From a634e02e74a2b42279bfbed9b9449c308f3767ad Mon Sep 17 00:00:00 2001 From: Katie Gjoni Date: Thu, 14 Dec 2023 11:35:08 -0800 Subject: [PATCH] update readme and rerun tests --- README.md | 45 +-- .../test_set_edge_SV_scores | 342 +++++++++--------- .../test_set_edge_simple_scores | 30 +- 3 files changed, 203 insertions(+), 214 deletions(-) diff --git a/README.md b/README.md index b3064a1..4228014 100644 --- a/README.md +++ b/README.md @@ -16,41 +16,30 @@ ## Installation + +**Download SuPreMo by:** +```shell +git clone https://github.com/ketringjoni/SuPreMo.git +``` -**Requirements:** -- Overall - * Python - * Pysam - * Pandas - * Numpy - * Pathlib - * Biopython -- Only for SuPreMo-Akita - * Akita, Basenji, and their dependencies -- Only for walkthroughs - * Jupyter - * Matplotlib - -**For SuPreMo**, we recommend creating a conda environment with the requirements outlined above or using [get_seq_env.yml](https://github.com/ketringjoni/SuPreMo/blob/main/get_seq_env.yml). +For **SuPreMo**, we recommend creating a conda environment with Python, Pysam, Pandas, Numpy, Pathlib, and Biopython or using [get_seq_env.yml](https://github.com/ketringjoni/SuPreMo/blob/main/get_seq_env.yml). -**For SuPreMo-Akita**, we recommend creating a conda environment following the steps outlined below. Compatible package versions are shown in [get_Akita_scores_env.yml](https://github.com/ketringjoni/SuPreMo/blob/main/get_Akita_scores_env.yml). +For **SuPreMo-Akita**, we recommend creating a conda environment following the steps outlined below. Compatible package versions are shown in [get_Akita_scores_env.yml](https://github.com/ketringjoni/SuPreMo/blob/main/get_Akita_scores_env.yml). -1. Create conda environment with python=3.10 numpy scipy pandas jupyter tensorflow -2. Install [basenji](https://github.com/calico/basenji) with no dependencies, then set the following environmental variables. - * python setup.py develop --no-deps - * export BASENJIDIR=~/basenji - * export PATH=$BASENJIDIR/bin:$PATH - * export PYTHONPATH=$BASENJIDIR/bin:$PYTHONPATH +1. Create conda environment with python=3.10 numpy scipy pandas jupyter tensorflow. +2. Install [basenji](https://github.com/calico/basenji) with no dependencies and set environmental variables. +```shell +python setup.py develop --no-deps +export BASENJIDIR=~/basenji +export PATH=$BASENJIDIR/bin:$PATH +export PYTHONPATH=$BASENJIDIR/bin:$PYTHONPATH +``` 3. Install the rest of the dependencies using pip: astropy, protobuf==3.19, tensorflow-io-gcs-filesystem, patsy, libclang, Cython, cooltools, biopython, pathlib, natsort. To test that all the necessary packages are properly installed, run [testing_packages.py](https://github.com/ketringjoni/SuPreMo/blob/main/scripts/testing_packages.py). - - -**Download SuPreMo by:** -```shell -git clone https://github.com/ketringjoni/SuPreMo.git -``` + +For the tutorials, Jupyter and Matplotlib are also needed. diff --git a/test_data/test_set_edge_cases/test_set_edge_SV_scores b/test_data/test_set_edge_cases/test_set_edge_SV_scores index 84e8c46..c6e7b1d 100644 --- a/test_data/test_set_edge_cases/test_set_edge_SV_scores +++ b/test_data/test_set_edge_cases/test_set_edge_SV_scores @@ -1,39 +1,39 @@ var_index mse_-10000 corr_-10000 mse_0 corr_0 mse_10000 corr_10000 -0 0.00784650219993532350 0.98062803790555475025 0.01118139102267819672 0.96822594515943194438 0.01255155243915618418 0.96440020818905225486 -1 0.00739262603255034286 0.98205685541180065545 0.00812494270269283579 0.97609003907671065825 0.02063598731876870734 0.93955396776288380156 -2 0.01692971863508223665 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0.01945985583213118847 0.84493590436507304275 +149 0.04972069855436260510 0.60378316953425081870 0.04103662833685202405 0.62181685852789825297 0.04972069855436260510 0.60378316953425081870 150 nan nan nan nan nan nan 151 nan nan nan nan nan nan -152 0.02213250050369520994 0.92906614799681108696 0.01894558285453000132 0.93467110456219226755 0.02213250050369520994 0.92906614799681108696 -153 0.03771207777275473183 0.88017980721835642477 0.03111504987566623623 0.91007128886433108494 0.03771207777275473183 0.88017980721835642477 -154 0.00097540940755276812 0.98354710757062013826 0.00118416547828186208 0.97692997923115132419 0.00097540940755276812 0.98354710757062013826 -155 0.02298720229472166199 0.70275688401776670311 0.02101443891147845508 0.67417632166268215421 0.02298720229472166199 0.70275688401776670311 +152 0.02213251380604271124 0.92906608221776676437 0.01894559693432975450 0.93467110376742379430 0.02213251380604271124 0.92906608221776676437 +153 0.03771208737733591082 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0.00288732511166607747 0.87394370204563920357 160 nan nan nan nan nan nan 161 nan nan nan nan nan nan 162 nan nan nan nan nan nan 163 nan nan nan nan nan nan -164 0.00191363846675439669 0.96761622824700488366 0.00350629181056150195 0.94675381743446740401 0.00191363846675439669 0.96761622824700488366 -165 0.01192970166658099962 0.82300327819968566168 0.00735785475412241517 0.91444644014923992525 0.01192970166658099962 0.82300327819968566168 +164 0.00191363980886039833 0.96761625919132376872 0.00350629517661084486 0.94675386653800774894 0.00191363980886039833 0.96761625919132376872 +165 0.01192970765625989485 0.82300364801510639268 0.00735787116240478427 0.91444645503502197670 0.01192970765625989485 0.82300364801510639268 166 nan nan nan nan nan nan 167 nan nan nan nan nan nan -168 0.00395875776478421017 0.97544669799221117579 0.00491466387144291923 0.97610368736480390695 0.00395875776478421017 0.97544669799221117579 -169 0.01142597743056034121 0.96656017978268815583 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nan nan -174 0.05326304168840666903 0.87274357273918101718 0.04945318526241095081 0.89258679835413778925 0.05326304059676709862 0.87274354633435247131 -175 0.06212143673524094661 0.84735564251945272307 0.05616629009950731971 0.87885026120663267690 0.06212181379465857300 0.84735449853284605659 +174 0.05325937387709844817 0.87275404482600826839 0.04945200204911939790 0.89258955582150389940 0.05325937387709844817 0.87275404482600826839 +175 0.06212081415986745597 0.84735733184073713797 0.05616605654919091389 0.87885070486825778158 0.06212081415986745597 0.84735733184073713797 176 nan nan nan nan nan nan 177 nan nan nan nan nan nan 178 nan nan nan nan nan nan @@ -185,46 +185,46 @@ var_index mse_-10000 corr_-10000 mse_0 corr_0 mse_10000 corr_10000 183 nan nan nan nan nan nan 184 nan nan nan nan nan nan 185 nan nan nan nan nan nan -186 0.00592470774869442978 0.97496280340320118363 0.00798303367513663806 0.96473298519886763192 0.00592470774869442978 0.97496280340320118363 -187 0.00980043616817789266 0.95584277002894413311 0.01496847181432681313 0.92522522104259175002 0.00980043616817789266 0.95584277002894413311 -188 0.01328526950200943982 0.95887986170015249154 0.01550185886372884396 0.95582367298074721873 0.01328526950200943982 0.95887986170015249154 -189 0.01982537745608235064 0.92566148354593924541 0.02300526828471678026 0.92619139548547757546 0.01982537745608235064 0.92566148354593924541 -190 0.02581077181880250468 0.81751215824845480107 0.02161559025737272779 0.82869655739989500631 0.02581077181880250468 0.81751215824845480107 -191 0.05156935167063615710 0.64893334287939263838 0.04092205929369646666 0.72823990895507584309 0.05156935167063615710 0.64893334287939263838 -192 0.02140678200936457082 0.84452173390577822687 0.01892278270669387266 0.84648182182688125330 0.02140678200936457082 0.84452173390577822687 -193 0.04299590836949553074 0.64810808286498822195 0.02875442869187323086 0.75815911080070652606 0.04299590836949553074 0.64810808286498822195 -194 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0.05156940603173688908 0.64893289851217184072 0.04092209743996251536 0.72823955603913337331 0.05156940603173688908 0.64893289851217184072 +192 0.02140678157522664035 0.84452185767308851627 0.01892279614336960306 0.84648181241243414252 0.02140678157522664035 0.84452185767308851627 +193 0.04299598593744132641 0.64810743497487832698 0.02875446536095131386 0.75815867537174341795 0.04299598593744132641 0.64810743497487832698 +194 0.00379185591862576292 0.98220438515026642357 0.00245849299237680102 0.98583864944614973957 0.00379185591862576292 0.98220438515026642357 +195 0.01905229337474988183 0.95307662145075700000 0.00829533939127815451 0.96879597470104250601 0.01905229337474988183 0.95307662145075700000 196 nan nan nan nan nan nan 197 nan nan nan nan nan nan -198 0.02058230745140438717 0.83987487733332533146 0.01717690435199498850 0.85235123341019480847 0.02058230745140438717 0.83987487733332533146 -199 0.04541140351334720104 0.62858548778033884741 0.03292281685327209245 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0.00141023218256880901 0.89397076283861420798 206 nan nan nan nan nan nan 207 nan nan nan nan nan nan -208 0.00200755285864207553 0.97852389728751254516 0.00226801893999220355 0.97658170749785266906 0.00200755285864207553 0.97852389728751254516 -209 0.01258673426189423593 0.82085913395202880771 0.00607041464266046098 0.92644602943571463527 0.01258673426189423593 0.82085913395202880771 -210 0.00282143406731724883 0.98686539188092303743 0.00353439663168543397 0.97855687590359219374 0.00282143406731724883 0.98686539188092303743 -211 0.01146962841207979471 0.95706380109329569628 0.00856863204969596920 0.95333005748946142432 0.01146962841207979471 0.95706380109329569628 +208 0.00200755150247444985 0.97852390047262627348 0.00226801832362842886 0.97658167574719045856 0.00200755150247444985 0.97852390047262627348 +209 0.01258679794058618906 0.82085807920823739003 0.00607045666773078571 0.92644548872151644137 0.01258679794058618906 0.82085807920823739003 +210 0.00282143744255286761 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