{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import pandas as pd\n", "import scipy.stats as stats\n", "import scipy.cluster.hierarchy as clust\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "rcParams['pdf.fonttype']=42\n", "rcParams['font.size']=14\n", "rcParams['font.family']='arial'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def clean_axis(ax):\n", "\t#\n", "\t# for use, e.g., when plotting a dendrogram alongside a heatmap. removes border on a frame.\n", "\t#\n", " ax.get_xaxis().set_ticks([])\n", " ax.get_yaxis().set_ticks([])\n", " for sp in ax.spines.values():\n", " sp.set_visible(False)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "fc_file = './Data/fc_replicateMeans_12kPairs_annotated.txt'\n", "smf_file = './Data/smf-meanFC-meanRepl-841genes-3cells.txt'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 12328 entries, AARS.1:CDX4.2 to ZSWIM6.3:ZSWIM4.3\n", "Data columns (total 5 columns):\n", "A549 12328 non-null float64\n", "HT29 12328 non-null float64\n", "OVCAR8 12328 non-null float64\n", "GENE1 12328 non-null object\n", "GENE2 12328 non-null object\n", "dtypes: float64(3), object(2)\n", "memory usage: 577.9+ KB\n" ] } ], "source": [ "fc = pd.read_csv(fc_file, index_col=1, sep='\\t')\n", "fc.drop('GENE_CLONE', axis=1, inplace=True)\n", "fc.info()\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 841 entries, AARS to ZSWIM6\n", "Data columns (total 3 columns):\n", "A549 841 non-null float64\n", "HT29 841 non-null float64\n", "OVCAR8 841 non-null float64\n", "dtypes: float64(3)\n", "memory usage: 26.3+ KB\n" ] } ], "source": [ "smf = pd.read_csv(smf_file, index_col=0, sep='\\t')\n", "smf.info()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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A549HT29OVCAR8GENE1GENE2
GENE
AARS.1:CDX4.2-1.4753-2.7470-3.6317AARSCTRL
AARS.2:F13B.3-0.0967-0.6473-1.9210AARSCTRL
AARS.3:SPEM1.1-0.6510-2.8547-2.3557AARSCTRL
ABHD16A.1:GPX6.32.13702.16631.9050ABHD16ACTRL
ABHD16A.2:GSX2.30.22730.89330.1897ABHD16ACTRL
\n", "
" ], "text/plain": [ " A549 HT29 OVCAR8 GENE1 GENE2\n", "GENE \n", "AARS.1:CDX4.2 -1.4753 -2.7470 -3.6317 AARS CTRL\n", "AARS.2:F13B.3 -0.0967 -0.6473 -1.9210 AARS CTRL\n", "AARS.3:SPEM1.1 -0.6510 -2.8547 -2.3557 AARS CTRL\n", "ABHD16A.1:GPX6.3 2.1370 2.1663 1.9050 ABHD16A CTRL\n", "ABHD16A.2:GSX2.3 0.2273 0.8933 0.1897 ABHD16A CTRL" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fc.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Get gene pairs to test" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Gene1Gene2
0ABHD16AABHD16B
1ABHD4ABHD5
2ABL1ABL2
\n", "
" ], "text/plain": [ " Gene1 Gene2\n", "0 ABHD16A ABHD16B\n", "1 ABHD4 ABHD5\n", "2 ABL1 ABL2" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pairs = pd.read_csv('paralog-pairs-to-test.txt', sep='\\t')\n", "pairs.head(3)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 405 entries, 0 to 404\n", "Data columns (total 2 columns):\n", "Gene1 405 non-null object\n", "Gene2 405 non-null object\n", "dtypes: object(2)\n", "memory usage: 6.5+ KB\n" ] } ], "source": [ "pairs.info()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "## for each pair:\n", "# - get the SMF for each gene (pre-calculated)\n", "# - get the 18 experimental guide pairs; calc mean FC (per screen)\n", "# - calc dLFC = obs - exp, where exp = SMFa + SMFb" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
A549HT29OVCAR8
ABHD16A_ABHD16BNaNNaNNaN
ABHD4_ABHD5NaNNaNNaN
ABL1_ABL2NaNNaNNaN
\n", "
" ], "text/plain": [ " A549 HT29 OVCAR8\n", "ABHD16A_ABHD16B NaN NaN NaN\n", "ABHD4_ABHD5 NaN NaN NaN\n", "ABL1_ABL2 NaN NaN NaN" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dLFC = pd.DataFrame( index=list(pairs.Gene1 + \"_\" + pairs.Gene2), columns=fc.columns[:-2], dtype=float)\n", "dLFC.head(3)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "for pair_idx in pairs.index:\n", " g1 = pairs.loc[pair_idx].Gene1\n", " g2 = pairs.loc[pair_idx].Gene2\n", " expt_idx = list( where( ( (fc.GENE1==g1) & (fc.GENE2==g2) ) | ( (fc.GENE1==g2) & (fc.GENE2==g1) ) )[0] )\n", " if ( len(expt_idx)==0 ):\n", " continue\n", " smf_sum = smf.loc[g1] + smf.loc[g2]\n", " expt = fc.iloc[ expt_idx ]\n", " genepair = g1 + \"_\" + g2\n", " dLFC.loc[genepair] = expt.median(0) - smf_sum" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 405 entries, ABHD16A_ABHD16B to BRCA1_PARP1\n", "Data columns (total 3 columns):\n", "A549 403 non-null float64\n", "HT29 403 non-null float64\n", "OVCAR8 403 non-null float64\n", "dtypes: float64(3)\n", "memory usage: 32.7+ KB\n" ] } ], "source": [ "dLFC.info()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
A549HT29OVCAR8
ABHD16A_ABHD16BNaNNaNNaN
ABHD4_ABHD5-0.4526-0.29125-0.32735
ABL1_ABL2-0.19720.04810-0.28185
\n", "
" ], "text/plain": [ " A549 HT29 OVCAR8\n", "ABHD16A_ABHD16B NaN NaN NaN\n", "ABHD4_ABHD5 -0.4526 -0.29125 -0.32735\n", "ABL1_ABL2 -0.1972 0.04810 -0.28185" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dLFC.head(3)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 79])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dropme = where( isnan( dLFC.A549) )[0]\n", "dropme" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "dLFC.drop( dLFC.index[dropme], axis=0, inplace=True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 403 entries, ABHD4_ABHD5 to BRCA1_PARP1\n", "Data columns (total 3 columns):\n", "A549 403 non-null float64\n", "HT29 403 non-null float64\n", "OVCAR8 403 non-null float64\n", "dtypes: float64(3)\n", "memory usage: 12.6+ KB\n" ] } ], "source": [ "dLFC.info()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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A549HT29OVCAR8
ABHD4_ABHD5-0.45260-0.29125-0.32735
ABL1_ABL2-0.197200.04810-0.28185
ABR_BCR0.462500.008000.06230
ACAP2_ACAP30.759850.455900.43260
ACTG1_ACTR1B0.118701.016151.39085
ACVR2A_ACVR2B0.379500.22055-0.10560
AGAP3_AGAP10.099650.07555-0.21025
AGAP9_AGAP1-0.46095-0.19875-0.39190
AKT1_AKT2-0.28725-0.50425-0.05080
ALG10_ALG10B-0.31265-0.61960-0.51430
ANKRA2_RFXANK-0.28050-0.01850-0.02840
ANKRD13A_ANKRD13D-0.89030-0.30690-0.33330
ANXA11_ANXA70.373800.13735-0.18300
AP1B1_AP2B1-0.64650-1.68525-1.19670
AP2A1_AP2A2-0.11540-0.55630-0.70460
AP3M1_AP3M2-0.54945-0.78740-0.50735
AP3S1_AP3S2-0.23285-0.30805-0.31745
APPL1_APPL20.096150.04665-0.36800
ARFGEF1_ARFGEF2-1.08965-1.49845-1.70760
ARFIP1_ARFIP2-0.05735-0.06545-0.04315
ARHGEF3_NET1-0.25205-0.13080-0.18440
ARID1A_ARID1B-0.58695-0.30580-0.57885
ARL8A_ARL8B-1.13770-0.24350-0.40670
ARPC1A_ARPC1B-1.28710-1.07320-0.89225
ARPC5_ARPC5L-0.64265-0.63825-0.22715
ARPP19_ENSA-0.53210-0.52865-1.19380
ASAP1_ASAP20.085550.25185-0.01505
ASIC1_ASIC30.356650.00665-0.17980
ATAD2_ATAD2B-0.25985-0.41495-0.31420
ATAD3A_ATAD3B1.117300.190951.36380
............
ZKSCAN1_ZKSCAN8-0.24650-0.32645-0.35055
ZKSCAN2_ZSCAN290.05130-0.26945-0.12840
ZKSCAN3_ZKSCAN4-0.40255-0.46080-0.43245
ZMIZ1_ZMIZ2-0.48940-0.501500.12560
ZNF133_ZNF169-0.53190-0.43280-0.39845
ZNF136_ZNF6270.187800.00845-0.05350
ZNF143_ZNF760.23920-1.21190-0.67130
ZNF181_ZNF3020.19940-0.15945-0.18590
ZNF26_ZNF2680.549850.385800.22890
ZNF286A_ZNF286B0.23500-0.07060-0.33410
ZNF324_ZNF324B-0.302650.34245-0.14110
ZNF354A_ZNF354B-0.25400-0.19730-0.16720
ZNF419_ZNF773-0.39225-0.39750-0.33370
ZNF524_ZNF581-0.179750.09070-0.14850
ZNF552_ZNF587B-0.34685-0.49020-0.16190
ZNF555_ZNF77-0.124750.233600.03995
ZNF557_ZNF558-0.19755-0.12490-0.10705
ZNF561_ZNF562-0.21605-0.35210-0.37055
ZNF580_ZNF581-0.06525-0.114350.07270
ZNF600_ZNF6110.13875-0.07245-0.12495
ZNF619_ZNF620-0.12450-0.31520-0.24945
ZNF747_ZNF7640.30745-0.015450.02145
ZNF785_ZNF764-0.03785-0.08180-0.08780
ZSCAN21_ZSCAN30-0.10285-0.18455-0.18895
ZSWIM4_ZSWIM6-0.21985-0.57605-0.34765
SMARCA2_SMARCA4-0.18310-0.52960-0.10250
CDH1_CDH3-0.26155-0.50410-0.34885
ME2_ME3-0.00020-0.059150.06250
BCL2L1_MCL10.11360-1.00990-0.88675
BRCA1_PARP10.10210-0.21700-0.52850
\n", "

403 rows × 3 columns

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" ], "text/plain": [ " A549 HT29 OVCAR8\n", "ABHD4_ABHD5 -0.45260 -0.29125 -0.32735\n", "ABL1_ABL2 -0.19720 0.04810 -0.28185\n", "ABR_BCR 0.46250 0.00800 0.06230\n", "ACAP2_ACAP3 0.75985 0.45590 0.43260\n", "ACTG1_ACTR1B 0.11870 1.01615 1.39085\n", "... ... ... ...\n", "SMARCA2_SMARCA4 -0.18310 -0.52960 -0.10250\n", "CDH1_CDH3 -0.26155 -0.50410 -0.34885\n", "ME2_ME3 -0.00020 -0.05915 0.06250\n", "BCL2L1_MCL1 0.11360 -1.00990 -0.88675\n", "BRCA1_PARP1 0.10210 -0.21700 -0.52850\n", "\n", "[403 rows x 3 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dLFC.to_csv('./Data/dLFC_paralogs_403pairs_3cells.txt', sep='\\t', float_format='%4.3f', index=True)\n", "dLFC" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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A549HT29OVCAR8
TIA1_TIAL1-1.62485-3.32405-3.23630
DDX19A_DDX19B-2.60180-3.22315-2.83175
GSK3A_GSK3B-1.75395-2.97390-2.06015
CNOT7_CNOT8-2.13680-2.83030-3.01300
HSP90AA1_HSP90AB1-2.16825-2.53255-1.53250
PTP4A1_PTP4A2-2.20335-2.51500-1.45980
MAPK1_MAPK3-1.60590-2.46195-2.17540
CSNK2A1_CSNK2A2-1.44370-2.45460-1.78705
SAR1A_SAR1B-1.95895-2.44815-2.72920
HDAC1_HDAC2-0.95180-2.20855-2.56430
PAPOLA_PAPOLG-0.95935-2.09265-1.92265
CAPZA1_CAPZA2-1.78510-2.00670-1.75840
ATP6V0A1_ATP6V0A2-1.17755-1.84015-0.16175
AP1B1_AP2B1-0.64650-1.68525-1.19670
PITPNA_PITPNB-2.06670-1.63300-2.61710
SAFB_SAFB2-1.68270-1.54790-0.79835
ARFGEF1_ARFGEF2-1.08965-1.49845-1.70760
KDELR1_KDELR2-2.19785-1.46595-1.73045
DNAJA1_DNAJA2-1.46920-1.44810-1.52540
CSNK1D_CSNK1E-1.68675-1.44095-2.15925
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" ], "text/plain": [ " A549 HT29 OVCAR8\n", "TIA1_TIAL1 -1.62485 -3.32405 -3.23630\n", "DDX19A_DDX19B -2.60180 -3.22315 -2.83175\n", "GSK3A_GSK3B -1.75395 -2.97390 -2.06015\n", "CNOT7_CNOT8 -2.13680 -2.83030 -3.01300\n", "HSP90AA1_HSP90AB1 -2.16825 -2.53255 -1.53250\n", "PTP4A1_PTP4A2 -2.20335 -2.51500 -1.45980\n", "MAPK1_MAPK3 -1.60590 -2.46195 -2.17540\n", "CSNK2A1_CSNK2A2 -1.44370 -2.45460 -1.78705\n", "SAR1A_SAR1B -1.95895 -2.44815 -2.72920\n", "HDAC1_HDAC2 -0.95180 -2.20855 -2.56430\n", "PAPOLA_PAPOLG -0.95935 -2.09265 -1.92265\n", "CAPZA1_CAPZA2 -1.78510 -2.00670 -1.75840\n", "ATP6V0A1_ATP6V0A2 -1.17755 -1.84015 -0.16175\n", "AP1B1_AP2B1 -0.64650 -1.68525 -1.19670\n", "PITPNA_PITPNB -2.06670 -1.63300 -2.61710\n", "SAFB_SAFB2 -1.68270 -1.54790 -0.79835\n", "ARFGEF1_ARFGEF2 -1.08965 -1.49845 -1.70760\n", "KDELR1_KDELR2 -2.19785 -1.46595 -1.73045\n", "DNAJA1_DNAJA2 -1.46920 -1.44810 -1.52540\n", "CSNK1D_CSNK1E -1.68675 -1.44095 -2.15925" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dLFC.nsmallest(20,'HT29' )" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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A549HT29OVCAR8
ACTG1_ACTR1B0.118701.016151.39085
TTC30A_TTC30B0.659400.545250.37205
ACAP2_ACAP30.759850.455900.43260
PPP1CC_PPP2CB0.358350.397200.08790
OSBPL3_OSBPL70.016600.394150.05450
ZNF26_ZNF2680.549850.385800.22890
FAM160B1_FAM160B2-0.337200.38475-0.10520
RABL2A_RABL2B0.336250.376550.11350
USP25_USP280.317150.359900.18190
ZNF324_ZNF324B-0.302650.34245-0.14110
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" ], "text/plain": [ " A549 HT29 OVCAR8\n", "ACTG1_ACTR1B 0.11870 1.01615 1.39085\n", "TTC30A_TTC30B 0.65940 0.54525 0.37205\n", "ACAP2_ACAP3 0.75985 0.45590 0.43260\n", "PPP1CC_PPP2CB 0.35835 0.39720 0.08790\n", "OSBPL3_OSBPL7 0.01660 0.39415 0.05450\n", "ZNF26_ZNF268 0.54985 0.38580 0.22890\n", "FAM160B1_FAM160B2 -0.33720 0.38475 -0.10520\n", "RABL2A_RABL2B 0.33625 0.37655 0.11350\n", "USP25_USP28 0.31715 0.35990 0.18190\n", "ZNF324_ZNF324B -0.30265 0.34245 -0.14110" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dLFC.nlargest(10,'HT29' )" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Count')" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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nO+2qrwP2kLRzxbIZwOPAg60pycxsW+0UnP8NPAEcWrHscGAwIra2piQzs221za56RGyRdDnwRUmnUYw2z6W4RKldtfxwQUbd0tdu6Sd0T18b3s+2OTkEIGkKcBnwd8AjwKKIWNTaqszM/lJbBaeZWSdop2OcHUnScyQtl7SpvL/+05La5hBII0naS9K/SNoo6T5JX5O0e6vrahYVfijpXa2upZG6bU6Isr+/lnRUo9bp4By/b1FcNjUHeBPwFuBDLa2oea6imEPgNcCxwGzgqy2tqEkk7QB8nqKvE03lnBBnAOdLOqW1JTWHpF2AbwIvauR6J+TIKBdJuwL/B3woIn4P3C7p28ARwCdbWlyDSZoJvBp4QUSsKZe9D7hR0pSI2DLmCjqIpH2AbwDPAx5ucTkNVTEnxPERMQgMShqeE6Khk+m0mqQDKP6xV6PX7RHnOETEoxHx5jI0kfQi4ATgx62trCk2AccBd1QsC4q/Q89sSUXN83KKfxAPpOj3RDLanBAHSZrUmpKa5gjgpxSj64byiLNBJN1EcQ3qIHBpi8tpuIh4FPh+1eL3AbdGxL0tKKlpIuI64DoAqeGDlVarNSfE+pZU1QQRcdnwnxv9PTo4ayiPkcwcpXlDGSgAZwLPBhZTHFM5IUN5DZXQVyR9AHgj8Lc5amuklH5OQFMo7sarNPy6+pZnG4WDs7Y+4MZR2t4BLAWIiF8BSDoduFlSb+XEJB2irr5KWkBxguHsiLg+T2kNVVc/J6i654Sw0Tk4a4iIlYxycFnSsySdHBFXVyy+rfw9HVjb5PIaaqy+DpP0ceAC4B8i4gtZCmuwevo5gf1pToiIeKJc5jkhEvnk0Pg8C1gm6eUVyw4EngL+tzUlNU95Fv184IyIWNzqemy7eE6IBvCIcxwi4k5JPwC+JOndwG4U98UujohHWltdY0l6LvBpiltivydpRkXzxnaaod9G16FzQrQdjzjHbx5wO8UlSN+hOBs7ES+AP4HiWNiZFGdeK3/2a2Fdlu4c4BfAT4B/Bj4eEd9qbUmdxfeqm5kl8ojTzCyRg9PMLJGD08wskYPTzCyRg9PMLJGD08wskYPTOpqkT0gakNQvKUabfV/SaWX7SD8nVbxvd0kXS/qtpC2S1kg6T9JO+Xpl7c53Dlk3WU8x12a1h6CYewC4GbiP4k6a35bv/zzwYuCtecq0dufgtG7ydI25Qz8NPAm8pmK+yt9Juh8YkLQ4In7W9Cqt7XlX3TqKpAMkrSx3o39EMQdqI9Y7GTgFuLRqkl8i4gbgSGB1I7Zlnc/BaR2jDLcVwO8odqG/S+Mmp5gFTKO4h3sbEfHTifRcJRsf76pbJzkK2BN4T0QMAb+RdCTF3Kf1eI6koaplX4mI9wPDjzmeaM8YsiZwcFonOQC4qwzNYauAo+v8/AbglVXLhoPy/vL3HttfnnUL76pbp6meuf3JhM8+FRF3Vv1sLNvuopgB/eARNypdLel121GvTUAOTuskvwaeL6lyVPiyRqy4nIj5m8BZ5bHUP5E0F3gTsHGkz1r38a66dZLrgbuBr0n6CDAHOAm4peI9fyPp6YrXWxMeKHcRxbPjfyTpY8DvKR4r8Vng6xFx03g7YBODg9M6RkQ8KelY4CsUz6//FfBF/vKi9hVVH9tMcba8nvVvlHQY8DHgCoqTTr8DFlJcBG8GeAZ4M7NkPsZpZpbIwWlmlsjBaWaWyMFpZpbIwWlmlsjBaWaWyMFpZpbIwWlmlsjBaWaW6P8Br4OF5RGUI1wAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dLFC['HT29'].hist(bins=30, figsize=(5,3))\n", "xlabel('dLFC')\n", "ylabel('Count')\n", "#savefig('hist_dLFC_ht29.pdf')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cc1 = dLFC.corr()\n", "\n", "Y = clust.linkage( cc1 , method='average', metric='correlation')\n", "\n", "f=figure( figsize(5,5))\n", "\n", "# DENDROGRAM\n", "axdend = f.add_axes([0.25,0.85,0.55,0.05])\n", "Z = clust.dendrogram( Y, orientation='top' )\n", "axdend.set_yticks([])\n", "idx = Z['leaves']\n", "clean_axis(axdend)\n", "\n", "# HEATMAP\n", "plotMatrix = cc1.values[:,idx]\n", "plotMatrix = plotMatrix[idx,:]\n", "\n", "axmatrix = f.add_axes([0.25,0.30,0.55,0.55])\n", "im = axmatrix.matshow( plotMatrix, aspect='auto', vmin=cc1.min().min(), vmax=1.0)\n", "axmatrix.set_xticks([])\n", "axmatrix.xaxis.set_ticks_position('bottom')\n", "axmatrix.yaxis.set_ticks_position('left')\n", "axmatrix.set_yticks(range(cc1.shape[0]))\n", "axmatrix.set_xticks(range(cc1.shape[0]))\n", "axmatrix.set_xticklabels(cc1.columns.values[idx], rotation=90)\n", "axmatrix.set_yticklabels(cc1.columns.values[idx])\n", "\n", "cbar = f.add_axes([.85,.30,.05,.55])\n", "colorbar(im, cax=cbar)\n", "#savefig('cluster-dLFC-3cells-403genepairs.pdf')\n", "show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = pyplot.subplots(figsize=(5,5))\n", "g=sns.violinplot(data=dLFC)\n", "#g.set_xticklabels(g.get_xticklabels(), rotation=90)\n", "ylabel('dLFC')\n", "#savefig('violin-dLFC-3cells-403genepairs.pdf')\n", "show()\n", "\n", "#sns.violinplot( data=dLFC_mean)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "figure()\n", "sns.scatterplot( data=dLFC, x='OVCAR8', y='HT29')\n", "figure()\n", "sns.scatterplot( data=dLFC, x='OVCAR8', y='A549')\n", "figure()\n", "sns.scatterplot( data=dLFC, x='A549', y='HT29')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "A549 -1.60590\n", "HT29 -2.46195\n", "OVCAR8 -2.17540\n", "Name: MAPK1_MAPK3, dtype: float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dLFC.loc['MAPK1_MAPK3']" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "A549 0.1651\n", "HT29 0.0245\n", "OVCAR8 -0.9726\n", "Name: MAP2K1_MAP2K2, dtype: float64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dLFC.loc['MAP2K1_MAP2K2']" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "A549 -0.95180\n", "HT29 -2.20855\n", "OVCAR8 -2.56430\n", "Name: HDAC1_HDAC2, dtype: float64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dLFC.loc['HDAC1_HDAC2']" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "A549 0.1021\n", "HT29 -0.2170\n", "OVCAR8 -0.5285\n", "Name: BRCA1_PARP1, dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dLFC.loc['BRCA1_PARP1']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }