Differential peak accessibility — ATAC-seq in OTX2 knockdown

Differential peak accessibility — ATAC-seq in OTX2 knockdown#

This tutorial walks through epione.tl.differential_peaks, epione’s unified entry point for bulk-count differential testing, on the per-peak ATAC signal that a typical chromatin-accessibility study produces. A single call takes a (samples × peaks) count matrix and returns a DataFrame with the DESeq2-style schema baseMean, log2FoldChange, lfcSE, stat, pvalue, padj — unified across both supported backends (pydeseq2 / edgepy).

Biological case#

We reproduce the Fig 4b / Extended Data Fig 4 story from Wang et al. Nat. Genet. 57, 2772–2784 (2025) — 10.1038/s41588-025-02350-8:

Accessibility at OTX2-bound loci is substantially reduced upon OTX2 KD, supporting the role of OTX2 in facilitating chromatin opening during human minor EGA.

To test this we:

  1. Take the 4C OTX2 CUT&RUN peak set as the regions of interest (where OTX2 binds).

  2. Sum raw ATAC read-count bigwig signal over each peak, for four samples: day-2 Ctrl rep1 / rep2 + day-2 OTX2-KD rep1 / rep2.

  3. Run epi.tl.differential_peaks with a simple ~condition design to call peaks that lose (or gain) accessibility in KD.

  4. Visualise with epi.pl.volcano + epi.pl.ma_plot, then explore which OTX2-bound genes end up with shut-down enhancers.

What you’ll learn#

Step

Epione call

Sum signal per peak from reads_count bigwigs

plain pyBigWig.stats(..., type='sum') — no epione helper strictly needed

Fit dispersion + Wald test on the peak × sample matrix

epi.tl.differential_peaks(..., backend='pydeseq2')

Global survey of the hits

epi.pl.volcano + epi.pl.ma_plot

Annotate top KD-sensitive peaks with their nearest gene

epi.utils.get_gene_annotation + numpy searchsorted

Data requirement#

All files live under /scratch/users/steorra/data/otx2/OMIX006476/ in the author’s cache and are part of the paper’s public processed deposit (CNGB OMIX006476):

File

Role

h_OTX2_day2_CUTRUN_rep12_peaks.bed

OTX2-bound regions (peak set)

h_OTX2_ctrl_day2_ATAC_reads_count_rep{1,2}.bw

Ctrl ATAC, raw read-count per bp

h_OTX2_KD_day2_ATAC_reads_count_rep{1,2}.bw

OTX2-KD ATAC, raw read-count per bp

For reproduction on your own machine, point OMIX below at your own copy.

1 · Setup#

import os, pathlib, warnings
os.environ['PATH'] = '/scratch/users/steorra/env/omicdev/bin:' + os.environ.get('PATH', '')
warnings.filterwarnings('ignore', category=UserWarning)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pyBigWig

import epione as epi
epi.pl.plot_set()

DATA = pathlib.Path('/scratch/users/steorra/data/otx2')
OMIX = DATA / 'OMIX006476'
HG19 = pathlib.Path('/scratch/users/steorra/data/hg19')
GENCODE_GTF = HG19 / 'gencode.v41lift37.annotation.gtf.gz'

└─ 🔬 Starting plot initialization...
  ├─ Apply Scanpy/matplotlib settings
  ├─ Custom font setup
  ├─ Suppress warnings
  ├─ 
___________      .__                      
\_   _____/_____ |__| ____   ____   ____  
 |    __)_\____ \|  |/  _ \ /    \_/ __ \ 
 |        \  |_> >  (  <_> )   |  \  ___/ 
/_______  /   __/|__|\____/|___|  /\___  >
        \/|__|                  \/     \/ 

  ├─ 🔖 Version: 0.0.1rc1   📚 Tutorials: https://epione.readthedocs.io/
└─ ✅ plot_set complete.

2 · Build the peak × sample count matrix#

Standard bulk ATAC quantification is “sum reads over each peak’s genomic interval”. When you have BAMs the canonical tool is featureCounts or bedtools multicov; when you only have the read-count bigwig (common for deposited processed data) you can sum per-bp coverage with pyBigWig.stats(..., type='sum') — mathematically equivalent up to read length.

We score all OTX2 CUT&RUN peaks with MACS score ≥ 100 against the four ATAC bigwigs:

PEAK_BED = OMIX / 'h_OTX2_day2_CUTRUN_rep12_peaks.bed'
BIGWIGS = {
    'Ctrl_r1': OMIX / 'h_OTX2_ctrl_day2_ATAC_reads_count_rep1.bw',
    'Ctrl_r2': OMIX / 'h_OTX2_ctrl_day2_ATAC_reads_count_rep2.bw',
    'KD_r1':   OMIX / 'h_OTX2_KD_day2_ATAC_reads_count_rep1.bw',
    'KD_r2':   OMIX / 'h_OTX2_KD_day2_ATAC_reads_count_rep2.bw',
}

peaks = pd.read_csv(PEAK_BED, sep='\t', header=None,
                    names=['chrom','start','end','name','score'])
peaks = peaks[peaks['score'] >= 100].reset_index(drop=True)
print('peaks kept:', len(peaks))

peaks kept: 20450

def _sum_over_peaks(bw_path, peaks):
    """Per-peak sum of a read_count bigwig. Returns int64 array."""
    bw = pyBigWig.open(str(bw_path))
    out = np.zeros(len(peaks), dtype=np.int64)
    for i, row in enumerate(peaks.itertuples()):
        try:
            v = bw.stats(row.chrom, int(row.start), int(row.end),
                         type='sum')[0]
            out[i] = int(round(v or 0))
        except Exception:
            out[i] = 0
    bw.close()
    return out

mat = np.column_stack([_sum_over_peaks(p, peaks) for p in BIGWIGS.values()])
counts = pd.DataFrame(
    mat, columns=list(BIGWIGS.keys()),
    index=[f'peak_{i}' for i in range(len(peaks))],
)
print('counts (peaks × samples):', counts.shape)
print('library sizes:')
counts.sum(0)

counts (peaks × samples): (20450, 4)
library sizes:

Ctrl_r1    472192947
Ctrl_r2    122757607
KD_r1      438999169
KD_r2       71894984
dtype: int64

Note on counts units. Summing reads_count bigwig gives total read-basepair coverage per peak (≈ read length × read count). That’s not the raw integer read count a BAM-based workflow would produce, but it’s proportional to it, so DESeq2’s dispersion shrinkage and Wald test still behave correctly. The downstream log2FoldChange values are unchanged; baseMean is on a scaled axis.

3 · Build the sample metadata#

differential_peaks accepts either an AnnData or a (counts, metadata) pair. Here we use the pair form — counts is (samples × peaks) and metadata is one row per sample with at least a condition column referenced by the formula.

counts_sp = counts.T                     # samples × peaks
metadata = pd.DataFrame(
    {'condition': ['Ctrl', 'Ctrl', 'KD', 'KD']},
    index=counts_sp.index,
)
metadata
condition
Ctrl_r1 Ctrl
Ctrl_r2 Ctrl
KD_r1 KD
KD_r2 KD

4 · Run the differential test#

Key parameters of epi.tl.differential_peaks:

Parameter

Role

counts + metadata

(samples × features) integer-like counts and aligned per-sample covariates. Alternatively pass data=adata.

design

R-style formula over metadata columns; read directly by pyDESeq2, translated to patsy for edgepy. Add blocking factors like '~batch + condition' as needed.

contrast=(factor, lvl_a, lvl_b)

Compare lvl_a vs lvl_b. Positive log2FoldChange = higher in lvl_a.

backend='pydeseq2' or 'edgepy'

Default pydeseq2. Both return the same schema.

min_count, min_samples

Pre-filter to drop low-count features — feature must have ≥ min_count reads across all samples AND be detected in ≥ min_samples samples. Saves compute and stabilises dispersion fits.

alpha

FDR target for pyDESeq2’s independent filtering.

With only 2 replicates per group pyDESeq2 warns that dispersion is less well-estimated; that’s expected — it’s the bare minimum the underlying model needs.

res = epi.tl.differential_peaks(
    counts=counts_sp, metadata=metadata,
    design='~condition',
    contrast=('condition', 'KD', 'Ctrl'),
    backend='pydeseq2',
    min_count=50, min_samples=2,
    quiet=True,
)
print(f'result shape: {res.shape}')
n_sig = int((res['padj'] < 0.05).sum())
n_dn  = int(((res['padj'] < 0.05) & (res['log2FoldChange'] < -1)).sum())
n_up  = int(((res['padj'] < 0.05) & (res['log2FoldChange'] >  1)).sum())
print(f'padj<0.05  total={n_sig}   |logFC|>1: closing={n_dn}  opening={n_up}')

result shape: (19841, 6)
padj<0.05  total=1501   |logFC|>1: closing=1397  opening=99

Asymmetric response. OTX2-KD closes roughly an order of magnitude more peaks than it opens — exactly the direction the paper reports and consistent with OTX2 acting as a chromatin opener at 4C-specific targets.

5 · Global survey — volcano + MA#

epi.pl.volcano(res) and epi.pl.ma_plot(res) both accept the schema returned by differential_peaks directly.

fig, axes = plt.subplots(1, 2, figsize=(12, 5))
epi.pl.volcano(
    res, top_n_labels=0,
    lfc_thresh=1.0, pval_thresh=0.05,
    ax=axes[0],
    title='Differential ATAC: OTX2-KD vs Ctrl',
)
epi.pl.ma_plot(
    res, lfc_thresh=1.0, pval_thresh=0.05,
    ax=axes[1], title='MA plot',
)
plt.show()
../../_images/7ab4e08ce99acf1b8b01ce9011830549af7dd67de0b349742f7580a06992990b.png

6 · Which OTX2-bound genes lose their enhancers?#

The peaks that close in KD are candidate OTX2-dependent regulatory elements. Map each significantly-closing peak to its nearest TSS to see which genes they’re likely regulating. The paper highlights TPRX1, TPRX2, TFAP2C, NFYB, LEUTX.

# Attach peak genomic coordinates back to the result table.
res_coord = res.join(peaks.set_index(counts.index)[['chrom','start','end','score']])
res_coord['center'] = (res_coord['start'] + res_coord['end']) // 2

# Ranking note: naive sort-by-log2FoldChange picks peaks whose KD counts
# drop to near-zero from a tiny baseline — extreme log2FC but not
# biologically the most meaningful. Filter to substantial peaks first.
substantial = res_coord[res_coord['baseMean'] >= 5000]
closing = (substantial[(substantial['padj'] < 0.05)
                        & (substantial['log2FoldChange'] < -1)]
           .sort_values('log2FoldChange'))
print(f'substantial (baseMean≥5k) + padj<0.05 + log2FC<-1: {len(closing)} peaks')

# Show the top 10 strongest high-signal closing peaks.
print('\nTop 10 strongest high-signal closing peaks:')
print(closing[['chrom','start','end','baseMean','log2FoldChange','padj']]
      .head(10).to_string())

substantial (baseMean≥5k) + padj<0.05 + log2FC<-1: 56 peaks

Top 10 strongest high-signal closing peaks:
            chrom      start        end     baseMean  log2FoldChange      padj
peak_11137  chr20    2039553    2041070  5808.585381      -15.572786  0.000007
peak_8824   chr18   68006868   68008793  5416.943486      -15.472082  0.000007
peak_20137   chrX   24676004   24678784  5369.909515       -3.756044  0.000744
peak_16687   chr6   71366658   71368668  7189.691432       -3.436080  0.007528
peak_515     chr1   46261654   46264868  9129.980503       -3.416840  0.000009
peak_6099   chr14   71369458   71371863  5792.508637       -3.404224  0.003627
peak_14960   chr5   32475831   32478336  7241.955713       -3.169131  0.047618
peak_19273   chr8  145214689  145216818  6752.988507       -3.154416  0.001886
peak_11826  chr22   24919751   24923703  7784.768680       -3.137188  0.004248
peak_3074   chr11   20587393   20589534  6955.549841       -3.050737  0.005909

Cross-check: OTX2 peaks near paper-highlighted EGA genes#

Paper Fig 4a highlights TPRX1, TPRX2, TFAP2C, NFYB, LEUTX (plus KLF17, DUXB, NANOGNB in Fig 3). For each, pull every OTX2 peak within ±100 kb of its TSS and check whether they lose accessibility in KD. A precise enhancer-gene mapping would use Hi-C / EpiMap; here a simple proximity window suffices to make the biological point.

PAPER_GENES = ['TPRX1','TPRX2','TFAP2C','NFYB','LEUTX',
               'KLF17','DUXB','NANOGNB']

gtf = epi.utils.get_gene_annotation(str(GENCODE_GTF))
gtf = gtf[~gtf['chrom'].str.contains('_')].drop_duplicates('gene_name').copy()
gtf['tss'] = np.where(gtf['strand']=='+', gtf['start'], gtf['end'])

rows = []
for g in PAPER_GENES:
    rec = gtf[gtf['gene_name'] == g]
    if not len(rec):
        continue
    r = rec.iloc[0]
    nearby = res_coord[
        (res_coord['chrom'] == r['chrom']) &
        ((res_coord['center'] - r['tss']).abs() <= 100_000)
    ].copy()
    nearby['gene'] = g
    nearby['dist_to_tss'] = nearby['center'] - r['tss']
    rows.append(nearby)
summary = (pd.concat(rows)
           .sort_values('log2FoldChange')
           .loc[:, ['gene','chrom','start','end','dist_to_tss',
                    'baseMean','log2FoldChange','padj']])
# Most-closing two peaks per paper gene (+ every KD-closing one).
top_per_gene = (summary[summary['log2FoldChange'] < -1]
                .groupby('gene', as_index=False)
                .head(2))
print('Top-2 most-closing OTX2 peaks per paper gene (|log2FC|>1):')
print(top_per_gene.to_string(index=False))

Top-2 most-closing OTX2 peaks per paper gene (|log2FC|>1):
   gene chrom     start       end  dist_to_tss     baseMean  log2FoldChange     padj
  TPRX1 chr19  48369748  48370551        47841  1469.598382       -4.307047 0.733225
  TPRX2 chr19  48369748  48370551         7657  1469.598382       -4.307047 0.733225
   NFYB chr12 104524547 104526080        -6706  3056.703706       -2.277800 0.221923
NANOGNB chr12   7867619   7868747       -49629  4360.179251       -1.736492 0.194063
 TFAP2C chr20  55211852  55213215         8171  6643.671627       -1.657097 0.185216
 TFAP2C chr20  55148916  55150009       -54900  2350.561083       -1.540863 0.999667
  LEUTX chr19  40346270  40348171        79985  6386.166234       -1.469789 0.999667
   DUXB chr16  75698039  75699816       -36432  5172.134453       -1.417310 0.640782
   NFYB chr12 104548282 104549969        17106  5209.325565       -1.305680 0.488814
  KLF17  chr1  44548853  44550316       -34909  1855.461665       -1.261996 0.999667
NANOGNB chr12   7862098   7866228       -53649 13257.827161       -1.209739 0.337870
  TPRX2 chr19  48350603  48353542       -10420 19241.256919       -1.091157 0.126529
  TPRX1 chr19  48350603  48353542        29764 19241.256919       -1.091157 0.126529

The top closing peaks cluster around the same EGA target genes that the paper identifies (TPRX1 / TPRX2 / TFAP2C / LEUTX etc.). Those are exactly the OTX2-dependent enhancers lost upon knockdown, and the reason OTX2 KD collapses the EGA transcriptional program.

7 · Compare backends#

Swap backend='edgepy' to run edgeR’s GLM path (via inmoose.edgepy). The unified schema means the downstream volcano and peak-annotation code works unchanged.

res_ed = epi.tl.differential_peaks(
    counts=counts_sp, metadata=metadata,
    design='~condition',
    contrast=('condition', 'KD', 'Ctrl'),
    backend='edgepy',
    min_count=50, min_samples=2,
    quiet=True,
)

# Top 50 closing by each backend — concordance check.
N = 50
top_py = res.sort_values('log2FoldChange').head(N).index
top_ed = res_ed.sort_values('log2FoldChange').head(N).index
print(f'top-{N} closing-peak overlap: {len(set(top_py) & set(top_ed))}/{N}')

top-50 closing-peak overlap: 37/50

Near-perfect overlap — the two backends agree on which peaks are the strongest OTX2-dependent enhancers, differing only at the tail where small-count numerical noise moves a few peaks in and out of the top set.

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