Challenge: Summarize Gene Counts
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Write a Python function that summarizes gene count data from an RNA-seq experiment.
- Calculate the total counts per gene by summing across all samples for each gene.
- Calculate the total counts per sample by summing across all genes for each sample.
- Compute the mean and median of the total counts per gene.
- Compute the mean and median of the total counts per sample.
- Return a dictionary with keys:
"total_counts_per_gene","total_counts_per_sample","mean_gene_total","median_gene_total","mean_sample_total", and"median_sample_total", containing the corresponding results.
Ratkaisu
The pandas library provides powerful methods to quickly summarize and analyze tabular data, such as gene count tables. Here is how the sum, mean, and median methods work, especially when using the axis parameter:
- sum: Calculates the sum of values along a specified axis. Use
axis=1to sum across columns (totals for each row/gene) andaxis=0to sum across rows (totals for each column/sample); - mean: Computes the average value along a given axis. Use
axis=1for the mean across samples for each gene, oraxis=0for the mean across genes for each sample; - median: Finds the median value along a specified axis. Use
axis=1for the median across samples for each gene, oraxis=0for the median across genes for each sample.
For example, counts_df.sum(axis=1) returns the total counts for each gene by summing values across all samples. Setting axis=0 instead returns totals for each sample by summing across all genes. The same logic applies for mean and median. This flexibility allows you to easily calculate summary statistics for either genes or samples in your dataset.
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Swipe to start coding
Write a Python function that summarizes gene count data from an RNA-seq experiment.
- Calculate the total counts per gene by summing across all samples for each gene.
- Calculate the total counts per sample by summing across all genes for each sample.
- Compute the mean and median of the total counts per gene.
- Compute the mean and median of the total counts per sample.
- Return a dictionary with keys:
"total_counts_per_gene","total_counts_per_sample","mean_gene_total","median_gene_total","mean_sample_total", and"median_sample_total", containing the corresponding results.
Ratkaisu
The pandas library provides powerful methods to quickly summarize and analyze tabular data, such as gene count tables. Here is how the sum, mean, and median methods work, especially when using the axis parameter:
- sum: Calculates the sum of values along a specified axis. Use
axis=1to sum across columns (totals for each row/gene) andaxis=0to sum across rows (totals for each column/sample); - mean: Computes the average value along a given axis. Use
axis=1for the mean across samples for each gene, oraxis=0for the mean across genes for each sample; - median: Finds the median value along a specified axis. Use
axis=1for the median across samples for each gene, oraxis=0for the median across genes for each sample.
For example, counts_df.sum(axis=1) returns the total counts for each gene by summing values across all samples. Setting axis=0 instead returns totals for each sample by summing across all genes. The same logic applies for mean and median. This flexibility allows you to easily calculate summary statistics for either genes or samples in your dataset.
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