Project
A high school student applies his new skills and knowledge to create a new way of examining the TCGA breast cancer data set.
Module 2: Breast Cancer Patient Data
In this activity, we will put our new skills in R to use with a large real-life dataset. We 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).
Module 6: Breast Cancer Cell Lines: Part 1
We previously worked with gene expression data from patient samples. We will apply what we learned to data from human cancer cell lines. Human cancer cell lines are widely used as experimental models of cancer and often provide additional information related to their physical properties.