RNA-Seq data from Breast Invasive Carcinoma (BRCA)

1. Open this file and explore the content: tcga_rnaseq.txt. Description:

• RNA-Seq data of BRCA samples taken from The Cancer Genome Atlas (TCGA) data portal. (http://cancergenome.nih.gov/)
• Contains 10 normal samples, 20 tumor samples with 2 subtypes (Basal-like and Her2-enriched).
• This dataset was normalized from TMM.

2. Upload your file to Babelomics 5.0.

3. Go to section Expression > Clustering and try several clustering strategies for samples & genes:

• UPGMA + Euclidean (square)
• UPGMA + Correlation coeff. (Spearman)
• Which distance parameter is better for proper clustering?

4. Repeat the analysis using the same distance parameters and SOTA method.

• SOTA + Euclidean (square)
• SOTA + Correlation coeff. (Spearman)
• Do the results change based on the method or the distance parameter?

5. Try to cluster your samples with K-means.

• Set k-value 6 and use Correlation coeff. (Spearman)
• Repeat the same analysis with k-value 3.
• Check the results of K-means.
• Are the results acceptable?
• Is the dendrogram representing any hierarchy between the samples?

6. Try to cluster your samples with K-means.

• Set k-value 2 and use Correlation coeff. (Spearman).
• Can we say that K-means is good to distinguish tumor from normal?