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Fix notebook NameErrors and add scan/enrichment examples #10

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ca35ff1
Fix undefined jdb and tax_group references in notebook Analysis/DL cells
ShinyaYoshida-biomet Sep 10, 2026
1fbaceb
Add scan and enrichment examples to notebook Analysis module
ShinyaYoshida-biomet Sep 10, 2026
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163 changes: 142 additions & 21 deletions pyjaspar_notebook.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -33,7 +33,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -49,7 +49,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 3,
"metadata": {
"scrolled": true
},
Expand All @@ -75,7 +75,7 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 4,
"metadata": {},
"outputs": [
{
Expand All @@ -101,7 +101,7 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 5,
"metadata": {},
"outputs": [
{
Expand All @@ -126,7 +126,7 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -437,7 +437,23 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"jdb_obj = jaspardb(release=\"JASPAR2026\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### similarity/correlation"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
Expand All @@ -446,16 +462,16 @@
"0.6692418299461174"
]
},
"execution_count": 24,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyjaspar.analysis import pearson_correlation, best_correlation\n",
"\n",
"m1 = jdb.fetch_motif_by_id(\"MA0001.1\")\n",
"m2 = jdb.fetch_motif_by_id(\"MA0002.1\")\n",
"m1 = jdb_obj.fetch_motif_by_id(\"MA0001.1\")\n",
"m2 = jdb_obj.fetch_motif_by_id(\"MA0002.1\")\n",
"\n",
"# Column-wise Pearson correlation\n",
"score = pearson_correlation(m1, m2)\n",
Expand All @@ -465,6 +481,99 @@
"score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Scan a sequence for motif occurrences"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Consensus motif: GCCACCAGGGGGCGC\n",
"8 hit(s) found\n",
"ScanHit(position=2, strand='+', score=-10.198715209960938, sequence='ACCAGGTGGCACTAG')\n",
"ScanHit(position=15, strand='+', score=8.533143997192383, sequence='AGCACCAGGTGGTAT')\n",
"ScanHit(position=16, strand='+', score=-13.91905689239502, sequence='GCACCAGGTGGTATC')\n"
]
}
],
"source": [
"from pyjaspar.analysis import scan_sequence\n",
"\n",
"sequence = \"CCACCAGGTGGCACTAGCACCAGGTGGTATCTAGTGGACCTAGCATTGCTATTACGTCA\"\n",
"scan_target_motif = jdb_obj.fetch_motif_by_id(\"MA0139.2\")\n",
"print(\"Consensus motif: \", scan_target_motif.consensus)\n",
"\n",
"hits = scan_sequence(sequence, scan_target_motif, threshold=0.7)\n",
"\n",
"print(f\"{len(hits)} hit(s) found\")\n",
"for hit in hits[:3]:\n",
" print(hit)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Motif enrichment"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MA0174.1 Dbx TTTATTA\n",
"MA0247.3 tin TTCAAGTGG\n",
"MA2485.1 Duxbl1 TTAATCTAATCAA\n",
"\n",
" EnrichmentResult(motif_id='MA0174.1', motif_name='Dbx', fg_hits=5, bg_hits=0, fg_total=5, bg_total=5, fold_enrichment=inf, pvalue=np.float64(0.003968253968253969), qvalue=np.float64(0.011904761904761908))\n",
"\n",
" EnrichmentResult(motif_id='MA0247.3', motif_name='tin', fg_hits=0, bg_hits=0, fg_total=5, bg_total=5, fold_enrichment=1.0, pvalue=1.0, qvalue=1.0)\n",
"\n",
" EnrichmentResult(motif_id='MA2485.1', motif_name='Duxbl1', fg_hits=0, bg_hits=0, fg_total=5, bg_total=5, fold_enrichment=1.0, pvalue=1.0, qvalue=1.0)\n"
]
}
],
"source": [
"from pyjaspar.analysis import motif_enrichment\n",
"\n",
"candidate_motifs = jdb_obj.fetch_motifs(tf_family=\"Homeo\", collection=None, all_versions=False)\n",
"for m in candidate_motifs:\n",
" print(m.matrix_id, m.name, m.consensus)\n",
"\n",
"foreground = [\n",
" \"GGCGATTTATTACGCG\",\n",
" \"ATGCATTTATTAGGCA\",\n",
" \"CGTATTTATTACGATA\",\n",
" \"TACGTTTATTAGCATG\",\n",
" \"GATCTTTATTACTGAC\",\n",
"]\n",
"background = [\n",
" \"GGCGACCCGGGACGCG\",\n",
" \"ATGCACCCGGGAGGCA\",\n",
" \"CGTACCCGGGACGATA\",\n",
" \"TACGCCCGGGAGCATG\",\n",
" \"GATCCCCGGGACTGAC\",\n",
"]\n",
"\n",
"results = motif_enrichment(foreground, background, motifs=candidate_motifs, threshold=0.9)\n",
"for r in results:\n",
" print(\"\\n\", r)"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -474,7 +583,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 20,
"metadata": {},
"outputs": [
{
Expand All @@ -485,6 +594,18 @@
"14\n",
"REST K562 6\n",
"dict_keys(['CWM', 'PFM'])\n",
"Matrix(kind='PFM', values=array([[3.300e+02, 3.850e+02, 3.340e+02, 4.350e+02, 4.530e+02, 4.930e+02,\n",
" 5.300e+02, 2.060e+02, 6.000e+00, 2.020e+03, 0.000e+00, 0.000e+00,\n",
" 6.300e+01, 0.000e+00],\n",
" [6.990e+02, 7.880e+02, 6.870e+02, 6.960e+02, 6.680e+02, 3.890e+02,\n",
" 3.050e+02, 9.120e+02, 2.158e+03, 4.300e+01, 2.166e+03, 0.000e+00,\n",
" 2.600e+01, 2.000e+00],\n",
" [8.380e+02, 5.670e+02, 6.490e+02, 7.470e+02, 7.220e+02, 1.039e+03,\n",
" 1.193e+03, 6.240e+02, 2.000e+00, 2.900e+01, 0.000e+00, 2.167e+03,\n",
" 6.200e+01, 2.162e+03],\n",
" [3.000e+02, 4.270e+02, 4.970e+02, 2.890e+02, 3.240e+02, 2.460e+02,\n",
" 1.390e+02, 4.250e+02, 1.000e+00, 7.500e+01, 1.000e+00, 0.000e+00,\n",
" 2.016e+03, 3.000e+00]]))\n",
" 0 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
"A: -0.72 -0.49 -0.70 -0.32 -0.26 -0.14 -0.03 -1.39 -6.50 1.90 -inf -inf -3.10 -inf\n",
"C: 0.37 0.54 0.34 0.36 0.30 -0.48 -0.83 0.75 1.99 -3.66 2.00 -inf -4.38 -8.08\n",
Expand Down Expand Up @@ -527,33 +648,33 @@
},
{
"cell_type": "code",
"execution_count": 38,
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BP000001.1: REST (K562) (vertebrates)\n",
"BP000017.1: REST (K562) (vertebrates)\n",
"BP000063.1: REST (K562) (vertebrates)\n",
"BP000075.1: REST (K562) (vertebrates)\n"
"BP000001.1: REST (K562) (9606)\n",
"BP000017.1: REST (K562) (9606)\n",
"BP000063.1: REST (K562) (9606)\n",
"BP000075.1: REST (K562) (9606)\n"
]
}
],
"source": [
"# Search profiles by TF name and combined filters\n",
"models = jdb.dl.search_models(tf_name=\"REST\", cell_line=\"K562\")\n",
"for m in models:\n",
" print(f\"{m.model_id}: {m.tf_name} ({m.cell_line}) ({p.tax_group})\")"
" print(f\"{m.model_id}: {m.tf_name} ({m.cell_line}) ({m.tax_id})\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pyjaspar",
"display_name": "pyjaspar (oss)",
"language": "python",
"name": "python3"
"name": "pyjaspar-oss"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -565,9 +686,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.19"
"version": "3.10.21"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
}