From 092ce6fe2d6994632066aebee85d948448b73e3e Mon Sep 17 00:00:00 2001 From: lzj1769 Date: Tue, 31 Jan 2023 16:17:20 -0500 Subject: [PATCH 1/6] add tests --- tests/test_compute_deviations.py | 7 ++++++- tests/test_motif_match.py | 4 ++++ tests/test_preprocessing.py | 11 +++++++++++ 3 files changed, 21 insertions(+), 1 deletion(-) create mode 100644 tests/test_motif_match.py create mode 100644 tests/test_preprocessing.py diff --git a/tests/test_compute_deviations.py b/tests/test_compute_deviations.py index 1f0ae38..176834a 100644 --- a/tests/test_compute_deviations.py +++ b/tests/test_compute_deviations.py @@ -1,6 +1,7 @@ import numpy as np +from anndata import AnnData -from pychromvar.compute_deviations import compute_expectation +from pychromvar.compute_deviations import compute_expectation, compute_deviations def test_compute_expectation(): count = np.array([[1, 0, 1, ], [0, 1, 1]], dtype=np.float32) @@ -11,3 +12,7 @@ def test_compute_expectation(): # check the output assert np.array_equal(exp, np.array([[0.5, 0.5, 1], [0.5, 0.5, 1]])) + +def test_compute_deviations(): + count = np.array([[1, 0, 1, ], [0, 1, 1]], dtype=np.float32) + data = AnnData(count) diff --git a/tests/test_motif_match.py b/tests/test_motif_match.py new file mode 100644 index 0000000..f8b3998 --- /dev/null +++ b/tests/test_motif_match.py @@ -0,0 +1,4 @@ +import MOODS.scan +import MOODS.tools +import MOODS.parsers + diff --git a/tests/test_preprocessing.py b/tests/test_preprocessing.py new file mode 100644 index 0000000..25eee4b --- /dev/null +++ b/tests/test_preprocessing.py @@ -0,0 +1,11 @@ +import numpy as np +from anndata import AnnData + +from pychromvar.preprocessing import add_gc_bias + +def test_gc_bias(): + data = AnnData(np.array([[1, 2,], [3, 4]]), dtype=np.float32) + data.uns["peak_seq"] = ["AAAAAAAA", "CCCCGGGG"] + add_gc_bias(data=data) + + assert np.array_equal(data.var['gc_bias'].values, np.array([0.0, 1.0])) \ No newline at end of file From 6f746a86014537ad15429a03204263438b5f0a36 Mon Sep 17 00:00:00 2001 From: lzj1769 Date: Sat, 4 Feb 2023 18:01:14 -0500 Subject: [PATCH 2/6] add test --- .../run_chromVAR-checkpoint.ipynb | 399 +++++++++++++++++- .../notebooks/compare_with_chromVAR.ipynb | 16 +- docs/source/notebooks/run_chromVAR.ipynb | 126 +++++- pychromvar/__init__.py | 2 +- pychromvar/compute_deviations.py | 9 +- tests/test_compute_deviations.py | 16 +- tests/test_motif_match.py | 4 - 7 files changed, 553 insertions(+), 19 deletions(-) delete mode 100644 tests/test_motif_match.py diff --git a/docs/source/notebooks/.ipynb_checkpoints/run_chromVAR-checkpoint.ipynb b/docs/source/notebooks/.ipynb_checkpoints/run_chromVAR-checkpoint.ipynb index 22fe37e..925767e 100644 --- a/docs/source/notebooks/.ipynb_checkpoints/run_chromVAR-checkpoint.ipynb +++ b/docs/source/notebooks/.ipynb_checkpoints/run_chromVAR-checkpoint.ipynb @@ -5,7 +5,7 @@ "id": "e8b8e7da-4243-4d58-a2f1-4b870ba031c1", "metadata": {}, "source": [ - "# Comparison between pychromVAR and chromVAR: Part I" + "# Compare with chromVAR: Part I" ] }, { @@ -259,6 +259,403 @@ "dev" ] }, + { + "cell_type": "markdown", + "id": "3ac3b08c-4737-450e-a8a2-4aec33f27714", + "metadata": {}, + "source": [ + "Plot variability" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "db6cf16a-e43f-4c88-89ca-14036497b01b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“\u001b[1m\u001b[22mRemoved 1 rows containing missing values (`geom_point()`).”\n" + ] + }, + { + "data": { + "image/png": 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p06ebT2Zmplx//fXypz/9STp37iyJiSHFXqE0h3tsJKC/D4cPH5Zb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\nh3lLVsqdMKvvVtSTNILFg1NFPz8pKqoTAREQge4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QuNBHyrWNersmCw7bQrnx+WTVw3IWxzTBNZw3fWy33XZ+vam4j1r2EUVY+ijES+Up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\njQAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "variability <- computeVariability(dev)\n", + "\n", + "plotVariability(variability, use_plotly = FALSE) " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ee292842-a736-458d-ac9a-50ac0da6b993", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 746 × 6
namevariabilitybootstrap_lower_boundbootstrap_upper_boundp_valuep_value_adj
<chr><dbl><dbl><dbl><dbl><dbl>
MA0004.1.ArntArnt 1.30975871.05743671.5390511.354635e-035.368102e-03
MA0006.1.Ahr::ArntAhr::Arnt 1.64080991.33103701.8973131.558287e-091.055386e-08
MA0019.1.Ddit3::CebpaDdit3::Cebpa0.92173920.75301521.0714867.633490e-018.387992e-01
MA0029.1.MecomMecom 1.11189840.91521951.2791911.241110e-012.357689e-01
MA0030.1.FOXF2FOXF2 1.24760690.98039631.4883057.572098e-032.648457e-02
MA0031.1.FOXD1FOXD1 1.41454921.10594771.6897894.012849e-052.019982e-04
MA0040.1.Foxq1Foxq1 0.88915480.68660201.0616698.532680e-019.066387e-01
MA0041.1.Foxd3Foxd3 1.08887960.89845211.2424331.751568e-013.041767e-01
MA0051.1.IRF2IRF2 2.34296541.99843462.6367993.956742e-325.458838e-31
MA0057.1.MZF1(var.2)MZF1(var.2) 1.43748551.13874471.6833771.677594e-058.801459e-05
MA0059.1.MAX::MYCMAX::MYC 1.19839490.95796091.4040832.431499e-026.718341e-02
MA0066.1.PPARGPPARG 1.19677990.96310231.3860542.518961e-026.875999e-02
MA0067.1.Pax2Pax2 1.48268141.17954581.7545152.707316e-061.516504e-05
MA0069.1.PAX6PAX6 1.43369321.14288481.6914561.942653e-059.912854e-05
MA0070.1.PBX1PBX1 0.95777910.77639401.0934326.379969e-017.364272e-01
MA0071.1.RORARORA 1.24629051.00005931.4436937.829811e-032.713121e-02
MA0072.1.RORA(var.2)RORA(var.2) 1.14283100.89855931.3745397.366332e-021.609360e-01
MA0073.1.RREB1RREB1 1.07830990.73607071.4437262.026617e-013.370155e-01
MA0074.1.RXRA::VDRRXRA::VDR 1.03456330.84878871.1914873.419100e-014.770092e-01
MA0077.1.SOX9SOX9 1.88291361.53885082.1696727.468473e-165.982809e-15
MA0078.1.Sox17Sox17 1.29805371.03092191.5431461.912968e-037.346191e-03
MA0084.1.SRYSRY 1.47271601.12751081.7502944.096331e-062.260568e-05
MA0087.1.Sox5Sox5 NaN NA NA NaN NaN
MA0091.1.TAL1::TCF3TAL1::TCF3 0.96294220.77502111.1213626.184271e-017.249186e-01
MA0092.1.Hand1::Tcf3Hand1::Tcf3 1.05192930.82657611.2453902.821033e-014.220220e-01
MA0101.1.RELREL 2.20044141.55779922.7418641.653870e-261.866869e-25
MA0107.1.RELARELA 2.23353701.52323952.8009799.093856e-281.058582e-26
MA0108.2.TBPTBP 1.12638570.92003471.3012399.803119e-021.964982e-01
MA0109.1.HLTFHLTF 1.16249420.92920811.3592125.103551e-021.234463e-01
MA0111.1.Spz1Spz1 0.89761060.72990201.0474798.323799e-018.935491e-01
⋮⋮⋮⋮⋮⋮⋮
MA0679.2.ONECUT1ONECUT1 0.88943370.72274641.0249286 8.526071e-019.066387e-01
MA0712.2.OTX2OTX2 1.56336021.20643281.8649122 7.404703e-084.635717e-07
MA1113.2.PBX2PBX2 1.05109190.86688761.2115406 2.848616e-014.244438e-01
MA0681.2.PHOX2BPHOX2B 1.04006740.76832391.3356516 3.223884e-014.592340e-01
MA0782.2.PKNOX1PKNOX1 1.07960600.81644351.2877377 1.991517e-013.326637e-01
MA0627.2.POU2F3POU2F3 1.70575631.40295321.9598849 4.527450e-113.274709e-10
MA0508.3.PRDM1PRDM1 0.97222300.78046261.1350415 5.826023e-016.966913e-01
MA0509.2.RFX1RFX1 1.28136081.03444861.5091161 3.077153e-031.134890e-02
MA0798.2.RFX3RFX3 1.31827161.03651621.5886845 1.047523e-034.218404e-03
MA0684.2.RUNX3RUNX3 2.24045201.81996902.5792896 4.920857e-285.912965e-27
MA0743.2.SCRT1SCRT1 1.23045550.98441281.4231488 1.159746e-023.710223e-02
MA0744.2.SCRT2SCRT2 1.02723600.81917671.2316353 3.686143e-015.057415e-01
MA0745.2.SNAI2SNAI2 1.20863320.91077711.5099362 1.935018e-025.697977e-02
MA0143.4.SOX2SOX2 1.65256341.37491701.8790048 8.381000e-105.781338e-09
MA0080.5.SPI1SPI1 2.70156712.16444503.1571455 1.716122e-483.118320e-47
MA0081.2.SPIBSPIB 3.28317312.70045913.7564744 1.438463e-817.144367e-80
MA0829.2.SREBF1(var.2)SREBF1(var.2)1.09241070.83697111.3067737 1.665396e-012.940096e-01
MA0522.3.TCF3TCF3 1.18973020.91967301.4171982 2.932678e-027.830986e-02
MA0830.2.TCF4TCF4 1.19346590.94148001.4157420 2.706830e-027.306480e-02
MA0769.2.TCF7TCF7 1.18093180.96514111.3773937 3.527899e-029.157786e-02
MA0090.3.TEAD1TEAD1 3.12310892.63673043.5007960 1.129212e-714.206315e-70
MA0809.2.TEAD4TEAD4 3.55785503.04956973.93849196.275334e-1005.843905e-98
MA0003.4.TFAP2ATFAP2A 0.81307370.64312980.9412571 9.676212e-019.781246e-01
MA0814.2.TFAP2C(var.2)TFAP2C(var.2)0.80257700.63722650.9467444 9.749408e-019.840183e-01
MA1123.2.TWIST1TWIST1 0.94064180.77096421.0774591 7.004148e-017.867569e-01
MA0093.3.USF1USF1 1.25487111.01645631.4568331 6.281181e-032.249750e-02
MA0526.3.USF2USF2 1.20867260.97983931.3944887 1.933289e-025.697977e-02
MA0748.2.YY2YY2 1.32198121.06213081.5250619 9.349956e-043.827317e-03
MA0528.2.ZNF263ZNF263 1.04095950.77804231.2858195 3.192716e-014.559017e-01
MA0609.2.CREMCREM 1.33808101.05760881.5691542 5.645780e-042.362981e-03
\n" + ], + "text/latex": [ + "A data.frame: 746 × 6\n", + "\\begin{tabular}{r|llllll}\n", + " & name & variability & bootstrap\\_lower\\_bound & bootstrap\\_upper\\_bound & p\\_value & p\\_value\\_adj\\\\\n", + " & & & & & & \\\\\n", + "\\hline\n", + "\tMA0004.1.Arnt & Arnt & 1.3097587 & 1.0574367 & 1.539051 & 1.354635e-03 & 5.368102e-03\\\\\n", + "\tMA0006.1.Ahr::Arnt & Ahr::Arnt & 1.6408099 & 1.3310370 & 1.897313 & 1.558287e-09 & 1.055386e-08\\\\\n", + "\tMA0019.1.Ddit3::Cebpa & Ddit3::Cebpa & 0.9217392 & 0.7530152 & 1.071486 & 7.633490e-01 & 8.387992e-01\\\\\n", + "\tMA0029.1.Mecom & Mecom & 1.1118984 & 0.9152195 & 1.279191 & 1.241110e-01 & 2.357689e-01\\\\\n", + "\tMA0030.1.FOXF2 & FOXF2 & 1.2476069 & 0.9803963 & 1.488305 & 7.572098e-03 & 2.648457e-02\\\\\n", + "\tMA0031.1.FOXD1 & FOXD1 & 1.4145492 & 1.1059477 & 1.689789 & 4.012849e-05 & 2.019982e-04\\\\\n", + "\tMA0040.1.Foxq1 & Foxq1 & 0.8891548 & 0.6866020 & 1.061669 & 8.532680e-01 & 9.066387e-01\\\\\n", + "\tMA0041.1.Foxd3 & Foxd3 & 1.0888796 & 0.8984521 & 1.242433 & 1.751568e-01 & 3.041767e-01\\\\\n", + "\tMA0051.1.IRF2 & IRF2 & 2.3429654 & 1.9984346 & 2.636799 & 3.956742e-32 & 5.458838e-31\\\\\n", + "\tMA0057.1.MZF1(var.2) & MZF1(var.2) & 1.4374855 & 1.1387447 & 1.683377 & 1.677594e-05 & 8.801459e-05\\\\\n", + "\tMA0059.1.MAX::MYC & MAX::MYC & 1.1983949 & 0.9579609 & 1.404083 & 2.431499e-02 & 6.718341e-02\\\\\n", + "\tMA0066.1.PPARG & PPARG & 1.1967799 & 0.9631023 & 1.386054 & 2.518961e-02 & 6.875999e-02\\\\\n", + "\tMA0067.1.Pax2 & Pax2 & 1.4826814 & 1.1795458 & 1.754515 & 2.707316e-06 & 1.516504e-05\\\\\n", + "\tMA0069.1.PAX6 & PAX6 & 1.4336932 & 1.1428848 & 1.691456 & 1.942653e-05 & 9.912854e-05\\\\\n", + "\tMA0070.1.PBX1 & PBX1 & 0.9577791 & 0.7763940 & 1.093432 & 6.379969e-01 & 7.364272e-01\\\\\n", + "\tMA0071.1.RORA & RORA & 1.2462905 & 1.0000593 & 1.443693 & 7.829811e-03 & 2.713121e-02\\\\\n", + "\tMA0072.1.RORA(var.2) & RORA(var.2) & 1.1428310 & 0.8985593 & 1.374539 & 7.366332e-02 & 1.609360e-01\\\\\n", + "\tMA0073.1.RREB1 & RREB1 & 1.0783099 & 0.7360707 & 1.443726 & 2.026617e-01 & 3.370155e-01\\\\\n", + "\tMA0074.1.RXRA::VDR & RXRA::VDR & 1.0345633 & 0.8487887 & 1.191487 & 3.419100e-01 & 4.770092e-01\\\\\n", + "\tMA0077.1.SOX9 & SOX9 & 1.8829136 & 1.5388508 & 2.169672 & 7.468473e-16 & 5.982809e-15\\\\\n", + "\tMA0078.1.Sox17 & Sox17 & 1.2980537 & 1.0309219 & 1.543146 & 1.912968e-03 & 7.346191e-03\\\\\n", + "\tMA0084.1.SRY & SRY & 1.4727160 & 1.1275108 & 1.750294 & 4.096331e-06 & 2.260568e-05\\\\\n", + "\tMA0087.1.Sox5 & Sox5 & NaN & NA & NA & NaN & NaN\\\\\n", + "\tMA0091.1.TAL1::TCF3 & TAL1::TCF3 & 0.9629422 & 0.7750211 & 1.121362 & 6.184271e-01 & 7.249186e-01\\\\\n", + "\tMA0092.1.Hand1::Tcf3 & Hand1::Tcf3 & 1.0519293 & 0.8265761 & 1.245390 & 2.821033e-01 & 4.220220e-01\\\\\n", + "\tMA0101.1.REL & REL & 2.2004414 & 1.5577992 & 2.741864 & 1.653870e-26 & 1.866869e-25\\\\\n", + "\tMA0107.1.RELA & RELA & 2.2335370 & 1.5232395 & 2.800979 & 9.093856e-28 & 1.058582e-26\\\\\n", + "\tMA0108.2.TBP & TBP & 1.1263857 & 0.9200347 & 1.301239 & 9.803119e-02 & 1.964982e-01\\\\\n", + "\tMA0109.1.HLTF & HLTF & 1.1624942 & 0.9292081 & 1.359212 & 5.103551e-02 & 1.234463e-01\\\\\n", + "\tMA0111.1.Spz1 & Spz1 & 0.8976106 & 0.7299020 & 1.047479 & 8.323799e-01 & 8.935491e-01\\\\\n", + "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\tMA0679.2.ONECUT1 & ONECUT1 & 0.8894337 & 0.7227464 & 1.0249286 & 8.526071e-01 & 9.066387e-01\\\\\n", + "\tMA0712.2.OTX2 & OTX2 & 1.5633602 & 1.2064328 & 1.8649122 & 7.404703e-08 & 4.635717e-07\\\\\n", + "\tMA1113.2.PBX2 & PBX2 & 1.0510919 & 0.8668876 & 1.2115406 & 2.848616e-01 & 4.244438e-01\\\\\n", + "\tMA0681.2.PHOX2B & PHOX2B & 1.0400674 & 0.7683239 & 1.3356516 & 3.223884e-01 & 4.592340e-01\\\\\n", + "\tMA0782.2.PKNOX1 & PKNOX1 & 1.0796060 & 0.8164435 & 1.2877377 & 1.991517e-01 & 3.326637e-01\\\\\n", + "\tMA0627.2.POU2F3 & POU2F3 & 1.7057563 & 1.4029532 & 1.9598849 & 4.527450e-11 & 3.274709e-10\\\\\n", + "\tMA0508.3.PRDM1 & PRDM1 & 0.9722230 & 0.7804626 & 1.1350415 & 5.826023e-01 & 6.966913e-01\\\\\n", + "\tMA0509.2.RFX1 & RFX1 & 1.2813608 & 1.0344486 & 1.5091161 & 3.077153e-03 & 1.134890e-02\\\\\n", + "\tMA0798.2.RFX3 & RFX3 & 1.3182716 & 1.0365162 & 1.5886845 & 1.047523e-03 & 4.218404e-03\\\\\n", + "\tMA0684.2.RUNX3 & RUNX3 & 2.2404520 & 1.8199690 & 2.5792896 & 4.920857e-28 & 5.912965e-27\\\\\n", + "\tMA0743.2.SCRT1 & SCRT1 & 1.2304555 & 0.9844128 & 1.4231488 & 1.159746e-02 & 3.710223e-02\\\\\n", + "\tMA0744.2.SCRT2 & SCRT2 & 1.0272360 & 0.8191767 & 1.2316353 & 3.686143e-01 & 5.057415e-01\\\\\n", + "\tMA0745.2.SNAI2 & SNAI2 & 1.2086332 & 0.9107771 & 1.5099362 & 1.935018e-02 & 5.697977e-02\\\\\n", + "\tMA0143.4.SOX2 & SOX2 & 1.6525634 & 1.3749170 & 1.8790048 & 8.381000e-10 & 5.781338e-09\\\\\n", + "\tMA0080.5.SPI1 & SPI1 & 2.7015671 & 2.1644450 & 3.1571455 & 1.716122e-48 & 3.118320e-47\\\\\n", + "\tMA0081.2.SPIB & SPIB & 3.2831731 & 2.7004591 & 3.7564744 & 1.438463e-81 & 7.144367e-80\\\\\n", + "\tMA0829.2.SREBF1(var.2) & SREBF1(var.2) & 1.0924107 & 0.8369711 & 1.3067737 & 1.665396e-01 & 2.940096e-01\\\\\n", + "\tMA0522.3.TCF3 & TCF3 & 1.1897302 & 0.9196730 & 1.4171982 & 2.932678e-02 & 7.830986e-02\\\\\n", + "\tMA0830.2.TCF4 & TCF4 & 1.1934659 & 0.9414800 & 1.4157420 & 2.706830e-02 & 7.306480e-02\\\\\n", + "\tMA0769.2.TCF7 & TCF7 & 1.1809318 & 0.9651411 & 1.3773937 & 3.527899e-02 & 9.157786e-02\\\\\n", + "\tMA0090.3.TEAD1 & TEAD1 & 3.1231089 & 2.6367304 & 3.5007960 & 1.129212e-71 & 4.206315e-70\\\\\n", + "\tMA0809.2.TEAD4 & TEAD4 & 3.5578550 & 3.0495697 & 3.9384919 & 6.275334e-100 & 5.843905e-98\\\\\n", + "\tMA0003.4.TFAP2A & TFAP2A & 0.8130737 & 0.6431298 & 0.9412571 & 9.676212e-01 & 9.781246e-01\\\\\n", + "\tMA0814.2.TFAP2C(var.2) & TFAP2C(var.2) & 0.8025770 & 0.6372265 & 0.9467444 & 9.749408e-01 & 9.840183e-01\\\\\n", + "\tMA1123.2.TWIST1 & TWIST1 & 0.9406418 & 0.7709642 & 1.0774591 & 7.004148e-01 & 7.867569e-01\\\\\n", + "\tMA0093.3.USF1 & USF1 & 1.2548711 & 1.0164563 & 1.4568331 & 6.281181e-03 & 2.249750e-02\\\\\n", + "\tMA0526.3.USF2 & USF2 & 1.2086726 & 0.9798393 & 1.3944887 & 1.933289e-02 & 5.697977e-02\\\\\n", + "\tMA0748.2.YY2 & YY2 & 1.3219812 & 1.0621308 & 1.5250619 & 9.349956e-04 & 3.827317e-03\\\\\n", + "\tMA0528.2.ZNF263 & ZNF263 & 1.0409595 & 0.7780423 & 1.2858195 & 3.192716e-01 & 4.559017e-01\\\\\n", + "\tMA0609.2.CREM & CREM & 1.3380810 & 1.0576088 & 1.5691542 & 5.645780e-04 & 2.362981e-03\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 746 × 6\n", + "\n", + "| | name <chr> | variability <dbl> | bootstrap_lower_bound <dbl> | bootstrap_upper_bound <dbl> | p_value <dbl> | p_value_adj <dbl> |\n", + "|---|---|---|---|---|---|---|\n", + "| MA0004.1.Arnt | Arnt | 1.3097587 | 1.0574367 | 1.539051 | 1.354635e-03 | 5.368102e-03 |\n", + "| MA0006.1.Ahr::Arnt | Ahr::Arnt | 1.6408099 | 1.3310370 | 1.897313 | 1.558287e-09 | 1.055386e-08 |\n", + "| MA0019.1.Ddit3::Cebpa | Ddit3::Cebpa | 0.9217392 | 0.7530152 | 1.071486 | 7.633490e-01 | 8.387992e-01 |\n", + "| MA0029.1.Mecom | Mecom | 1.1118984 | 0.9152195 | 1.279191 | 1.241110e-01 | 2.357689e-01 |\n", + "| MA0030.1.FOXF2 | FOXF2 | 1.2476069 | 0.9803963 | 1.488305 | 7.572098e-03 | 2.648457e-02 |\n", + "| MA0031.1.FOXD1 | FOXD1 | 1.4145492 | 1.1059477 | 1.689789 | 4.012849e-05 | 2.019982e-04 |\n", + "| MA0040.1.Foxq1 | Foxq1 | 0.8891548 | 0.6866020 | 1.061669 | 8.532680e-01 | 9.066387e-01 |\n", + "| MA0041.1.Foxd3 | Foxd3 | 1.0888796 | 0.8984521 | 1.242433 | 1.751568e-01 | 3.041767e-01 |\n", + "| MA0051.1.IRF2 | IRF2 | 2.3429654 | 1.9984346 | 2.636799 | 3.956742e-32 | 5.458838e-31 |\n", + "| MA0057.1.MZF1(var.2) | MZF1(var.2) | 1.4374855 | 1.1387447 | 1.683377 | 1.677594e-05 | 8.801459e-05 |\n", + "| MA0059.1.MAX::MYC | MAX::MYC | 1.1983949 | 0.9579609 | 1.404083 | 2.431499e-02 | 6.718341e-02 |\n", + "| MA0066.1.PPARG | PPARG | 1.1967799 | 0.9631023 | 1.386054 | 2.518961e-02 | 6.875999e-02 |\n", + "| MA0067.1.Pax2 | Pax2 | 1.4826814 | 1.1795458 | 1.754515 | 2.707316e-06 | 1.516504e-05 |\n", + "| MA0069.1.PAX6 | PAX6 | 1.4336932 | 1.1428848 | 1.691456 | 1.942653e-05 | 9.912854e-05 |\n", + "| MA0070.1.PBX1 | PBX1 | 0.9577791 | 0.7763940 | 1.093432 | 6.379969e-01 | 7.364272e-01 |\n", + "| MA0071.1.RORA | RORA | 1.2462905 | 1.0000593 | 1.443693 | 7.829811e-03 | 2.713121e-02 |\n", + "| MA0072.1.RORA(var.2) | RORA(var.2) | 1.1428310 | 0.8985593 | 1.374539 | 7.366332e-02 | 1.609360e-01 |\n", + "| MA0073.1.RREB1 | RREB1 | 1.0783099 | 0.7360707 | 1.443726 | 2.026617e-01 | 3.370155e-01 |\n", + "| MA0074.1.RXRA::VDR | RXRA::VDR | 1.0345633 | 0.8487887 | 1.191487 | 3.419100e-01 | 4.770092e-01 |\n", + "| MA0077.1.SOX9 | SOX9 | 1.8829136 | 1.5388508 | 2.169672 | 7.468473e-16 | 5.982809e-15 |\n", + "| MA0078.1.Sox17 | Sox17 | 1.2980537 | 1.0309219 | 1.543146 | 1.912968e-03 | 7.346191e-03 |\n", + "| MA0084.1.SRY | SRY | 1.4727160 | 1.1275108 | 1.750294 | 4.096331e-06 | 2.260568e-05 |\n", + "| MA0087.1.Sox5 | Sox5 | NaN | NA | NA | NaN | NaN |\n", + "| MA0091.1.TAL1::TCF3 | TAL1::TCF3 | 0.9629422 | 0.7750211 | 1.121362 | 6.184271e-01 | 7.249186e-01 |\n", + "| MA0092.1.Hand1::Tcf3 | Hand1::Tcf3 | 1.0519293 | 0.8265761 | 1.245390 | 2.821033e-01 | 4.220220e-01 |\n", + "| MA0101.1.REL | REL | 2.2004414 | 1.5577992 | 2.741864 | 1.653870e-26 | 1.866869e-25 |\n", + "| MA0107.1.RELA | RELA | 2.2335370 | 1.5232395 | 2.800979 | 9.093856e-28 | 1.058582e-26 |\n", + "| MA0108.2.TBP | TBP | 1.1263857 | 0.9200347 | 1.301239 | 9.803119e-02 | 1.964982e-01 |\n", + "| MA0109.1.HLTF | HLTF | 1.1624942 | 0.9292081 | 1.359212 | 5.103551e-02 | 1.234463e-01 |\n", + "| MA0111.1.Spz1 | Spz1 | 0.8976106 | 0.7299020 | 1.047479 | 8.323799e-01 | 8.935491e-01 |\n", + "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", + "| MA0679.2.ONECUT1 | ONECUT1 | 0.8894337 | 0.7227464 | 1.0249286 | 8.526071e-01 | 9.066387e-01 |\n", + "| MA0712.2.OTX2 | OTX2 | 1.5633602 | 1.2064328 | 1.8649122 | 7.404703e-08 | 4.635717e-07 |\n", + "| MA1113.2.PBX2 | PBX2 | 1.0510919 | 0.8668876 | 1.2115406 | 2.848616e-01 | 4.244438e-01 |\n", + "| MA0681.2.PHOX2B | PHOX2B | 1.0400674 | 0.7683239 | 1.3356516 | 3.223884e-01 | 4.592340e-01 |\n", + "| MA0782.2.PKNOX1 | PKNOX1 | 1.0796060 | 0.8164435 | 1.2877377 | 1.991517e-01 | 3.326637e-01 |\n", + "| MA0627.2.POU2F3 | POU2F3 | 1.7057563 | 1.4029532 | 1.9598849 | 4.527450e-11 | 3.274709e-10 |\n", + "| MA0508.3.PRDM1 | PRDM1 | 0.9722230 | 0.7804626 | 1.1350415 | 5.826023e-01 | 6.966913e-01 |\n", + "| MA0509.2.RFX1 | RFX1 | 1.2813608 | 1.0344486 | 1.5091161 | 3.077153e-03 | 1.134890e-02 |\n", + "| MA0798.2.RFX3 | RFX3 | 1.3182716 | 1.0365162 | 1.5886845 | 1.047523e-03 | 4.218404e-03 |\n", + "| MA0684.2.RUNX3 | RUNX3 | 2.2404520 | 1.8199690 | 2.5792896 | 4.920857e-28 | 5.912965e-27 |\n", + "| MA0743.2.SCRT1 | SCRT1 | 1.2304555 | 0.9844128 | 1.4231488 | 1.159746e-02 | 3.710223e-02 |\n", + "| MA0744.2.SCRT2 | SCRT2 | 1.0272360 | 0.8191767 | 1.2316353 | 3.686143e-01 | 5.057415e-01 |\n", + "| MA0745.2.SNAI2 | SNAI2 | 1.2086332 | 0.9107771 | 1.5099362 | 1.935018e-02 | 5.697977e-02 |\n", + "| MA0143.4.SOX2 | SOX2 | 1.6525634 | 1.3749170 | 1.8790048 | 8.381000e-10 | 5.781338e-09 |\n", + "| MA0080.5.SPI1 | SPI1 | 2.7015671 | 2.1644450 | 3.1571455 | 1.716122e-48 | 3.118320e-47 |\n", + "| MA0081.2.SPIB | SPIB | 3.2831731 | 2.7004591 | 3.7564744 | 1.438463e-81 | 7.144367e-80 |\n", + "| MA0829.2.SREBF1(var.2) | SREBF1(var.2) | 1.0924107 | 0.8369711 | 1.3067737 | 1.665396e-01 | 2.940096e-01 |\n", + "| MA0522.3.TCF3 | TCF3 | 1.1897302 | 0.9196730 | 1.4171982 | 2.932678e-02 | 7.830986e-02 |\n", + "| MA0830.2.TCF4 | TCF4 | 1.1934659 | 0.9414800 | 1.4157420 | 2.706830e-02 | 7.306480e-02 |\n", + "| MA0769.2.TCF7 | TCF7 | 1.1809318 | 0.9651411 | 1.3773937 | 3.527899e-02 | 9.157786e-02 |\n", + "| MA0090.3.TEAD1 | TEAD1 | 3.1231089 | 2.6367304 | 3.5007960 | 1.129212e-71 | 4.206315e-70 |\n", + "| MA0809.2.TEAD4 | TEAD4 | 3.5578550 | 3.0495697 | 3.9384919 | 6.275334e-100 | 5.843905e-98 |\n", + "| MA0003.4.TFAP2A | TFAP2A | 0.8130737 | 0.6431298 | 0.9412571 | 9.676212e-01 | 9.781246e-01 |\n", + "| MA0814.2.TFAP2C(var.2) | TFAP2C(var.2) | 0.8025770 | 0.6372265 | 0.9467444 | 9.749408e-01 | 9.840183e-01 |\n", + "| MA1123.2.TWIST1 | TWIST1 | 0.9406418 | 0.7709642 | 1.0774591 | 7.004148e-01 | 7.867569e-01 |\n", + "| MA0093.3.USF1 | USF1 | 1.2548711 | 1.0164563 | 1.4568331 | 6.281181e-03 | 2.249750e-02 |\n", + "| MA0526.3.USF2 | USF2 | 1.2086726 | 0.9798393 | 1.3944887 | 1.933289e-02 | 5.697977e-02 |\n", + "| MA0748.2.YY2 | YY2 | 1.3219812 | 1.0621308 | 1.5250619 | 9.349956e-04 | 3.827317e-03 |\n", + "| MA0528.2.ZNF263 | ZNF263 | 1.0409595 | 0.7780423 | 1.2858195 | 3.192716e-01 | 4.559017e-01 |\n", + "| MA0609.2.CREM | CREM | 1.3380810 | 1.0576088 | 1.5691542 | 5.645780e-04 | 2.362981e-03 |\n", + "\n" + ], + "text/plain": [ + " name variability bootstrap_lower_bound\n", + "MA0004.1.Arnt Arnt 1.3097587 1.0574367 \n", + "MA0006.1.Ahr::Arnt Ahr::Arnt 1.6408099 1.3310370 \n", + "MA0019.1.Ddit3::Cebpa Ddit3::Cebpa 0.9217392 0.7530152 \n", + "MA0029.1.Mecom Mecom 1.1118984 0.9152195 \n", + "MA0030.1.FOXF2 FOXF2 1.2476069 0.9803963 \n", + "MA0031.1.FOXD1 FOXD1 1.4145492 1.1059477 \n", + "MA0040.1.Foxq1 Foxq1 0.8891548 0.6866020 \n", + "MA0041.1.Foxd3 Foxd3 1.0888796 0.8984521 \n", + "MA0051.1.IRF2 IRF2 2.3429654 1.9984346 \n", + "MA0057.1.MZF1(var.2) MZF1(var.2) 1.4374855 1.1387447 \n", + "MA0059.1.MAX::MYC MAX::MYC 1.1983949 0.9579609 \n", + "MA0066.1.PPARG PPARG 1.1967799 0.9631023 \n", + "MA0067.1.Pax2 Pax2 1.4826814 1.1795458 \n", + "MA0069.1.PAX6 PAX6 1.4336932 1.1428848 \n", + "MA0070.1.PBX1 PBX1 0.9577791 0.7763940 \n", + "MA0071.1.RORA RORA 1.2462905 1.0000593 \n", + "MA0072.1.RORA(var.2) RORA(var.2) 1.1428310 0.8985593 \n", + "MA0073.1.RREB1 RREB1 1.0783099 0.7360707 \n", + "MA0074.1.RXRA::VDR RXRA::VDR 1.0345633 0.8487887 \n", + "MA0077.1.SOX9 SOX9 1.8829136 1.5388508 \n", + "MA0078.1.Sox17 Sox17 1.2980537 1.0309219 \n", + "MA0084.1.SRY SRY 1.4727160 1.1275108 \n", + "MA0087.1.Sox5 Sox5 NaN NA \n", + "MA0091.1.TAL1::TCF3 TAL1::TCF3 0.9629422 0.7750211 \n", + "MA0092.1.Hand1::Tcf3 Hand1::Tcf3 1.0519293 0.8265761 \n", + "MA0101.1.REL REL 2.2004414 1.5577992 \n", + "MA0107.1.RELA RELA 2.2335370 1.5232395 \n", + "MA0108.2.TBP TBP 1.1263857 0.9200347 \n", + "MA0109.1.HLTF HLTF 1.1624942 0.9292081 \n", + "MA0111.1.Spz1 Spz1 0.8976106 0.7299020 \n", + "⋮ ⋮ ⋮ ⋮ \n", + "MA0679.2.ONECUT1 ONECUT1 0.8894337 0.7227464 \n", + "MA0712.2.OTX2 OTX2 1.5633602 1.2064328 \n", + "MA1113.2.PBX2 PBX2 1.0510919 0.8668876 \n", + "MA0681.2.PHOX2B PHOX2B 1.0400674 0.7683239 \n", + "MA0782.2.PKNOX1 PKNOX1 1.0796060 0.8164435 \n", + "MA0627.2.POU2F3 POU2F3 1.7057563 1.4029532 \n", + "MA0508.3.PRDM1 PRDM1 0.9722230 0.7804626 \n", + "MA0509.2.RFX1 RFX1 1.2813608 1.0344486 \n", + "MA0798.2.RFX3 RFX3 1.3182716 1.0365162 \n", + "MA0684.2.RUNX3 RUNX3 2.2404520 1.8199690 \n", + "MA0743.2.SCRT1 SCRT1 1.2304555 0.9844128 \n", + "MA0744.2.SCRT2 SCRT2 1.0272360 0.8191767 \n", + "MA0745.2.SNAI2 SNAI2 1.2086332 0.9107771 \n", + "MA0143.4.SOX2 SOX2 1.6525634 1.3749170 \n", + "MA0080.5.SPI1 SPI1 2.7015671 2.1644450 \n", + "MA0081.2.SPIB SPIB 3.2831731 2.7004591 \n", + "MA0829.2.SREBF1(var.2) SREBF1(var.2) 1.0924107 0.8369711 \n", + "MA0522.3.TCF3 TCF3 1.1897302 0.9196730 \n", + "MA0830.2.TCF4 TCF4 1.1934659 0.9414800 \n", + "MA0769.2.TCF7 TCF7 1.1809318 0.9651411 \n", + "MA0090.3.TEAD1 TEAD1 3.1231089 2.6367304 \n", + "MA0809.2.TEAD4 TEAD4 3.5578550 3.0495697 \n", + "MA0003.4.TFAP2A TFAP2A 0.8130737 0.6431298 \n", + "MA0814.2.TFAP2C(var.2) TFAP2C(var.2) 0.8025770 0.6372265 \n", + "MA1123.2.TWIST1 TWIST1 0.9406418 0.7709642 \n", + "MA0093.3.USF1 USF1 1.2548711 1.0164563 \n", + "MA0526.3.USF2 USF2 1.2086726 0.9798393 \n", + "MA0748.2.YY2 YY2 1.3219812 1.0621308 \n", + "MA0528.2.ZNF263 ZNF263 1.0409595 0.7780423 \n", + "MA0609.2.CREM CREM 1.3380810 1.0576088 \n", + " bootstrap_upper_bound p_value p_value_adj \n", + "MA0004.1.Arnt 1.539051 1.354635e-03 5.368102e-03\n", + "MA0006.1.Ahr::Arnt 1.897313 1.558287e-09 1.055386e-08\n", + "MA0019.1.Ddit3::Cebpa 1.071486 7.633490e-01 8.387992e-01\n", + "MA0029.1.Mecom 1.279191 1.241110e-01 2.357689e-01\n", + "MA0030.1.FOXF2 1.488305 7.572098e-03 2.648457e-02\n", + "MA0031.1.FOXD1 1.689789 4.012849e-05 2.019982e-04\n", + "MA0040.1.Foxq1 1.061669 8.532680e-01 9.066387e-01\n", + "MA0041.1.Foxd3 1.242433 1.751568e-01 3.041767e-01\n", + "MA0051.1.IRF2 2.636799 3.956742e-32 5.458838e-31\n", + "MA0057.1.MZF1(var.2) 1.683377 1.677594e-05 8.801459e-05\n", + "MA0059.1.MAX::MYC 1.404083 2.431499e-02 6.718341e-02\n", + "MA0066.1.PPARG 1.386054 2.518961e-02 6.875999e-02\n", + "MA0067.1.Pax2 1.754515 2.707316e-06 1.516504e-05\n", + "MA0069.1.PAX6 1.691456 1.942653e-05 9.912854e-05\n", + "MA0070.1.PBX1 1.093432 6.379969e-01 7.364272e-01\n", + "MA0071.1.RORA 1.443693 7.829811e-03 2.713121e-02\n", + "MA0072.1.RORA(var.2) 1.374539 7.366332e-02 1.609360e-01\n", + "MA0073.1.RREB1 1.443726 2.026617e-01 3.370155e-01\n", + "MA0074.1.RXRA::VDR 1.191487 3.419100e-01 4.770092e-01\n", + "MA0077.1.SOX9 2.169672 7.468473e-16 5.982809e-15\n", + "MA0078.1.Sox17 1.543146 1.912968e-03 7.346191e-03\n", + "MA0084.1.SRY 1.750294 4.096331e-06 2.260568e-05\n", + "MA0087.1.Sox5 NA NaN NaN\n", + "MA0091.1.TAL1::TCF3 1.121362 6.184271e-01 7.249186e-01\n", + "MA0092.1.Hand1::Tcf3 1.245390 2.821033e-01 4.220220e-01\n", + "MA0101.1.REL 2.741864 1.653870e-26 1.866869e-25\n", + "MA0107.1.RELA 2.800979 9.093856e-28 1.058582e-26\n", + "MA0108.2.TBP 1.301239 9.803119e-02 1.964982e-01\n", + "MA0109.1.HLTF 1.359212 5.103551e-02 1.234463e-01\n", + "MA0111.1.Spz1 1.047479 8.323799e-01 8.935491e-01\n", + "⋮ ⋮ ⋮ ⋮ \n", + "MA0679.2.ONECUT1 1.0249286 8.526071e-01 9.066387e-01\n", + "MA0712.2.OTX2 1.8649122 7.404703e-08 4.635717e-07\n", + "MA1113.2.PBX2 1.2115406 2.848616e-01 4.244438e-01\n", + "MA0681.2.PHOX2B 1.3356516 3.223884e-01 4.592340e-01\n", + "MA0782.2.PKNOX1 1.2877377 1.991517e-01 3.326637e-01\n", + "MA0627.2.POU2F3 1.9598849 4.527450e-11 3.274709e-10\n", + "MA0508.3.PRDM1 1.1350415 5.826023e-01 6.966913e-01\n", + "MA0509.2.RFX1 1.5091161 3.077153e-03 1.134890e-02\n", + "MA0798.2.RFX3 1.5886845 1.047523e-03 4.218404e-03\n", + "MA0684.2.RUNX3 2.5792896 4.920857e-28 5.912965e-27\n", + "MA0743.2.SCRT1 1.4231488 1.159746e-02 3.710223e-02\n", + "MA0744.2.SCRT2 1.2316353 3.686143e-01 5.057415e-01\n", + "MA0745.2.SNAI2 1.5099362 1.935018e-02 5.697977e-02\n", + "MA0143.4.SOX2 1.8790048 8.381000e-10 5.781338e-09\n", + "MA0080.5.SPI1 3.1571455 1.716122e-48 3.118320e-47\n", + "MA0081.2.SPIB 3.7564744 1.438463e-81 7.144367e-80\n", + "MA0829.2.SREBF1(var.2) 1.3067737 1.665396e-01 2.940096e-01\n", + "MA0522.3.TCF3 1.4171982 2.932678e-02 7.830986e-02\n", + "MA0830.2.TCF4 1.4157420 2.706830e-02 7.306480e-02\n", + "MA0769.2.TCF7 1.3773937 3.527899e-02 9.157786e-02\n", + "MA0090.3.TEAD1 3.5007960 1.129212e-71 4.206315e-70\n", + "MA0809.2.TEAD4 3.9384919 6.275334e-100 5.843905e-98\n", + "MA0003.4.TFAP2A 0.9412571 9.676212e-01 9.781246e-01\n", + "MA0814.2.TFAP2C(var.2) 0.9467444 9.749408e-01 9.840183e-01\n", + "MA1123.2.TWIST1 1.0774591 7.004148e-01 7.867569e-01\n", + "MA0093.3.USF1 1.4568331 6.281181e-03 2.249750e-02\n", + "MA0526.3.USF2 1.3944887 1.933289e-02 5.697977e-02\n", + "MA0748.2.YY2 1.5250619 9.349956e-04 3.827317e-03\n", + "MA0528.2.ZNF263 1.2858195 3.192716e-01 4.559017e-01\n", + "MA0609.2.CREM 1.5691542 5.645780e-04 2.362981e-03" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(variability)" + ] + }, { "cell_type": "markdown", "id": "10f44082-c5cc-42fd-a0a9-19c4e617ec51", diff --git a/docs/source/notebooks/compare_with_chromVAR.ipynb b/docs/source/notebooks/compare_with_chromVAR.ipynb index 65516b9..36b9dd7 100644 --- a/docs/source/notebooks/compare_with_chromVAR.ipynb +++ b/docs/source/notebooks/compare_with_chromVAR.ipynb @@ -5,7 +5,7 @@ "id": "33fe3950-e22b-4a28-a6dc-693fc6592dc4", "metadata": {}, "source": [ - "# Comparison between pychromVAR and chromVAR: Part II" + "# Compare with chromVAR: Part II" ] }, { @@ -50,7 +50,7 @@ { "data": { "text/plain": [ - "'0.0.2'" + "'0.0.3'" ] }, "execution_count": 2, @@ -92,6 +92,16 @@ "adata" ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0c5b5f7b-79d1-4e8d-bf09-b255c0d088dd", + "metadata": {}, + "outputs": [], + "source": [ + "adata.write_h5ad(\"example_data.h5ad\")" + ] + }, { "cell_type": "markdown", "id": "2bd28801-44b0-4a30-8bb3-230d1d33feff", @@ -1049,7 +1059,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/docs/source/notebooks/run_chromVAR.ipynb b/docs/source/notebooks/run_chromVAR.ipynb index bccfb71..4cc9c38 100644 --- a/docs/source/notebooks/run_chromVAR.ipynb +++ b/docs/source/notebooks/run_chromVAR.ipynb @@ -5,7 +5,7 @@ "id": "e8b8e7da-4243-4d58-a2f1-4b870ba031c1", "metadata": {}, "source": [ - "# Comparison between pychromVAR and chromVAR: Part I" + "# Compare with chromVAR: Part I" ] }, { @@ -259,6 +259,128 @@ "dev" ] }, + { + "cell_type": "markdown", + "id": "3ac3b08c-4737-450e-a8a2-4aec33f27714", + "metadata": {}, + "source": [ + "Plot variability" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "db6cf16a-e43f-4c88-89ca-14036497b01b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“\u001b[1m\u001b[22mRemoved 1 rows containing missing values (`geom_point()`).”\n" + ] + }, + { + "data": { + "image/png": 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p06ebT2Zmplx//fXypz/9STp37iyJiSHFXqE0h3tsJKC/D4cPH5Zb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\nh3lLVsqdMKvvVtSTNILFg1NFPz8pKqoTAREQge4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QuNBHyrWNersmCw7bQrnx+WTVw3IWxzTBNZw3fWy33XZ+vam4j1r2EUVY+ijES+Up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\njQAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "variability <- computeVariability(dev)\n", + "\n", + "plotVariability(variability, use_plotly = FALSE) " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ee292842-a736-458d-ac9a-50ac0da6b993", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 6
namevariabilitybootstrap_lower_boundbootstrap_upper_boundp_valuep_value_adj
<chr><dbl><dbl><dbl><dbl><dbl>
MA0004.1.ArntArnt 1.30975871.05743671.5390511.354635e-035.368102e-03
MA0006.1.Ahr::ArntAhr::Arnt 1.64080991.33103701.8973131.558287e-091.055386e-08
MA0019.1.Ddit3::CebpaDdit3::Cebpa0.92173920.75301521.0714867.633490e-018.387992e-01
MA0029.1.MecomMecom 1.11189840.91521951.2791911.241110e-012.357689e-01
MA0030.1.FOXF2FOXF2 1.24760690.98039631.4883057.572098e-032.648457e-02
MA0031.1.FOXD1FOXD1 1.41454921.10594771.6897894.012849e-052.019982e-04
\n" + ], + "text/latex": [ + "A data.frame: 6 × 6\n", + "\\begin{tabular}{r|llllll}\n", + " & name & variability & bootstrap\\_lower\\_bound & bootstrap\\_upper\\_bound & p\\_value & p\\_value\\_adj\\\\\n", + " & & & & & & \\\\\n", + "\\hline\n", + "\tMA0004.1.Arnt & Arnt & 1.3097587 & 1.0574367 & 1.539051 & 1.354635e-03 & 5.368102e-03\\\\\n", + "\tMA0006.1.Ahr::Arnt & Ahr::Arnt & 1.6408099 & 1.3310370 & 1.897313 & 1.558287e-09 & 1.055386e-08\\\\\n", + "\tMA0019.1.Ddit3::Cebpa & Ddit3::Cebpa & 0.9217392 & 0.7530152 & 1.071486 & 7.633490e-01 & 8.387992e-01\\\\\n", + "\tMA0029.1.Mecom & Mecom & 1.1118984 & 0.9152195 & 1.279191 & 1.241110e-01 & 2.357689e-01\\\\\n", + "\tMA0030.1.FOXF2 & FOXF2 & 1.2476069 & 0.9803963 & 1.488305 & 7.572098e-03 & 2.648457e-02\\\\\n", + "\tMA0031.1.FOXD1 & FOXD1 & 1.4145492 & 1.1059477 & 1.689789 & 4.012849e-05 & 2.019982e-04\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 6\n", + "\n", + "| | name <chr> | variability <dbl> | bootstrap_lower_bound <dbl> | bootstrap_upper_bound <dbl> | p_value <dbl> | p_value_adj <dbl> |\n", + "|---|---|---|---|---|---|---|\n", + "| MA0004.1.Arnt | Arnt | 1.3097587 | 1.0574367 | 1.539051 | 1.354635e-03 | 5.368102e-03 |\n", + "| MA0006.1.Ahr::Arnt | Ahr::Arnt | 1.6408099 | 1.3310370 | 1.897313 | 1.558287e-09 | 1.055386e-08 |\n", + "| MA0019.1.Ddit3::Cebpa | Ddit3::Cebpa | 0.9217392 | 0.7530152 | 1.071486 | 7.633490e-01 | 8.387992e-01 |\n", + "| MA0029.1.Mecom | Mecom | 1.1118984 | 0.9152195 | 1.279191 | 1.241110e-01 | 2.357689e-01 |\n", + "| MA0030.1.FOXF2 | FOXF2 | 1.2476069 | 0.9803963 | 1.488305 | 7.572098e-03 | 2.648457e-02 |\n", + "| MA0031.1.FOXD1 | FOXD1 | 1.4145492 | 1.1059477 | 1.689789 | 4.012849e-05 | 2.019982e-04 |\n", + "\n" + ], + "text/plain": [ + " name variability bootstrap_lower_bound\n", + "MA0004.1.Arnt Arnt 1.3097587 1.0574367 \n", + "MA0006.1.Ahr::Arnt Ahr::Arnt 1.6408099 1.3310370 \n", + "MA0019.1.Ddit3::Cebpa Ddit3::Cebpa 0.9217392 0.7530152 \n", + "MA0029.1.Mecom Mecom 1.1118984 0.9152195 \n", + "MA0030.1.FOXF2 FOXF2 1.2476069 0.9803963 \n", + "MA0031.1.FOXD1 FOXD1 1.4145492 1.1059477 \n", + " bootstrap_upper_bound p_value p_value_adj \n", + "MA0004.1.Arnt 1.539051 1.354635e-03 5.368102e-03\n", + "MA0006.1.Ahr::Arnt 1.897313 1.558287e-09 1.055386e-08\n", + "MA0019.1.Ddit3::Cebpa 1.071486 7.633490e-01 8.387992e-01\n", + "MA0029.1.Mecom 1.279191 1.241110e-01 2.357689e-01\n", + "MA0030.1.FOXF2 1.488305 7.572098e-03 2.648457e-02\n", + "MA0031.1.FOXD1 1.689789 4.012849e-05 2.019982e-04" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(variability)" + ] + }, { "cell_type": "markdown", "id": "10f44082-c5cc-42fd-a0a9-19c4e617ec51", @@ -312,7 +434,7 @@ "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", - "version": "4.1.1" + "version": "4.1.3" } }, "nbformat": 4, diff --git a/pychromvar/__init__.py b/pychromvar/__init__.py index d63873f..d3f82c1 100644 --- a/pychromvar/__init__.py +++ b/pychromvar/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.0.3" +__version__ = "0.0.4" __version_info__ = tuple([int(num) for num in __version__.split('.')]) # noqa: F401 from .preprocessing import get_bg_peaks, add_gc_bias, add_peak_seq diff --git a/pychromvar/compute_deviations.py b/pychromvar/compute_deviations.py index c4629b4..ee9fdb3 100644 --- a/pychromvar/compute_deviations.py +++ b/pychromvar/compute_deviations.py @@ -49,7 +49,7 @@ def compute_deviations(data: Union[AnnData, MuData], n_jobs=-1) -> AnnData: logging.info('computing observed motif deviations...') motif_match = adata.varm['motif_match'].transpose() - obs_dev = _compute_dev( + obs_dev = _compute_deviations( (motif_match, adata.X.transpose(), expectation.transpose())).transpose() # compute background deviations for bias-correction @@ -67,7 +67,7 @@ def compute_deviations(data: Union[AnnData, MuData], n_jobs=-1) -> AnnData: bg_peak_idx = adata.varm['bg_peaks'][:, i] bg_motif_match = adata.varm['motif_match'][bg_peak_idx, :].transpose( ) - bg_dev[i, :, :] = _compute_dev((bg_motif_match, adata.X.transpose(), + bg_dev[i, :, :] = _compute_deviations((bg_motif_match, adata.X.transpose(), expectation.transpose())).transpose() elif n_jobs > 1: @@ -83,7 +83,7 @@ def compute_deviations(data: Union[AnnData, MuData], n_jobs=-1) -> AnnData: # run the function with multiple cpus with Pool(processes=n_jobs) as pool: - all_results = pool.map(_compute_dev, arguments_list) + all_results = pool.map(_compute_deviations, arguments_list) # parse the results for i in range(n_bg_peaks): @@ -102,7 +102,7 @@ def compute_deviations(data: Union[AnnData, MuData], n_jobs=-1) -> AnnData: return dev -def _compute_dev(arguments): +def _compute_deviations(arguments): motif_match, count, expectation = arguments observed = np.dot(motif_match, count) @@ -134,3 +134,4 @@ def compute_expectation(count: np.array) -> np.array: exp = np.dot(b, a) return exp + diff --git a/tests/test_compute_deviations.py b/tests/test_compute_deviations.py index 176834a..5b679d3 100644 --- a/tests/test_compute_deviations.py +++ b/tests/test_compute_deviations.py @@ -1,7 +1,5 @@ import numpy as np -from anndata import AnnData - -from pychromvar.compute_deviations import compute_expectation, compute_deviations +from pychromvar.compute_deviations import compute_expectation, _compute_deviations def test_compute_expectation(): count = np.array([[1, 0, 1, ], [0, 1, 1]], dtype=np.float32) @@ -15,4 +13,14 @@ def test_compute_expectation(): def test_compute_deviations(): count = np.array([[1, 0, 1, ], [0, 1, 1]], dtype=np.float32) - data = AnnData(count) + exp = compute_expectation(count) + + motif_match = np.array([[1, 1], [0, 1], [1, 0]], dtype=np.int8) + + dev = _compute_deviations((motif_match, count, exp)).transpose() + + # make sure output has same dimensionas + assert dev.shape == (count.shape[1], motif_match.shape[0]) + + # check the output + assert np.array_equal(dev, np.array([[0.0, -1, 1], [0, 1, -1], [0, 0, 0]])) \ No newline at end of file diff --git a/tests/test_motif_match.py b/tests/test_motif_match.py deleted file mode 100644 index f8b3998..0000000 --- a/tests/test_motif_match.py +++ /dev/null @@ -1,4 +0,0 @@ -import MOODS.scan -import MOODS.tools -import MOODS.parsers - From 40fa5e0e53da4743b8cc8d2998f0cde7464b954e Mon Sep 17 00:00:00 2001 From: lzj1769 Date: Sat, 4 Feb 2023 18:04:05 -0500 Subject: [PATCH 3/6] update version --- pyproject.toml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index db2aa83..6878001 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "flit_core.buildapi" [project] name = "pychromvar" -version = "0.0.3" +version = "0.0.4" description = "A python package for chromVAR" authors = [ {name = "Zhijian Li", email = "lzj1769@gmail.com"} @@ -18,14 +18,13 @@ classifiers = [ "Topic :: Scientific/Engineering :: Bio-Informatics", "Intended Audience :: Science/Research" ] -requires-python = ">= 3.6" +requires-python = ">= 3.8" dependencies = [ "anndata", "numpy", "scipy", "mudata", "scanpy", - "scikit-learn", "muon", "biopython", "MOODS-python", From 38e969c3ab760a8206fe386300a8ab3c2b2c10cb Mon Sep 17 00:00:00 2001 From: lzj1769 Date: Sat, 4 Feb 2023 18:05:29 -0500 Subject: [PATCH 4/6] update version --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 6878001..0fcde63 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ classifiers = [ ] requires-python = ">= 3.8" dependencies = [ + "scikit-learn", "anndata", "numpy", "scipy", From f16b1d52f6a74f6d2fb86edec9b2decf7616dde2 Mon Sep 17 00:00:00 2001 From: lzj1769 Date: Sat, 4 Feb 2023 18:07:07 -0500 Subject: [PATCH 5/6] update action --- .github/workflows/python-package.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 10f5a47..e42cbf0 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -27,7 +27,7 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install flake8 pytest + python -m pip install flake8 pytest scikit-learn python -m pip install ./ - name: Lint with flake8 run: | From abeeb19ff6a62fae88088b494888c8844ce713e5 Mon Sep 17 00:00:00 2001 From: lzj1769 Date: Sat, 4 Feb 2023 18:09:18 -0500 Subject: [PATCH 6/6] update action --- .github/workflows/python-package.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index e42cbf0..e5cdfde 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -26,8 +26,9 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | + python -m pip install scikit-learn python -m pip install --upgrade pip - python -m pip install flake8 pytest scikit-learn + python -m pip install flake8 pytest python -m pip install ./ - name: Lint with flake8 run: |