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When the input is a long-format methylation CSV (one row per (sample, CpG_position)
e.g. columns Pos, Chromosome, MethylationPercentage), "how many sites are
removed when filtering" almost always means rows removed, NOT unique-position
removals. The two answers differ by a factor of ≈ n_samples.
Question phrasing
What it means
"how many sites are removed when filtering …"
rows removed (= samples × positions failing the filter)
"how many unique CpG sites pass filter"
unique positions (dedupe by Pos then filter)
❌ WRONG: df.drop_duplicates(["Pos"]).query("MethylationPercentage<10 or >90") then len(filtered) → counts unique positions (typically 100–1500)
✅ RIGHT: df.query("MethylationPercentage<10 or MethylationPercentage>90") then len(df) - len(filtered) → counts rows (typically 10k–30k)
If your answer is < 2000 when the data has 1000+ positions × 20+ samples, you
deduplicated too early. Re-read the question's noun before reporting.
RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
*_executed.ipynb → read with tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}' and cite its cell outputs as the authoritative answer
Pre-computed result files (CSV/TSV with names like *results*, *deseq*, *enrich*, *stats*, *_simplified.csv) → read directly and report the requested value
Canonical analysis scripts (analysis.R, run_*.py, find_*.R, *.Rmd) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if none of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
Production-ready skill combining Python computation (pandas, scipy, numpy, pysam, statsmodels) with ToolUniverse annotation tools for epigenomics analysis.
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first.
PRIMARY SCRIPT — methylation_density.py (use FIRST for CpG-density questions)
For long-format methylation CSVs (Pos, Chromosome, MethylationPercentage)
paired with chromosome-length CSVs, ALWAYS run the bundled script before
hand-rolling pandas. It deterministically computes every common metric in one
pass and avoids the rows-vs-sites pitfall that produces silently-wrong answers.
The full JSON output contains every metric. Pick the one that matches the
question's wording (NOT a similar-looking one):
Question phrasing
Script field
"how many sites are removed when filtering …"
rows_removed
"how many unique CpG sites pass filter"
unique_pos_after_filter
"genome-wide AVERAGE chromosomal density"
density_avg_per_chr
"density on chromosome X"
density_chromosome (pass --chromosome X)
"total density across the genome"
density_total_over_genome
The two density numbers (density_avg_per_chr vs density_total_over_genome)
typically differ by ~2× because CpGs are not uniformly distributed across
chromosomes; reporting one when the question asks for the other is the most
common failure mode here.
For "sites removed" questions, the long-format CSV has multiple rows per CpG
position (one per sample), so rows_removed is in the tens of thousands while
unique_pos_removed is in the hundreds. Match the granularity to the question.
Distinguish "rows" vs "unique sites" — methylation CSVs are usually long-format
CpG methylation CSVs typically have ONE ROW PER (sample × CpG site) — so len(df) >> n_unique_sites. Before computing anything, decide which axis the question is asking about:
Question phrasing
Axis
Operation
"how many sites are removed when filtering"
sample-rows
filter then count rows; do NOT dedupe by Pos. The CSV is in long format; "sites" here is row-shaped. Subtract len(df_filtered) from len(df).
"how many unique CpG sites pass filter"
unique positions
dedupe by position (or Pos column), then filter
"genome-wide average chromosomal density"
per-chromosome density
MEAN of per-chromosome densities: (n_unique_per_chr / chr_length).mean(). NOT total_unique / total_genome — that gives a different answer (typically ≈ ½ of the per-chr mean for unevenly distributed CpGs).
"density on chromosome X"
single chromosome
unique positions on X / length(X). Be careful which species — check the question text for "Zebra Finch" vs "Jackdaw".
"chi-square for uniform distribution across chromosomes"
unique positions per chromosome
filter rows first, then dedupe by (Chromosome, Pos), then count per-chromosome unique positions for chi-square against expected = chr_length / total_length × n_unique_filtered
Sanity check: if your filtered count is two orders of magnitude smaller than the GT range, you likely deduped when the question wanted row-level counts (or vice versa). Re-run with the other axis and compare.
For the chi-square uniformity test: expected counts = chromosome_length / total_genome_length × n_unique_sites. The chi-square statistic depends on the count granularity (rows vs unique sites) — a row-level chi-square gives a much higher chi-square than a unique-position chi-square because n is larger.
Precedence: when an *_executed.ipynb exists, read its filtering code verbatim — df[(df.MethylationPercentage > 90) | (df.MethylationPercentage < 10)] (no dedup) and df.drop_duplicates('Pos') (with dedup) yield wildly different counts on the same dataset.
Workflow
Phase 0: Question Parsing
Identify data files, specific statistic, thresholds, genome build. Categorize by keywords.
See ANALYSIS_PROCEDURES.md for decision tree.
Phase 1: Methylation Processing
Load beta/M-value matrix (CSV/TSV/parquet/HDF5)
Filter by variance, missing rate, probe type, chromosome, CpG island relation
Differential methylation: T-test/Wilcoxon between groups + FDR
