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Using a decision tree to predict polymorphism at tandem repeat regions of the human genome
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finding_tandem-repeats_enriched_in_pacbio-calls.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Source \n",
"\n",
"```\n",
"/scratch/ucgd/lustre-work/quinlan/u6018199/chaisson_2019/analysis/finding_tandem-repeats_enriched_in_pacbio-calls\n",
"```\n",
"\n",
"## Decision tree to predict variation at tandem repeats \n",
"\n",
"The number of copies of a repeat unit in a tandem repeat can vary among individuals. The most likely scenario is that a single copy is removed/inserted, but changes involving more than one copy are possible too (Fig 5 of https://academic.oup.com/nar/article/42/14/8884/1288911 and Fig 1 of https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1482962/). It has also been suggested that the greater the number of copies of the repeat unit, the greater the mutation rate (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1482962/). \n",
"\n",
"Using this prior biological knowledge, I trained a decision tree to predict whether a tandem repeat harbored a **100-105bp deletion** based upon features of the tandem repeat such as period (length of the repeat unit), number of copies (of the repeat unit), degree of similarity among the repeat units, and length of the tandem repeat (copies * period). The labels (\"contains event\" and \"does not contain event\") were assigned to the training examples based upon pacbio calls in a single individual (HG00514). See `get-data.sh` in this directory.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
"from dtreeviz.trees import dtreeviz \n",
"import matplotlib.pyplot as plt\n",
"import qgrid # also see http://beakerx.com/"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"population = \"CHS\"\n",
"sample = \"HG00514\"\n",
"svtype = \"DEL\" \n",
"absolute_SV_length_lower = 100 \n",
"absolute_SV_length_upper = 105 \n",
"\n",
"negative_csv = '{}.negative_training_examples.csv'.format(sample) # '{}.negative_training_examples.small.csv'.format(sample) \n",
"positive_csv = '{}.{}.gt{}.lt{}.positive_training_examples.csv'.format(\n",
" sample, svtype, absolute_SV_length_lower, absolute_SV_length_upper\n",
")\n",
"\n",
"file_to_class = {negative_csv: 0, positive_csv: 1} \n",
"\n",
"class_names = ['\"does not contain pacbio call\"', '\"contains pacbio call\"']\n",
"\n",
"balance_classes = False \n",
"\n",
"# AI parameters and features\n",
"max_tree_depth = 4\n",
"AI_features = [\n",
"# 'start_normalized', \n",
"# 'length'\n",
"# 'period', \n",
"# 'copy_number', \n",
" 'log10_length',\n",
" 'log10_period', \n",
"# 'log10_copy_number', \n",
" 'percentage_match', \n",
" 'percentage_indel', \n",
"# 'percent_A_in_repeat_unit', \n",
"# 'percent_C_in_repeat_unit', \n",
"# 'percent_G_in_repeat_unit', \n",
"# 'percent_T_in_repeat_unit',\n",
"# 'entropy'\n",
"]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"***** performance on train set ******\n",
"auc: 0.9794894773358003\n",
" prob. thresh. tn fp fn tp\n",
"0 0.1 166 22 1 183\n",
"1 0.2 166 22 1 183\n",
"2 0.3 166 22 1 183\n",
"3 0.4 166 22 1 183\n",
"4 0.5 166 22 1 183\n",
"5 0.6 178 10 16 168\n",
"6 0.7 178 10 16 168\n",
"7 0.8 178 10 16 168\n",
"8 0.9 184 4 51 133\n",
"\n",
"(tn, fp, fn, tp) = (166, 22, 1, 183)\n",
"\n",
"size of original set: 372\n",
"number of positives in original set: 184\n",
"fraction of positives in original set: 0.4946\n",
"\n",
"size of reduced set: 205\n",
"number of positives in reduced set: 183\n",
"fraction of positives in reduced set: 0.8927\n",
"\n",
"***** performance on dev set ******\n",
"auc: 0.9107551487414187\n",
" prob. thresh. tn fp fn tp\n",
"0 0.1 16 3 2 21\n",
"1 0.2 16 3 2 21\n",
"2 0.3 16 3 2 21\n",
"3 0.4 16 3 2 21\n",
"4 0.5 16 3 2 21\n",
"5 0.6 16 3 7 16\n",
"6 0.7 16 3 7 16\n",
"7 0.8 16 3 7 16\n",
"8 0.9 17 2 9 14\n",
"\n",
"(tn, fp, fn, tp) = (16, 3, 2, 21)\n",
"\n",
"size of original set: 42\n",
"number of positives in original set: 23\n",
"fraction of positives in original set: 0.5476\n",
"\n",
"size of reduced set: 24\n",
"number of positives in reduced set: 21\n",
"fraction of positives in reduced set: 0.875\n",
"\n",
"***** performance on test set ******\n",
"auc: 0.95358080917803\n",
" prob. thresh. tn fp fn tp\n",
"0 0.1 767610 112162 0 23\n",
"1 0.2 767610 112162 0 23\n",
"2 0.3 767610 112162 0 23\n",
"3 0.4 767610 112162 0 23\n",
"4 0.5 767610 112162 0 23\n",
"5 0.6 809145 70627 2 21\n",
"6 0.7 809145 70627 2 21\n",
"7 0.8 809145 70627 2 21\n",
"8 0.9 835048 44724 8 15\n",
"\n",
"(tn, fp, fn, tp) = (767610, 112162, 0, 23)\n",
"\n",
"size of original set: 879795\n",
"number of positives in original set: 23\n",
"fraction of positives in original set: 2.614e-05\n",
"\n",
"size of reduced set: 112185\n",
"number of positives in reduced set: 23\n",
"fraction of positives in reduced set: 0.000205\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/uufs/chpc.utah.edu/common/HIPAA/u6018199/.conda/envs/chaisson_2019/lib/python3.7/site-packages/sklearn/utils/validation.py:71: FutureWarning: Pass classes=[0 1], y=[0 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 0 1 0 1 0 0 0 1 1 1 1 0 1 0\n",
" 1 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0\n",
" 1 1 0 1 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 1 1 0\n",
" 1 1 1 1 0 1 1 1 0 1 1 0 1 0 0 0 1 1 1 1 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 1 1\n",
" 1 1 1 0 0 0 0 1 1 1 1 0 0 0 1 1 0 0 0 1 0 0 1 1 1 1 0 0 1 1 1 0 1 1 0 0 0\n",
" 1 1 0 1 0 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 0 1 0 1 1 1 0 1 0 1 0 0 1\n",
" 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 1 0 0 1 1 1 1 0 0 1 1 0 1 0 1\n",
" 1 0 1 0 1 1 1 1 0 0 0 1 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1 0 1 1 1 0 0 0\n",
" 1 0 1 0 1 1 1 0 0 0 1 1 0 1 1 1 0 1 0 1 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1\n",
" 1 1 1 1 1 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1\n",
" 0 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
" FutureWarning)\n"
]
}
],
"source": [
"def concatenate(positive_tuple, negative_tuple): \n",
" Xp, yp, dfp = positive_tuple\n",
" Xn, yn, dfn = negative_tuple\n",
" X = np.concatenate((Xp, Xn), axis=0)\n",
" y = np.concatenate((yp, yn), axis=0)\n",
" df = pd.concat([dfp, dfn], axis=0)\n",
" return X, y, df\n",
"\n",
"def truncate(tuple_, size=100): \n",
" X, y, df = tuple_\n",
" X = X[:size,:]\n",
" y = y[:size,:]\n",
" df = df.head(size)\n",
" return X, y, df\n",
" \n",
"def create_dataframe_of_AI_predictions(AI): \n",
" positive_tuple = create_X_y_df(positive_csv)\n",
" _, _, dfp = positive_tuple\n",
" number_positive_examples = len(dfp)\n",
" \n",
" if balance_classes: \n",
" negative_tuple = truncate(create_X_y_df(negative_csv), size=number_positive_examples)\n",
" else: \n",
" negative_tuple = create_X_y_df(negative_csv)\n",
" \n",
" X, _, df = concatenate(positive_tuple, negative_tuple)\n",
" df['AI_prediction'] = AI.predict(X) \n",
" \n",
" return df\n",
" \n",
"def create_X_df(csv_file): \n",
" df = pd.read_csv(csv_file, header=0)\n",
" df['log10_period'] = np.log10(df['period']) \n",
" df['log10_length'] = np.log10(df['length']) \n",
" df['log10_copy_number'] = np.log10(df['copy_number']) \n",
" df['copy_number'] = df['copy_number'].round(decimals=1)\n",
" df['entropy'] = df['entropy'].round(decimals=2)\n",
" df['locus'] = df['chromosome'] + ':' + df['start'].map(str) + '-' + df['end'].map(str)\n",
" df['IGV'] = df['locus'].map(lambda locus: '<a target=\"_blank\" href=\"http://0.0.0.0:5000/?locus={0}\">link</a>'.format(locus))\n",
" \n",
" X = np.array(df[AI_features])\n",
"\n",
" return X, df \n",
"\n",
"def create_X_y_df(csv_file): \n",
" X, df = create_X_df(csv_file) \n",
" \n",
" y = file_to_class[csv_file]*np.ones((len(df),1), dtype=int)\n",
" df['observation'] = y \n",
"\n",
" return X, y, df\n",
" \n",
"def split_positive_class(size): \n",
" X, y, _ = create_X_y_df(positive_csv) \n",
" return train_test_split(X, y, train_size=size, shuffle=True) \n",
" \n",
"def split_negative_class(size): \n",
" X, y, _ = create_X_y_df(negative_csv) \n",
" return train_test_split(X, y, train_size=size, shuffle=True) \n",
"\n",
"def compute_enrichment(tn, fp, fn, tp): \n",
" print('size of original set:', tn+fp+fn+tp)\n",
" print('number of positives in original set:', fn+tp)\n",
" print('fraction of positives in original set: {0:.4}'.format((fn+tp)/float(tn+fp+fn+tp)))\n",
" print('')\n",
" \n",
" print('size of reduced set:', fp+tp)\n",
" print('number of positives in reduced set:', tp)\n",
" print('fraction of positives in reduced set: {0:.4}'.format(tp/float(fp+tp)))\n",
" print('')\n",
" \n",
"def AI_performance(AI, X, y, set_name): \n",
" print('***** performance on {} ******'.format(set_name))\n",
" fpr, tpr, thresholds = roc_curve(y, AI.predict_proba(X)[:,1])\n",
" print('auc:', auc(fpr, tpr))\n",
"\n",
" probability_thresholds = np.arange(0.1,1,0.1)\n",
" data = []\n",
" for probability_threshold in probability_thresholds: \n",
" y_predict = AI.predict_proba(X)[:, 1] > probability_threshold\n",
" tn, fp, fn, tp = confusion_matrix(y, y_predict).ravel()\n",
" data.append([probability_threshold, tn, fp, fn, tp])\n",
" df = pd.DataFrame(data, columns = [\"prob. thresh.\", \"tn\", \"fp\", \"fn\", \"tp\"])\n",
" print(df)\n",
" print('')\n",
"\n",
" tn, fp, fn, tp = confusion_matrix(y, AI.predict(X)).ravel()\n",
" print('(tn, fp, fn, tp) =', (tn, fp, fn, tp))\n",
" print('')\n",
" \n",
" compute_enrichment(tn, fp, fn, tp) \n",
" \n",
"def train_dev_test_visualize_AI():\n",
" X_positive_train_dev, X_positive_test, y_positive_train_dev, y_positive_test = split_positive_class(size=0.9)\n",
" X_negative_train_dev, X_negative_test, y_negative_train_dev, y_negative_test = split_negative_class(size=X_positive_train_dev.shape[0])\n",
"\n",
" X_train_dev = np.concatenate((X_positive_train_dev, X_negative_train_dev), axis=0)\n",
" y_train_dev = np.concatenate((y_positive_train_dev, y_negative_train_dev), axis=0)\n",
" \n",
" X_train, X_dev, y_train, y_dev = train_test_split(X_train_dev, y_train_dev, train_size=0.9, shuffle=True) \n",
"\n",
" X_test = np.concatenate((X_positive_test, X_negative_test), axis=0)\n",
" y_test = np.concatenate((y_positive_test, y_negative_test), axis=0)\n",
"\n",
" AI = DecisionTreeClassifier(max_depth=max_tree_depth)\n",
" AI = AI.fit(X_train, y_train)\n",
" \n",
" AI_performance(AI, X_train, y_train, \"train set\")\n",
" AI_performance(AI, X_dev, y_dev, \"dev set\") \n",
" AI_performance(AI, X_test, y_test, \"test set\")\n",
"\n",
" df = create_dataframe_of_AI_predictions(AI)\n",
" \n",
" # https://github.com/parrt/dtreeviz\n",
" decision_tree_visualization = dtreeviz(\n",
" AI, \n",
" X_train,\n",
" y_train[:,0],\n",
" target_name='tandem-repeat classes',\n",
" feature_names=AI_features, \n",
" class_names=class_names\n",
" ) \n",
" \n",
" return decision_tree_visualization, df, AI\n",
" \n",
"decision_tree_visualization_, df_, trained_AI = train_dev_test_visualize_AI()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The performace statistics above show that when given a set of tandem repeats, the classifier outputs a subset that is an order of magnitude smaller, yet contains almost all of the tandem repeats that harbor SVs of the given size. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interpreting the decision tree"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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"text/plain": [
"<dtreeviz.trees.DTreeViz at 0x2ba23545e550>"
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"execution_count": 4,
"metadata": {},
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"source": [
"decision_tree_visualization_"
]
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the tree's first branch point, which gives the classifier most of its discriminative power, we see that the fact that the classifier has very few false negatives supports the hypothesis that a tandem repeat can only harbor a 100bp deletion if the tandem repeat is at least 100bp long."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## No obvious way to reduce false-positive rate of decision tree classifier \n",
"\n",
"We visually inspected some of the false-positive calls that the classifier made, but failed to spot a way to reduce their number..."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
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" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>112187 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" chromosome start end start_normalized length \\\n",
"7 chr1 66159 66629 0.000266 470 \n",
"8 chr1 66160 66630 0.000266 470 \n",
"9 chr1 66204 66632 0.000266 428 \n",
"14 chr1 83791 84041 0.000337 250 \n",
"15 chr1 88674 88962 0.000356 288 \n",
"... ... ... ... ... ... \n",
"879927 chrX 155469086 155469341 0.996336 255 \n",
"879929 chrX 155471171 155471306 0.996349 135 \n",
"879956 chrX 155620120 155620316 0.997303 196 \n",
"879957 chrX 155620137 155620316 0.997304 179 \n",
"879958 chrX 155620137 155620316 0.997304 179 \n",
"\n",
" simple_repeat_tag_name period copy_number percentage_match \\\n",
"7 trf 7 68.1 72 \n",
"8 trf 2 262.0 71 \n",
"9 trf 5 87.4 73 \n",
"14 trf 4 64.8 86 \n",
"15 trf 71 4.0 90 \n",
"... ... ... ... ... \n",
"879927 trf 50 5.0 76 \n",
"879929 trf 37 4.0 80 \n",
"879956 trf 82 2.4 100 \n",
"879957 trf 26 6.6 88 \n",
"879958 trf 56 3.3 86 \n",
"\n",
" percentage_indel ... percent_T_in_repeat_unit entropy \\\n",
"7 18 ... 49 1.08 \n",
"8 21 ... 49 1.08 \n",
"9 16 ... 48 1.06 \n",
"14 11 ... 0 0.86 \n",
"15 1 ... 18 1.83 \n",
"... ... ... ... ... \n",
"879927 16 ... 47 1.59 \n",
"879929 12 ... 45 1.50 \n",
"879956 0 ... 36 1.79 \n",
"879957 7 ... 36 1.79 \n",
"879958 9 ... 36 1.79 \n",
"\n",
" repeat_unit log10_period \\\n",
"7 TTATATA 0.845098 \n",
"8 TA 0.301030 \n",
"9 ATATA 0.698970 \n",
"14 AAAG 0.602060 \n",
"15 ACCCAGAGTGCTCGTGACGGCCAGCAGAGCCGGCCCCCATCTCCTC... 1.851258 \n",
"... ... ... \n",
"879927 ATATATGTATTTATAGTTATATAACTAACATATATGTATTTATAGTTAC 1.698970 \n",
"879929 ATTTATATAAATACTATATATAATATACATGT 1.568202 \n",
"879956 TTAGCAGATTCTTATAAAGTTAAACACACTTTATAAGATTCTTAGT... 1.913814 \n",
"879957 AGTTAAACACACTTTATAAGATTCTT 1.414973 \n",
"879958 AGTTAAACACACTTTATAAGATTCTTAGTTAAACACACTTTATAAG... 1.748188 \n",
"\n",
" log10_length log10_copy_number locus \\\n",
"7 2.672098 1.833147 chr1:66159-66629 \n",
"8 2.672098 2.418301 chr1:66160-66630 \n",
"9 2.631444 1.941511 chr1:66204-66632 \n",
"14 2.397940 1.811575 chr1:83791-84041 \n",
"15 2.459392 0.602060 chr1:88674-88962 \n",
"... ... ... ... \n",
"879927 2.406540 0.698970 chrX:155469086-155469341 \n",
"879929 2.130334 0.602060 chrX:155471171-155471306 \n",
"879956 2.292256 0.380211 chrX:155620120-155620316 \n",
"879957 2.252853 0.819544 chrX:155620137-155620316 \n",
"879958 2.252853 0.518514 chrX:155620137-155620316 \n",
"\n",
" IGV observation \\\n",
"7 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"8 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"9 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"14 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"15 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"... ... ... \n",
"879927 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"879929 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"879956 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"879957 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"879958 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 0 \n",
"\n",
" AI_prediction \n",
"7 1 \n",
"8 1 \n",
"9 1 \n",
"14 1 \n",
"15 1 \n",
"... ... \n",
"879927 1 \n",
"879929 1 \n",
"879956 1 \n",
"879957 1 \n",
"879958 1 \n",
"\n",
"[112187 rows x 23 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def prettify_core(df): \n",
" print('number of records:', len(df))\n",
" return qgrid.show_grid(\n",
" df, \n",
" show_toolbar=True,\n",
" grid_options={'forceFitColumns': False, 'defaultColumnWidth': 175}\n",
" )\n",
" \n",
"def prettify(df, include_heuristic_prediction=False): \n",
" labels = ['locus', 'IGV', 'length', 'period', 'copy_number', 'entropy', 'observation', 'AI_prediction'] \n",
" if include_heuristic_prediction: \n",
" labels += ['heuristic_prediction']\n",
" df = df[labels] \n",
" return prettify_core(df)\n",
" \n",
"def false_positives(df, model): \n",
" prediction_label = model + '_prediction'\n",
" predicted_to_contain_call_ = df[prediction_label] == 1\n",
" does_not_contain_call_ = df['observation'] == 0\n",
" return df[predicted_to_contain_call_ & does_not_contain_call_]\n",
"\n",
"# prettify(false_positives(df_, 'AI')) \n",
"false_positives(df_, 'AI')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Heuristic model has a lower false-positive rate, but higher false-negative rate\n",
"\n",
"We next considered a simple heuristic motivated by the hypothesis that deletions of 100bp arise from a tandem repeat by: (i) deletion of a single repeat unit of size (period) 100bp; (ii) deletion of two repeat units, each of size 50bp; (iii) deletion of three repeat units, each of size 33bp; etc. \n",
"\n",
"Given that this heuristic model is more \"stringent\" in what it predicts than the trained model (which essentially only asks that the tandem repeat length exceed 100bp), we reasoned that it would have a lower false-positive rate. \n",
"\n",
"That supposition turns out to be true. The trade-off, however, is that the heuristic model has a higher false-negative rate than the original trained model: \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# heuristic-model parameters\n",
"sv_len_lower = absolute_SV_length_lower\n",
"sv_len_upper = absolute_SV_length_upper \n",
"slop_in_target_period = 0.05 "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"***** performance of heuristic ******\n",
"\n",
"(tn, fp, fn, tp) = (800346, 79633, 114, 116)\n",
"\n",
"size of original set: 880209\n",
"number of positives in original set: 230\n",
"fraction of positives in original set: 0.0002613\n",
"\n",
"size of reduced set: 79749\n",
"number of positives in reduced set: 116\n",
"fraction of positives in reduced set: 0.001455\n",
"\n",
"***** performance of AI ******\n",
"\n",
"(tn, fp, fn, tp) = (767792, 112187, 3, 227)\n",
"\n",
"size of original set: 880209\n",
"number of positives in original set: 230\n",
"fraction of positives in original set: 0.0002613\n",
"\n",
"size of reduced set: 112414\n",
"number of positives in reduced set: 227\n",
"fraction of positives in reduced set: 0.002019\n",
"\n"
]
}
],
"source": [
"def heuristic_predict_core(period, number_deleted_repeat_units):\n",
" target_period_min = sv_len_lower/float(number_deleted_repeat_units)\n",
" target_period_min = target_period_min * (1.0 - slop_in_target_period)\n",
" target_period_max = sv_len_upper/float(number_deleted_repeat_units)\n",
" target_period_max = target_period_max * (1.0 + slop_in_target_period)\n",
" if period > target_period_min and period < target_period_max:\n",
" return True\n",
" return False\n",
"\n",
"def heuristic_predict(period):\n",
" if heuristic_predict_core(period, number_deleted_repeat_units=1):\n",
" return 1\n",
" if heuristic_predict_core(period, number_deleted_repeat_units=2):\n",
" return 1\n",
" if heuristic_predict_core(period, number_deleted_repeat_units=3):\n",
" return 1\n",
" if heuristic_predict_core(period, number_deleted_repeat_units=4):\n",
" return 1\n",
" return 0\n",
"\n",
"def update_dataframe_with_heuristic_predictions(df): \n",
" df['heuristic_prediction'] = df['period'].map(heuristic_predict)\n",
" return df \n",
"\n",
"df_ = update_dataframe_with_heuristic_predictions(df_)\n",
"\n",
"def performance(df, model): \n",
" print('***** performance of {} ******'.format(model))\n",
" print('')\n",
"\n",
" prediction_label = model + '_prediction'\n",
" tn, fp, fn, tp = confusion_matrix(df['observation'], df[prediction_label]).ravel()\n",
" print('(tn, fp, fn, tp) =', (tn, fp, fn, tp))\n",
" print('')\n",
" \n",
" compute_enrichment(tn, fp, fn, tp)\n",
"\n",
"performance(df_, 'heuristic')\n",
"performance(df_, 'AI')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Heuristic model has a high false-negative rate partly because data is noisy \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>chromosome</th>\n",
" <th>start</th>\n",
" <th>end</th>\n",
" <th>start_normalized</th>\n",
" <th>length</th>\n",
" <th>simple_repeat_tag_name</th>\n",
" <th>period</th>\n",
" <th>copy_number</th>\n",
" <th>percentage_match</th>\n",
" <th>percentage_indel</th>\n",
" <th>...</th>\n",
" <th>entropy</th>\n",
" <th>repeat_unit</th>\n",
" <th>log10_period</th>\n",
" <th>log10_length</th>\n",
" <th>log10_copy_number</th>\n",
" <th>locus</th>\n",
" <th>IGV</th>\n",
" <th>observation</th>\n",
" <th>AI_prediction</th>\n",
" <th>heuristic_prediction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>chr1</td>\n",
" <td>3016318</td>\n",
" <td>3016507</td>\n",
" <td>0.012116</td>\n",
" <td>189</td>\n",
" <td>trf</td>\n",
" <td>23</td>\n",
" <td>8.6</td>\n",
" <td>75</td>\n",
" <td>15</td>\n",
" <td>...</td>\n",
" <td>1.17</td>\n",
" <td>GAAGGGAGAGAGGGAGGAGAGAG</td>\n",
" <td>1.361728</td>\n",
" <td>2.276462</td>\n",
" <td>0.934498</td>\n",
" <td>chr1:3016318-3016507</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>chr1</td>\n",
" <td>3016323</td>\n",
" <td>3016689</td>\n",
" <td>0.012116</td>\n",
" <td>366</td>\n",
" <td>trf</td>\n",
" <td>31</td>\n",
" <td>12.2</td>\n",
" <td>69</td>\n",
" <td>16</td>\n",
" <td>...</td>\n",
" <td>1.15</td>\n",
" <td>GAGGGAGGGAGGAGAGAGGAAGGAAGGAGG</td>\n",
" <td>1.491362</td>\n",
" <td>2.563481</td>\n",
" <td>1.086360</td>\n",
" <td>chr1:3016323-3016689</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>chr1</td>\n",
" <td>3016348</td>\n",
" <td>3016557</td>\n",
" <td>0.012116</td>\n",
" <td>209</td>\n",
" <td>trf</td>\n",
" <td>46</td>\n",
" <td>4.6</td>\n",
" <td>68</td>\n",
" <td>21</td>\n",
" <td>...</td>\n",
" <td>1.13</td>\n",
" <td>AGGAGGGAGGGAGGGAGGAGAGAGGAAGGAGAGAGGGAGAGAGGG</td>\n",
" <td>1.662758</td>\n",
" <td>2.320146</td>\n",
" <td>0.662758</td>\n",
" <td>chr1:3016348-3016557</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>chr1</td>\n",
" <td>3016349</td>\n",
" <td>3016660</td>\n",
" <td>0.012116</td>\n",
" <td>311</td>\n",
" <td>trf</td>\n",
" <td>4</td>\n",
" <td>80.0</td>\n",
" <td>63</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>1.14</td>\n",
" <td>GGAG</td>\n",
" <td>0.602060</td>\n",
" <td>2.492760</td>\n",
" <td>1.903090</td>\n",
" <td>chr1:3016349-3016660</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>chr1</td>\n",
" <td>144136861</td>\n",
" <td>144141870</td>\n",
" <td>0.578964</td>\n",
" <td>5009</td>\n",
" <td>trf</td>\n",
" <td>18</td>\n",
" <td>276.2</td>\n",
" <td>68</td>\n",
" <td>22</td>\n",
" <td>...</td>\n",
" <td>1.57</td>\n",
" <td>CATCATTTCATCTCATTT</td>\n",
" <td>1.255273</td>\n",
" <td>3.699751</td>\n",
" <td>2.441224</td>\n",
" <td>chr1:144136861-144141870</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>224</th>\n",
" <td>chr21</td>\n",
" <td>10651736</td>\n",
" <td>10738591</td>\n",
" <td>0.228040</td>\n",
" <td>86855</td>\n",
" <td>trf</td>\n",
" <td>5</td>\n",
" <td>17339.8</td>\n",
" <td>68</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>1.80</td>\n",
" <td>TCCAT</td>\n",
" <td>0.698970</td>\n",
" <td>4.938795</td>\n",
" <td>4.239044</td>\n",
" <td>chr21:10651736-10738591</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>225</th>\n",
" <td>chr21</td>\n",
" <td>31783961</td>\n",
" <td>31785489</td>\n",
" <td>0.680453</td>\n",
" <td>1528</td>\n",
" <td>trf</td>\n",
" <td>3</td>\n",
" <td>506.3</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>1.37</td>\n",
" <td>CCA</td>\n",
" <td>0.477121</td>\n",
" <td>3.184123</td>\n",
" <td>2.704408</td>\n",
" <td>chr21:31783961-31785489</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>226</th>\n",
" <td>chr21</td>\n",
" <td>39583817</td>\n",
" <td>39584123</td>\n",
" <td>0.847438</td>\n",
" <td>306</td>\n",
" <td>trf</td>\n",
" <td>5</td>\n",
" <td>61.2</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.97</td>\n",
" <td>TCCCT</td>\n",
" <td>0.698970</td>\n",
" <td>2.485721</td>\n",
" <td>1.786751</td>\n",
" <td>chr21:39583817-39584123</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>228</th>\n",
" <td>chrX</td>\n",
" <td>84191522</td>\n",
" <td>84191993</td>\n",
" <td>0.539548</td>\n",
" <td>471</td>\n",
" <td>trf</td>\n",
" <td>8</td>\n",
" <td>58.9</td>\n",
" <td>71</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.49</td>\n",
" <td>TATATATA</td>\n",
" <td>0.903090</td>\n",
" <td>2.673021</td>\n",
" <td>1.770115</td>\n",
" <td>chrX:84191522-84191993</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>229</th>\n",
" <td>chrX</td>\n",
" <td>121347526</td>\n",
" <td>121347814</td>\n",
" <td>0.777665</td>\n",
" <td>288</td>\n",
" <td>trf</td>\n",
" <td>59</td>\n",
" <td>4.9</td>\n",
" <td>88</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>1.84</td>\n",
" <td>GAGTAGAGACACGGAGAGAAGGGGTCGGGGGGTTCTTGCCTCCTAG...</td>\n",
" <td>1.770852</td>\n",
" <td>2.459392</td>\n",
" <td>0.690196</td>\n",
" <td>chrX:121347526-121347814</td>\n",
" <td>&lt;a target=\"_blank\" href=\"http://0.0.0.0:5000/?...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>114 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" chromosome start end start_normalized length \\\n",
"0 chr1 3016318 3016507 0.012116 189 \n",
"1 chr1 3016323 3016689 0.012116 366 \n",
"2 chr1 3016348 3016557 0.012116 209 \n",
"3 chr1 3016349 3016660 0.012116 311 \n",
"8 chr1 144136861 144141870 0.578964 5009 \n",
".. ... ... ... ... ... \n",
"224 chr21 10651736 10738591 0.228040 86855 \n",
"225 chr21 31783961 31785489 0.680453 1528 \n",
"226 chr21 39583817 39584123 0.847438 306 \n",
"228 chrX 84191522 84191993 0.539548 471 \n",
"229 chrX 121347526 121347814 0.777665 288 \n",
"\n",
" simple_repeat_tag_name period copy_number percentage_match \\\n",
"0 trf 23 8.6 75 \n",
"1 trf 31 12.2 69 \n",
"2 trf 46 4.6 68 \n",
"3 trf 4 80.0 63 \n",
"8 trf 18 276.2 68 \n",
".. ... ... ... ... \n",
"224 trf 5 17339.8 68 \n",
"225 trf 3 506.3 82 \n",
"226 trf 5 61.2 100 \n",
"228 trf 8 58.9 71 \n",
"229 trf 59 4.9 88 \n",
"\n",
" percentage_indel ... entropy \\\n",
"0 15 ... 1.17 \n",
"1 16 ... 1.15 \n",
"2 21 ... 1.13 \n",
"3 9 ... 1.14 \n",
"8 22 ... 1.57 \n",
".. ... ... ... \n",
"224 5 ... 1.80 \n",
"225 3 ... 1.37 \n",
"226 0 ... 0.97 \n",
"228 0 ... 1.49 \n",
"229 4 ... 1.84 \n",
"\n",
" repeat_unit log10_period \\\n",
"0 GAAGGGAGAGAGGGAGGAGAGAG 1.361728 \n",
"1 GAGGGAGGGAGGAGAGAGGAAGGAAGGAGG 1.491362 \n",
"2 AGGAGGGAGGGAGGGAGGAGAGAGGAAGGAGAGAGGGAGAGAGGG 1.662758 \n",
"3 GGAG 0.602060 \n",
"8 CATCATTTCATCTCATTT 1.255273 \n",
".. ... ... \n",
"224 TCCAT 0.698970 \n",
"225 CCA 0.477121 \n",
"226 TCCCT 0.698970 \n",
"228 TATATATA 0.903090 \n",
"229 GAGTAGAGACACGGAGAGAAGGGGTCGGGGGGTTCTTGCCTCCTAG... 1.770852 \n",
"\n",
" log10_length log10_copy_number locus \\\n",
"0 2.276462 0.934498 chr1:3016318-3016507 \n",
"1 2.563481 1.086360 chr1:3016323-3016689 \n",
"2 2.320146 0.662758 chr1:3016348-3016557 \n",
"3 2.492760 1.903090 chr1:3016349-3016660 \n",
"8 3.699751 2.441224 chr1:144136861-144141870 \n",
".. ... ... ... \n",
"224 4.938795 4.239044 chr21:10651736-10738591 \n",
"225 3.184123 2.704408 chr21:31783961-31785489 \n",
"226 2.485721 1.786751 chr21:39583817-39584123 \n",
"228 2.673021 1.770115 chrX:84191522-84191993 \n",
"229 2.459392 0.690196 chrX:121347526-121347814 \n",
"\n",
" IGV observation \\\n",
"0 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
"1 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
"2 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
"3 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
"8 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
".. ... ... \n",
"224 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
"225 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
"226 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
"228 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
"229 <a target=\"_blank\" href=\"http://0.0.0.0:5000/?... 1 \n",
"\n",
" AI_prediction heuristic_prediction \n",
"0 1 0 \n",
"1 1 0 \n",
"2 1 0 \n",
"3 1 0 \n",
"8 1 0 \n",
".. ... ... \n",
"224 1 0 \n",
"225 1 0 \n",
"226 1 0 \n",
"228 1 0 \n",
"229 1 0 \n",
"\n",
"[114 rows x 24 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def false_negatives(df, model): \n",
" prediction_label = model + '_prediction'\n",
" predicted_not_to_contain_call_ = df[prediction_label] == 0\n",
" does_contain_call_ = df['observation'] == 1\n",
" return df[predicted_not_to_contain_call_ & does_contain_call_]\n",
"\n",
"# prettify(false_negatives(df_, 'heuristic'), include_heuristic_prediction=True)\n",
"false_negatives(df_, 'heuristic')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following, we visualize one of the false negatives above to show that: (i) `X` is noisy in the sense that the repeat annotation of the sequence is poor and (ii) `y` is noisy in the sense that a tandem repeat annotation that contains an SV of length 100bp, as judged from a vcf record, might actually contain an SV of lesser or greater length instead. \n",
"\n",
"It is therefore not surprising that the heuristic model fails to flag such tandem repeats as containing SVs as the heuristic model relies heavily on the accuracy of `X` and `y`. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"igv_1727976\"></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import igv\n",
"\n",
"reference = \"GRCh38_full_analysis_set_plus_decoy_hla\"\n",
"\n",
"def render_igv(locus_):\n",
" igv_instance = igv.Browser({\n",
" \"locus\": locus_,\n",
" \"showCursorTrackingGuide\": True,\n",
" \"reference\": {\n",
" \"fastaURL\": reference + \".fa\",\n",
" \"indexURL\": reference + \".fa.fai\",\n",
" }\n",
" })\n",
"\n",
" igv_instance.load_track({\n",
" \"type\": \"annotation\",\n",
" \"format\": \"bed\",\n",
" \"url\": \"simple_repeats.bed.gz\",\n",
" \"indexURL\": \"simple_repeats.bed.gz.tbi\",\n",
" \"displayMode\": \"expanded\",\n",
" \"name\": \"tandem repeats\",\n",
" \"height\": 200\n",
" })\n",
" \n",
" igv_instance.load_track({\n",
" \"type\": \"variant\",\n",
" \"format\": \"vcf\",\n",
" \"url\": \"{}.BIP-unified.filtered.vcf.gz\".format(sample),\n",
" \"indexURL\": \"{}.BIP-unified.filtered.vcf.gz.tbi\".format(sample),\n",
" \"name\": \"pacbio calls\",\n",
" \"homrefColor\": \"rgb(255,0,0)\",\n",
" \"hetvarColor\": \"rgb(0,255,0)\",\n",
" \"homvarColor\": \"rgb(0,0,255)\",\n",
" \"height\": 75\n",
" }) \n",
" \n",
" igv_instance.load_track({\n",
" \"type\": \"alignment\",\n",
" \"format\": \"bam\",\n",
" \"url\": \"{}.h0.bam\".format(sample),\n",
" \"indexURL\": \"{}.h0.bam.bai\".format(sample),\n",
" \"height\": 150,\n",
" \"showSoftClips\": True,\n",
" \"name\": \"haplotype 0 pacbio contig\"\n",
" }) \n",
"\n",
" igv_instance.load_track({\n",
" \"type\": \"alignment\",\n",
" \"format\": \"bam\",\n",
" \"url\": \"{}.h1.bam\".format(sample),\n",
" \"indexURL\": \"{}.h1.bam.bai\".format(sample),\n",
" \"height\": 150,\n",
" \"showSoftClips\": True,\n",
" \"name\": \"haplotype 1 pacbio contig\"\n",
" }) \n",
"\n",
" return igv_instance\n",
" \n",
"locus_1 = render_igv(\"chr1:144,139,191-144,139,839\")\n",
"locus_1.show()\n",
"locus_1.get_svg()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Awaiting SVG - try again in a few seconds'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"locus_1.display_svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But sometimes, a false-negative tandem repeat overlaps another tandem repeat annotation that the heuristic classifier would predict to contain an SV (based upon the period of the tandem repeat): "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"igv_479760\"></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"locus_2 = render_igv(\"chr6:101,761,761-101,762,055\")\n",
"locus_2.show()\n",
"locus_2.get_svg()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Awaiting SVG - try again in a few seconds'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"locus_2.display_svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thus, we should really map predictions on tandem repeats onto predictions on disjoint genomic intervals resulting from merging tandem repeats. \n",
"\n",
"This is what we did next..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trained classifier (decision tree) has lowest false-negative and false-positive rates on merged tandem-repeat intervals"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"***** performance of heuristic ******\n",
"\n",
"(tn, fp, fn, tp) = (549693, 66338, 40, 101)\n",
"\n",
"size of original set: 616172\n",
"number of positives in original set: 141\n",
"fraction of positives in original set: 0.0002288\n",
"\n",
"size of reduced set: 66439\n",
"number of positives in reduced set: 101\n",
"fraction of positives in reduced set: 0.00152\n",
"\n",
"***** performance of AI ******\n",
"\n",
"(tn, fp, fn, tp) = (553916, 62115, 3, 138)\n",
"\n",
"size of original set: 616172\n",
"number of positives in original set: 141\n",
"fraction of positives in original set: 0.0002288\n",
"\n",
"size of reduced set: 62253\n",
"number of positives in reduced set: 138\n",
"fraction of positives in reduced set: 0.002217\n",
"\n"
]
}
],
"source": [
"def compute_region_label(TR_labels): \n",
" TR_labels_ = [int(TR_label) for TR_label in TR_labels.split(',')]\n",
" return 1 if 1 in TR_labels_ else 0\n",
" \n",
"def merge_tandem_repeats_from_train_dev_test_set(df): \n",
" df[[\n",
" 'chromosome', \n",
" 'start', \n",
" 'end', \n",
" 'observation', \n",
" 'AI_prediction', \n",
" 'heuristic_prediction'\n",
" ]].to_csv(\n",
" path_or_buf='tandem_repeats.tmp.bed', \n",
" sep='\\t', \n",
" header=False,\n",
" index=False\n",
" )\n",
"\n",
" ! sort --version-sort -k1,1 -k2,2 \"tandem_repeats.tmp.bed\" > \"tandem_repeats.tmp.sorted.bed\"\n",
" \n",
" ! bedtools merge -i \"tandem_repeats.tmp.sorted.bed\" -c 4,5,6 -o collapse,collapse,collapse \\\n",
" | sort --version-sort -k1,1 -k2,2 \\\n",
" > \"tandem_repeats.tmp.merged.sorted.bed\"\n",
" \n",
" data = []\n",
" with open(\"tandem_repeats.tmp.merged.sorted.bed\", \"r\") as f: \n",
" for line in f: \n",
" (chromosome, \n",
" start, \n",
" end, \n",
" TR_observations, \n",
" TR_AI_predictions, \n",
" TR_heuristic_predictions) = line.strip().split('\\t')\n",
" \n",
" region_observation = compute_region_label(TR_observations)\n",
" region_AI_prediction = compute_region_label(TR_AI_predictions)\n",
" region_heuristic_prediction = compute_region_label(TR_heuristic_predictions)\n",
"\n",
" data.append([\n",
" chromosome,\n",
" start,\n",
" end,\n",
" TR_observations, \n",
" TR_AI_predictions, \n",
" TR_heuristic_predictions,\n",
" region_observation,\n",
" region_AI_prediction,\n",
" region_heuristic_prediction\n",
" ])\n",
"\n",
" df = pd.DataFrame(data, columns=[\n",
" \"chromosome\", \n",
" \"start\", \n",
" \"end\",\n",
" \"TR_obs\", \n",
" \"TR_AI_pred\", \n",
" \"TR_heuristic_pred\",\n",
" \"observation\",\n",
" \"AI_prediction\",\n",
" \"heuristic_prediction\"\n",
" ])\n",
" \n",
" ! rm tandem_repeats.tmp.bed tandem_repeats.tmp.sorted.bed tandem_repeats.tmp.merged.sorted.bed\n",
"\n",
" return df\n",
"\n",
"df_merged = merge_tandem_repeats_from_train_dev_test_set(df_)\n",
"\n",
"performance(df_merged, 'heuristic')\n",
"performance(df_merged, 'AI')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge all tandem repeats (not just those in training-dev-test set) "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def compress_and_index(filename): \n",
" ! bgzip --force --stdout $filename\".bed\" > $filename\".bed.gz\"\n",
" ! tabix --force --preset bed $filename\".bed.gz\"\n",
"\n",
"def merge_tandem_repeats(): \n",
" csv_file = '/scratch/ucgd/lustre-work/quinlan/u6018199/chaisson_2019/repeats/simple-repeats_complete-annotations.csv'\n",
" X, df = create_X_df(csv_file) \n",
"\n",
" df['AI_prediction'] = trained_AI.predict(X) \n",
"\n",
" df[[\n",
" 'chromosome', \n",
" 'start', \n",
" 'end', \n",
" 'AI_prediction', \n",
" ]].to_csv(\n",
" path_or_buf='tandem_repeats.bed', \n",
" sep='\\t', \n",
" header=False,\n",
" index=False\n",
" )\n",
" \n",
" ! sort --version-sort -k1,1 -k2,2 \"tandem_repeats.bed\" > \"tandem_repeats.sorted.bed\"\n",
" \n",
" ! bedtools merge -i \"tandem_repeats.sorted.bed\" -c 4 -o collapse \\\n",
" | sort --version-sort -k1,1 -k2,2 \\\n",
" > \"tandem_repeats.merged.sorted.bed\"\n",
" \n",
" compress_and_index(\"tandem_repeats.merged.sorted\")\n",
" \n",
" data = []\n",
" with open(\"tandem_repeats.merged.sorted.bed\", \"r\") as f: \n",
" for line in f: \n",
" (chromosome, \n",
" start, \n",
" end, \n",
" TR_AI_predictions) = line.strip().split('\\t')\n",
" \n",
" region_AI_prediction = compute_region_label(TR_AI_predictions)\n",
"\n",
" data.append([\n",
" chromosome,\n",
" start,\n",
" end,\n",
" TR_AI_predictions, \n",
" region_AI_prediction\n",
" ])\n",
"\n",
" df = pd.DataFrame(data, columns=[\n",
" \"chromosome\", \n",
" \"start\", \n",
" \"end\",\n",
" \"TR_AI_pred\", \n",
" \"AI_prediction\"\n",
" ])\n",
" \n",
" return df \n",
"\n",
"merged_tandem_repeat_intervals = merge_tandem_repeats()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select tandem-repeat intervals predicted to contain an SV"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"intervals_predicted_to_contain_SV_str = '{}.{}.gt{}.lt{}.intervals_predicted_to_contain_SV'.format(\n",
" sample, svtype, absolute_SV_length_lower, absolute_SV_length_upper\n",
")\n",
"\n",
"def select_tandem_repeat_intervals(df): \n",
" df[df['AI_prediction'] == 1].to_csv(\n",
" path_or_buf=intervals_predicted_to_contain_SV_str+'.bed', \n",
" sep='\\t', \n",
" header=False,\n",
" index=False\n",
" )\n",
"\n",
" ! sort --version-sort -k1,1 -k2,2 $intervals_predicted_to_contain_SV_str\".bed\" \\\n",
" > $intervals_predicted_to_contain_SV_str\".sorted.bed\"\n",
" \n",
" ! rm $intervals_predicted_to_contain_SV_str\".bed\"\n",
"\n",
"select_tandem_repeat_intervals(merged_tandem_repeat_intervals)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Annotate selected tandem-repeat intervals with short-read coverage"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def annotate():\n",
" ! ./strip_bed.sh $intervals_predicted_to_contain_SV_str\".sorted.bed\" \\\n",
" > $intervals_predicted_to_contain_SV_str\".sorted.stripped.bed\"\n",
"\n",
" mosdepth_prefix = intervals_predicted_to_contain_SV_str + '.sorted.stripped'\n",
" short_reads = '{}.alt_bwamem_GRCh38DH.20150715.{}.high_coverage'.format(sample, population)\n",
"\n",
" ! mosdepth \\\n",
" --no-per-base \\\n",
" --fast-mode \\\n",
" --threads 16 \\\n",
" --by $intervals_predicted_to_contain_SV_str\".sorted.stripped.bed\" \\\n",
" --fasta $reference\".fa\" \\\n",
" $mosdepth_prefix \\\n",
" $short_reads\".cram\"\n",
" \n",
" ! gunzip --stdout --force $mosdepth_prefix\".regions.bed.gz\" \\\n",
" > $mosdepth_prefix\".coverage.bed\" \n",
" \n",
" # for igv.js: \n",
" ! tabix --preset bed $mosdepth_prefix\".regions.bed.gz\" \n",
" \n",
"# annotate()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.16"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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