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Why is hierarchical clustering commonly used in gene expression analysis?
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What is a key limitation of K-means clustering when applied to gene expression data?
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What does it indicate if two genes are consistently upregulated together across multiple conditions?
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You are analyzing a gene expression matrix where each row is a gene and each column is a condition (Normal, Cancer A, Cancer B). You observe that Gene X is highly expressed in Cancer A but not in Normal or Cancer B. Question: What could be the most likely biological implication of this observation?
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You observe that Gene A is upregulated in Cancer A and Cancer B but downregulated in Normal tissue. However, another gene, Gene B, shows the opposite pattern: it is upregulated in Normal but downregulated in both cancer types. Question: What is the most plausible biological relationship between Gene A and Gene B?
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You performed hierarchical clustering on a gene expression matrix and found that Cluster X consists of genes that are highly expressed in immune-related conditions but show low expression in cancer samples. Question: Which of the following hypotheses best explains this observation?
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You want to cluster genes with unknown expression patterns and are unsure about the number of clusters. Question: Which clustering method would be the most suitable choice?
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Your clustering analysis of gene expression data revealed a cluster enriched for genes involved in DNA repair, which is highly active in cancer samples. Question: What is the most plausible interpretation of this finding?
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