Module 11 walkthrough prompts

Module 11 walkthrough prompts#

Companion to the Module 11 walkthrough. Use each Point to write your own Colab cell-level AI prompt. Run the generated code and check the printed objects before the next step. Open the hint only if you want a short sample prompt.

Setup#

The notebook already installs applied-biostats-helper and gseapy==1.3.2 and imports numpy, pandas, seaborn, matplotlib, scipy.stats, statsmodels BH correction, and gseapy. Do not ask the AI to reinstall those unless an import fails.

Step 1. Load the staged tables#

Point: Load gse199911_samples.csv into samples and gse199911_tpm.csv.gz into tpm. Confirm the design at the sample level (PBMC RNA-seq): print n samples, samples['group'].value_counts(), and tpm.shape, then show tpm.head().

Hint

Load samples and tpm from the staged CSVs. Print n, group counts, shape, and tpm.head().

Step 2. Abundance filter#

Point: From tpm, set gene symbols as the index and keep only columns in samples['sample_id']. Keep genes with TPM > 1 in at least 10 samples. Store the filtered matrix as expr_f. Print gene counts before and after, then expr_f.head().

Hint

Index by gene, align to sample IDs, keep genes with TPM > 1 in ≥10 samples as expr_f, and print before/after n genes.

Step 3. Log2 transform#

Point: Create log2 as log2(TPM + 1) from expr_f. Print shape and log2.head().

Hint

Set log2 = log2(expr_f + 1); print shape and head.

Step 4. Variance filter#

Point: On log2, compute per-gene variance across samples. Drop genes in the bottom 20% of variance. Overwrite log2 with the kept genes. Print the variance cutoff and the number of genes remaining.

Hint

Drop the bottom 20% variance genes from log2; print cutoff and n remaining.

Step 5. Gene-wise log2 fold change#

Point: From samples, get sample_id lists for hiv and control. For each gene in log2, compute HIV mean minus control mean. Store the Series as log2fc. Print how many genes have a fold change and show the top of log2fc sorted descending.

Hint

Build log2fc as HIV − control means on log2; show the top sorted values.

Step 6. Gene-wise Welch t-test#

Point: Using the HIV and control columns of log2, run a vectorized Welch t-test with scipy.stats.ttest_ind(..., equal_var=False) along genes (avoid pingouin loops). Store t_stat and p_val. Print the number of p-values and how many raw p-values are < 0.05.

Hint

Vectorized Welch ttest_ind on log2 → t_stat, p_val; count raw p < 0.05.

Step 7. Benjamini–Hochberg FDR#

Point: Adjust p_val with Benjamini–Hochberg via statsmodels.stats.multitest.multipletests. Build DataFrame results with columns gene_symbol, log2fc, t, p, and fdr, sorted by fdr. Print how many genes have FDR < 0.05 and show results.head(10).

Hint

BH-adjust p_val into results (gene_symbol, log2fc, t, p, fdr); count FDR < 0.05.

Step 8. Build the Enrichr input list#

Point: From results, make list sig of gene symbols with FDR < 0.05 and |log2FC| > 1. Print len(sig) and the first 10 symbols.

Hint

Make sig from FDR < 0.05 and |log2FC| > 1; print length and first 10.

Step 9. Enrichr (Hallmark)#

Point: Run gseapy.enrichr on sig with gene set library MSigDB_Hallmark_2020, organism Human, and outdir=None. Store the table as enrich_df sorted by adjusted p-value and show the top 10 rows.

Hint

gseapy.enrichr on sig with Hallmark 2020 → enrich_df; show top 10 by adj-p.

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