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Created October 27, 2018 23:24
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Tommy_Nans.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Tommy_Nans.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/AvantiShri/6428ca274e55c8d242f3429ee9ca42be/tommy_nans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "ee7Y0A1rpTGK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104
},
"outputId": "d0c76832-4ab0-4ffc-8093-033482cc5b92"
},
"cell_type": "code",
"source": [
"!curl -L \"https://drive.google.com/uc?authuser=0&id=1L4mebLr97Y7FLG4RjkzkrjpfXLajkHVD&export=download\" > NaN_Zipped.zip\n",
"\n"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 266 0 266 0 0 429 0 --:--:-- --:--:-- --:--:-- 428\n",
"100 388 0 388 0 0 308 0 --:--:-- 0:00:01 --:--:-- 0\n",
"100 308k 100 308k 0 0 191k 0 0:00:01 0:00:01 --:--:-- 191k\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "zRBbb4KFpgRA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 242
},
"outputId": "439efdf6-b560-4915-c58b-d9ddcf09abce"
},
"cell_type": "code",
"source": [
"!unzip NaN_Zipped.zip"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Archive: NaN_Zipped.zip\n",
" creating: NaN_Zipped/\n",
" inflating: NaN_Zipped/NaN Issue Replication.ipynb \n",
" inflating: NaN_Zipped/GSE31332_hotair_oe_peaks.bed.pos.train.fa-small \n",
" creating: __MACOSX/\n",
" creating: __MACOSX/NaN_Zipped/\n",
" inflating: __MACOSX/NaN_Zipped/._GSE31332_hotair_oe_peaks.bed.pos.train.fa-small \n",
" inflating: NaN_Zipped/GSE31332_hotair_oe_peaks.bed.neg.train.fa-small \n",
" inflating: __MACOSX/NaN_Zipped/._GSE31332_hotair_oe_peaks.bed.neg.train.fa-small \n",
" inflating: NaN_Zipped/GSE31332_hotair_oe_peaks.bed.neg.test.fa-small \n",
" inflating: __MACOSX/NaN_Zipped/._GSE31332_hotair_oe_peaks.bed.neg.test.fa-small \n",
" inflating: NaN_Zipped/GSE31332_hotair_oe_peaks.bed.pos.test.fa-small \n",
" inflating: __MACOSX/NaN_Zipped/._GSE31332_hotair_oe_peaks.bed.pos.test.fa-small \n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "Ek0eA4eBpiBK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "9f4207d7-cd8c-4e98-fdbf-fa7d1dbaed3f"
},
"cell_type": "code",
"source": [
"%cd NaN_Zipped"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/NaN_Zipped\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "xmoN-qqpqTAu",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "e5564599-8643-4755-94ee-bab55fcaa703"
},
"cell_type": "code",
"source": [
"!pip install biopython\n",
"!pip install deeplift"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting biopython\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/64/1a/ee21f05bd9d5e215bee7204f4d0280a8de0323ac1c51aa5a911b2436de9f/biopython-1.72-cp36-cp36m-manylinux1_x86_64.whl (2.1MB)\n",
"\u001b[K 100% |████████████████████████████████| 2.1MB 10.6MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from biopython) (1.14.6)\n",
"Installing collected packages: biopython\n",
"Successfully installed biopython-1.72\n",
"Collecting deeplift\n",
" Downloading https://files.pythonhosted.org/packages/ee/de/ae89fcdac328badf298d6181d2a38822a353e655771be09a3a80d3db8b84/deeplift-0.6.7.1.tar.gz\n",
"Requirement already satisfied: numpy>=1.9 in /usr/local/lib/python3.6/dist-packages (from deeplift) (1.14.6)\n",
"Building wheels for collected packages: deeplift\n",
" Running setup.py bdist_wheel for deeplift ... \u001b[?25l-\b \bdone\n",
"\u001b[?25h Stored in directory: /root/.cache/pip/wheels/de/c4/b0/ec9deaa25dfaad60ad234f1d17b0339a8a9f5bfe4d7de42edd\n",
"Successfully built deeplift\n",
"Installing collected packages: deeplift\n",
"Successfully installed deeplift-0.6.7.1\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "97iAeXHuqaNe",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 589
},
"outputId": "96d2fd66-69d4-4553-d311-a315e1321afa"
},
"cell_type": "code",
"source": [
"!git clone https://github.com/kundajelab/tfmodisco\n",
"%cd tfmodisco\n",
"!git checkout v0.4.2.2-alpha\n",
"!pip install .\n",
"%cd .."
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'tfmodisco'...\n",
"remote: Enumerating objects: 30, done.\u001b[K\n",
"remote: Counting objects: 100% (30/30), done.\u001b[K\n",
"remote: Compressing objects: 100% (22/22), done.\u001b[K\n",
"remote: Total 2513 (delta 10), reused 20 (delta 6), pack-reused 2483\u001b[K\n",
"Receiving objects: 100% (2513/2513), 31.83 MiB | 18.91 MiB/s, done.\n",
"Resolving deltas: 100% (1641/1641), done.\n",
"/content/NaN_Zipped/tfmodisco\n",
"Note: checking out 'v0.4.2.2-alpha'.\n",
"\n",
"You are in 'detached HEAD' state. You can look around, make experimental\n",
"changes and commit them, and you can discard any commits you make in this\n",
"state without impacting any branches by performing another checkout.\n",
"\n",
"If you want to create a new branch to retain commits you create, you may\n",
"do so (now or later) by using -b with the checkout command again. Example:\n",
"\n",
" git checkout -b <new-branch-name>\n",
"\n",
"HEAD is now at e5268ab Update setup.py\n",
"Processing /content/NaN_Zipped/tfmodisco\n",
"Requirement already satisfied: numpy>=1.9 in /usr/local/lib/python3.6/dist-packages (from modisco==0.4.2.2) (1.14.6)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from modisco==0.4.2.2) (0.12.5)\n",
"Requirement already satisfied: scikit-learn>=0.19 in /usr/local/lib/python3.6/dist-packages (from modisco==0.4.2.2) (0.19.2)\n",
"Requirement already satisfied: h5py>=2.5 in /usr/local/lib/python3.6/dist-packages (from modisco==0.4.2.2) (2.8.0)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py>=2.5->modisco==0.4.2.2) (1.11.0)\n",
"Building wheels for collected packages: modisco\n",
" Running setup.py bdist_wheel for modisco ... \u001b[?25l-\b \b\\\b \bdone\n",
"\u001b[?25h Stored in directory: /tmp/pip-ephem-wheel-cache-4205x_fp/wheels/77/19/ef/6f5e2a9372dc2189d77767e01fce845ca19807dd983c3913ef\n",
"Successfully built modisco\n",
"Installing collected packages: modisco\n",
"Successfully installed modisco-0.4.2.2\n",
"/content/NaN_Zipped\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "LdIxRn-WpoFv",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
},
"outputId": "6f8b1e1d-ce96-4a62-c1f0-2167bb37b36b"
},
"cell_type": "code",
"source": [
"from __future__ import print_function\n",
"import h5py\n",
"import os\n",
"\n",
"from collections import OrderedDict # used for background noise elements\n",
"\n",
"import numpy as np\n",
"import random\n",
"from Bio import SeqIO\n",
"import json\n",
"from sklearn import metrics\n",
"from collections import Counter\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from keras.models import Sequential\n",
"from keras.layers.convolutional import Conv1D\n",
"from keras.layers import Dense, Flatten, Activation, Dropout, AveragePooling1D\n",
"from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor\n",
"from keras.initializers import VarianceScaling\n",
"from keras.models import model_from_json\n",
"\n",
"import deeplift\n",
"from deeplift.layers import NonlinearMxtsMode\n",
"import deeplift.conversion.kerasapi_conversion as kc\n",
"from deeplift.util import compile_func, get_shuffle_seq_ref_function, get_hypothetical_contribs_func_onehot\n",
"\n",
"from deeplift.dinuc_shuffle import dinuc_shuffle #function to do a dinucleotide shuffle\n",
"\n",
"import modisco as modisco\n",
"import modisco.backend\n",
"import modisco.nearest_neighbors\n",
"import modisco.affinitymat\n",
"import modisco.tfmodisco_workflow.seqlets_to_patterns\n",
"import modisco.tfmodisco_workflow.workflow\n",
"import modisco.aggregator\n",
"import modisco.cluster\n",
"import modisco.core\n",
"import modisco.util\n",
"import modisco.coordproducers\n",
"import modisco.metaclusterers\n",
"from modisco.visualization import viz_sequence \n",
"\n",
"\n",
"#only applicable on GPU supported systems, this solves a memory issue.\n",
"import tensorflow as tf\n",
"config = tf.ConfigProto()\n",
"config.gpu_options.allow_growth = True\n",
"session = tf.Session(config=config)\n",
"\n",
"#%run Functions.ipynb\n",
"%matplotlib inline\n",
"\n",
"print(\"imports loaded\")"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n",
"TF-MoDISco is using the TensorFlow backend.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"imports loaded\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "Z3Gl9AoZprYw",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"############################\n",
"# Common Functions Cell #\n",
"############################\n",
"\n",
"\n",
"#this is set up for 1d convolutions where examples credit to DeepLift for this encoding scheme\n",
"#have dimensions (len, num_channels)\n",
"#the channel axis is the axis for one-hot encoding.\n",
"def one_hot_encode_along_channel_axis(sequence, length, classVal):\n",
" to_return = np.zeros((length,4), dtype=np.int8)\n",
" \n",
" seq_to_one_hot_fill_in_array(zeros_array=to_return,\n",
" sequence=sequence, one_hot_axis=1, length = length, classVal = classVal)\n",
" return to_return\n",
"\n",
"#same as above, build a one-hot array\n",
"def seq_to_one_hot_fill_in_array(zeros_array, sequence, one_hot_axis, length, classVal):\n",
" assert one_hot_axis==0 or one_hot_axis==1\n",
" if (one_hot_axis==0):\n",
" assert zeros_array.shape[1] == length\n",
" elif (one_hot_axis==1): \n",
" assert zeros_array.shape[0] == length\n",
" #will mutate zeros_array\n",
" for (i,char) in enumerate(sequence):\n",
" #print(i, \" and \", char)\n",
" if (char==\"A\" or char==\"a\"):\n",
" char_idx = 0\n",
" elif (char==\"C\" or char==\"c\"):\n",
" char_idx = 1\n",
" elif (char==\"G\" or char==\"g\"):\n",
" char_idx = 2\n",
" elif (char==\"T\" or char==\"t\"):\n",
" char_idx = 3\n",
" elif (char==\"N\" or char==\"n\"):\n",
" continue #leave that pos as all 0's\n",
" else:\n",
" raise RuntimeError(\"Unsupported character: \"+str(char))\n",
" if (one_hot_axis==0):\n",
" zeros_array[char_idx,i] = 1\n",
" elif (one_hot_axis==1):\n",
" zeros_array[i,char_idx] = 1\n",
" if(classVal != -99):\n",
" if (one_hot_axis==0):\n",
" zeros_array[0,length-1] = classVal\n",
" zeros_array[1,length-1] = classVal\n",
" zeros_array[2,length-1] = classVal\n",
" zeros_array[3,length-1] = classVal\n",
" elif (one_hot_axis==1):\n",
" zeros_array[length-1,0] = classVal\n",
" zeros_array[length-1,1] = classVal\n",
" zeros_array[length-1,2] = classVal\n",
" zeros_array[length-1,3] = classVal\n",
"\n",
"#classless version of above\n",
"def one_hot_encode_along_channel_axis_core(sequence, length):\n",
" to_return = np.zeros((length,4), dtype=np.int8)\n",
" \n",
" seq_to_one_hot_fill_in_array_core(zeros_array=to_return,\n",
" sequence=sequence, one_hot_axis=1, length = length)\n",
" return to_return\n",
"\n",
"#classless version of above\n",
"def seq_to_one_hot_fill_in_array_core(zeros_array, sequence, one_hot_axis, length):\n",
" assert one_hot_axis==0 or one_hot_axis==1\n",
" if (one_hot_axis==0):\n",
" assert zeros_array.shape[1] == length\n",
" elif (one_hot_axis==1): \n",
" assert zeros_array.shape[0] == length\n",
" #will mutate zeros_array\n",
" for (i,char) in enumerate(sequence):\n",
" #print(i, \" and \", char)\n",
" if (char==\"A\" or char==\"a\"):\n",
" char_idx = 0\n",
" elif (char==\"C\" or char==\"c\"):\n",
" char_idx = 1\n",
" elif (char==\"G\" or char==\"g\"):\n",
" char_idx = 2\n",
" elif (char==\"T\" or char==\"t\"):\n",
" char_idx = 3\n",
" elif (char==\"N\" or char==\"n\"):\n",
" continue #leave that pos as all 0's\n",
" else:\n",
" raise RuntimeError(\"Unsupported character: \"+str(char))\n",
" if (one_hot_axis==0):\n",
" zeros_array[char_idx,i] = 1\n",
" elif (one_hot_axis==1):\n",
" zeros_array[i,char_idx] = 1\n",
"\n",
"\n",
"#take a file, and parse it into a list of sequences, and return stats about the file\n",
"#the list will have the list of sequences after we're done here\n",
"def getBasicSequenceFromFile(theFile, theList):\n",
" longestLength = 0\n",
" shortestLength = 999999\n",
" shortestLengths = []\n",
" idx = 0\n",
" shortIdx = 0\n",
" with open(theFile) as parseFile:\n",
" for seq_record in SeqIO.parse(parseFile, 'fasta'):\n",
" workingSeq = str(seq_record.seq).upper()\n",
" theList.append(workingSeq)\n",
" if(longestLength < len(workingSeq)):\n",
" longestLength = len(workingSeq)\n",
" if(shortestLength > len(workingSeq)):\n",
" \n",
" shortestLengths.append(len(workingSeq))\n",
" shortIdx = idx\n",
" idx = idx + 1\n",
" \n",
" return longestLength, shortestLengths, shortIdx\n",
"\n",
"#Keras outputs its JSON with an extra 'Layers\" parent, DeepLift can't parse\n",
"#this object, so this function makes the conversion\n",
"def convertKerasJSONtoDeepLIFT(kerasJSON_str):\n",
" jsonData = json.loads(kerasJSON_str)\n",
" layersData = jsonData[\"config\"][\"layers\"]\n",
" jsonData[\"config\"] = layersData\n",
" return json.dumps(jsonData)\n",
"\n",
"\n",
"#create a model given a longest sequence length\n",
"# lines with #Deep are simliar to the deeplift model used in the example project\n",
"def deeps_Model1(longestLength): \n",
" # create model\n",
" kernel_init = VarianceScaling(scale=1.0, mode='fan_avg', distribution='uniform')\n",
" model = Sequential()\n",
" model.add(Conv1D(64, 11, activation = 'linear', input_shape=(longestLength,4))) #- deep\n",
" model.add(Activation(\"relu\"))#- deep\n",
" model.add(Conv1D(64, 6, activation = 'linear'))#- deep\n",
" model.add(Activation(\"relu\"))#- deep\n",
" model.add(AveragePooling1D(pool_size=int(longestLength*0.05)))\n",
" model.add(Flatten())#- deep\n",
" model.add(Dense(150, activation='linear', kernel_initializer=kernel_init, bias_initializer='zeros')) #- deep \n",
" model.add(Activation(\"relu\"))#- deep\n",
" model.add(Dropout(rate = 0.15))\n",
" model.add(Dense(1, activation='linear', kernel_initializer=kernel_init, bias_initializer='zeros')) #- deep used 3, i'm using 1\n",
" model.add(Activation('sigmoid'))#- deep\n",
" # Compile model\n",
" model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])\n",
" return model\n",
"\n",
"\n",
"\n",
"#create a model given a longest sequence length\n",
"# lines with #Deep are simliar to the deeplift model used in the example project\n",
"def deeps_Model2(longestLength): \n",
" # create model\n",
" kernel_init = VarianceScaling(scale=1.0, mode='fan_avg', distribution='uniform')\n",
" model = Sequential()\n",
" model.add(Conv1D(64, 11, activation = 'linear', input_shape=(longestLength,4))) #- deep\n",
" model.add(Activation(\"relu\"))#- deep\n",
" model.add(Conv1D(64, 11, activation = 'linear'))#- deep\n",
" model.add(Activation(\"relu\"))#- deep\n",
" model.add(AveragePooling1D(pool_size=int(longestLength*0.05)))\n",
" model.add(Flatten())#- deep\n",
" model.add(Dense(120, activation='linear', kernel_initializer=kernel_init, bias_initializer='zeros')) #- deep \n",
" model.add(Activation(\"relu\"))#- deep\n",
" model.add(Dropout(rate = 0.25))\n",
" model.add(Dense(1, activation='linear'))\n",
" model.add(Activation('sigmoid'))#- deep\n",
" # Compile model\n",
" model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])\n",
" return model\n",
"\n",
"#create a model given a longest sequence length\n",
"def deeps_Model5(longestLength): \n",
" # create model\n",
" kernel_init = VarianceScaling(scale=1.0, mode='fan_avg', distribution='uniform')\n",
" model = Sequential()\n",
" model.add(Conv1D(64, 11, activation = 'linear', input_shape=(longestLength,4))) #- deep\n",
" model.add(Activation(\"relu\"))#- deep\n",
" model.add(Conv1D(32, 6, activation = 'linear'))#- deep\n",
" model.add(Activation(\"relu\"))#- deep\n",
" model.add(AveragePooling1D(pool_size=int(longestLength*0.05)))\n",
" model.add(Flatten())#- deep\n",
" model.add(Dense(250, activation='linear', kernel_initializer=kernel_init, bias_initializer='zeros')) #- deep \n",
" model.add(Activation(\"relu\"))#- deep\n",
" model.add(Dropout(rate = 0.1))\n",
" model.add(Dense(1, activation='linear', kernel_initializer=kernel_init, bias_initializer='zeros')) #- deep used 3, i'm using 1\n",
" model.add(Activation('sigmoid'))#- deep\n",
" # Compile model\n",
" model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])\n",
" return model\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "AsfgwaYNqrQw",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# this is used to show the visuals of TF MoDISco\n",
"def printVisuals(fileToTest, iteration):\n",
" filePath = fileToTest + iteration + \"results.hdf5\"\n",
"\n",
"\n",
" hdf5_results = h5py.File(filePath,\"r\")\n",
"\n",
" print(hdf5_results)\n",
" metacluster_names = [\n",
" x.decode(\"utf-8\") for x in \n",
" list(hdf5_results[\"metaclustering_results\"]\n",
" [\"all_metacluster_names\"][:])]\n",
" all_patterns = []\n",
" print(metacluster_names)\n",
" for metacluster_name in metacluster_names:\n",
" print(metacluster_name)\n",
" metacluster_grp = (hdf5_results[\"metacluster_idx_to_submetacluster_results\"]\n",
" [metacluster_name])\n",
" print(\"activity pattern:\",metacluster_grp[\"activity_pattern\"][:])\n",
" all_pattern_names = [x.decode(\"utf-8\") for x in \n",
" list(metacluster_grp[\"seqlets_to_patterns_result\"]\n",
" [\"patterns\"][\"all_pattern_names\"][:])]\n",
" print(all_pattern_names)\n",
" if (len(all_pattern_names)==0):\n",
" print(\"No motifs found for this activity pattern\")\n",
"\n",
" for pattern_name in all_pattern_names:\n",
" print(metacluster_name, pattern_name)\n",
" all_patterns.append((metacluster_name, pattern_name))\n",
" pattern = metacluster_grp[\"seqlets_to_patterns_result\"][\"patterns\"][pattern_name]\n",
"\n",
" print(\"total seqlets:\",len(pattern[\"seqlets_and_alnmts\"][\"seqlets\"]))\n",
" background = np.array([0.27, 0.23, 0.23, 0.27])\n",
"\n",
" #these are neat to see, but not important yet\n",
" #print(\"Task 0 hypothetical scores:\")\n",
" #viz_sequence.plot_weights(pattern[\"task_hypothetical_contribs\"][\"fwd\"])\n",
" #print(\"Task 0 actual importance scores:\")\n",
" #viz_sequence.plot_weights(pattern[\"task_contrib_scores\"][\"fwd\"])\n",
" print(\"onehot, fwd and rev:\")\n",
" viz_sequence.plot_weights(viz_sequence.ic_scale(np.array(pattern[\"sequence\"][\"fwd\"]),\n",
" background=background)) \n",
" viz_sequence.plot_weights(viz_sequence.ic_scale(np.array(pattern[\"sequence\"][\"rev\"]),\n",
" background=background)) \n",
"\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "HYuaMwEfqtPe",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"##################################\n",
"# Notable variable declarions #\n",
"# Start the Main program. #\n",
"##################################\n",
"\n",
"\n",
"sequenceName = 'GSE31332_hotair_oe_peaks'\n",
"iteration = ''\n",
"prototype = '-small'\n",
"#postfix = \"\"\n",
"numEpochs = 50\n",
"\n",
"\n",
"X_train = []\n",
"y_train = []\n",
"X_test = []\n",
"y_test = []\n",
" \n",
"X_testSequences = [] #sanitized list without classification for testing\n",
"X_testDL = []\n",
"\n",
"# returned from generating the sequences\n",
"longestLength = 0; \n",
"shortestLength = 0;\n",
"shortIdx = 0 \n",
" \n",
"modelHistory = 0 # history object from Keras\n",
"kerasModel = 0\n",
" \n",
"loadedSeqName = \"\"\n",
"saveLoadIteration = \"\"\n",
" \n",
"currentKerasPredictions = 0\n",
" \n",
"scores = [] #Deeplifts raw scores for each sequence on each element of the oneHot\n",
"hyp_contrib_scores = []\n",
" \n",
"deeplift_contribs = 0 #holds the contributions of each layer in a DeepLift model - deepLIFT_model.get_target_contribs_func\n",
"deeplift_multipliers = 0 #holds the multiples (hypotheticals) of each layer in a DeepLift model - deepLIFT_model.get_target_multipliers_func\n",
"\n",
"tfmodisco_results = []\n",
" \n",
"num_refs_per_seq = 10"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "XT_5T3rbqvCG",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 86
},
"outputId": "160a7175-26ba-43ea-99c1-2a27a8695b5a"
},
"cell_type": "code",
"source": [
"#############################################\n",
"# Load all the sequences into the lists #\n",
"############################################\n",
"\n",
"\n",
"\n",
"fileHeader = sequenceName\n",
"saveLoadIteration = iteration\n",
" \n",
"trainingPosFile = fileHeader + '.bed.pos.train.fa' + prototype\n",
"trainingNegFile = fileHeader + '.bed.neg.train.fa' + prototype\n",
"testPosFile = fileHeader + '.bed.pos.test.fa' + prototype\n",
"testNegFile = fileHeader + '.bed.neg.test.fa' + prototype\n",
"\n",
"pos_train_rows = []\n",
"neg_train_rows = []\n",
"pos_test_rows = []\n",
"neg_test_rows = []\n",
"\n",
"tempLength = getBasicSequenceFromFile(trainingPosFile, pos_train_rows)\n",
"longestLength = tempLength[0]\n",
"seqLengths = tempLength[1]\n",
"shortIdx = tempLength[2]\n",
"shortestLength = seqLengths[shortIdx]\n",
"\n",
"\n",
"tempLength = getBasicSequenceFromFile(trainingNegFile, neg_train_rows)\n",
"if(tempLength[0] > longestLength):\n",
" longestLength = tempLength[0]\n",
"tempLength = getBasicSequenceFromFile(testPosFile, pos_test_rows)\n",
"if(tempLength[0] > longestLength):\n",
" longestLength = tempLength[0]\n",
"tempLength = getBasicSequenceFromFile(testNegFile, neg_test_rows)\n",
"if(tempLength[0] > longestLength):\n",
" longestLength = tempLength[0]\n",
"\n",
"pos_train_rows = np.asarray(pos_train_rows)\n",
"neg_train_rows = np.asarray(neg_train_rows)\n",
"pos_test_rows = np.asarray(pos_test_rows)\n",
"neg_test_rows = np.asarray(neg_test_rows)\n",
"\n",
"\n",
"print(\"Longest sequence\", longestLength)\n",
"print(\"Shortest sequence\", shortestLength)\n",
"print(\"Index of shortest sequence\", shortIdx)\n",
"\n",
"#we build the onehot numpy array for each sequence and pass the classVal. Note since the output is going to be one \n",
"#element bigger (for the classVal) we have to pass length+1\n",
"pos_train_onehot_data = np.array([one_hot_encode_along_channel_axis(seq,longestLength+1, 1) for seq in pos_train_rows])\n",
"neg_train_onehot_data = np.array([one_hot_encode_along_channel_axis(seq,longestLength+1, 0) for seq in neg_train_rows])\n",
"combinedTrain = np.concatenate((pos_train_onehot_data, neg_train_onehot_data))\n",
"\n",
"pos_test_onehot_data = np.array([one_hot_encode_along_channel_axis(seq,longestLength+1, 1) for seq in pos_test_rows])\n",
"neg_test_onehot_data = np.array([one_hot_encode_along_channel_axis(seq,longestLength+1, 0) for seq in neg_test_rows])\n",
"combinedTest = np.concatenate((pos_train_onehot_data, neg_train_onehot_data))\n",
"\n",
"\n",
"combinedLen = len(combinedTrain[0])-1\n",
"X_train = combinedTrain[:,0:combinedLen,:]\n",
"y_train = combinedTrain[:,combinedLen:combinedLen+1,:1]\n",
"y_train = y_train.reshape(y_train.shape[0],1)\n",
"\n",
"X_test = combinedTest[:,0:combinedLen,:]\n",
"y_test = combinedTest[:,combinedLen:combinedLen+1,:1]\n",
"y_test = y_test.reshape(y_test.shape[0],1)\n",
"\n",
"\n",
"#dump the memory alloc\n",
"pos_train_rows = []\n",
"neg_train_rows = []\n",
"pos_test_rows = []\n",
"neg_test_rows = []\n",
"pos_train_onehot_data = []\n",
"neg_train_onehot_data = []\n",
"combinedTrain = []\n",
"#pos_test_onehot_data = [] we will be using this one for DeepLift visualization\n",
"neg_test_onehot_data = []\n",
"combinedTest = []\n",
"print(\"Finished loading files\")"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"Longest sequence 1948\n",
"Shortest sequence 1948\n",
"Index of shortest sequence 66\n",
"Finished loading files\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "SZ6cSK-vqw2m",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "3804a79e-5ab7-44ea-fa54-68cf368510c8"
},
"cell_type": "code",
"source": [
"#############################################\n",
"# Build the Keras Model. #\n",
"############################################\n",
"\n",
"#PICK ONLY ONE MODEL\n",
"# The fail/work situation may change depending on environment\n",
"#kerasModel = deeps_Model1(longestLength) # may fail (random) in TF_MoDISco calculations\n",
"#kerasModel = deeps_Model2(longestLength) # should work in TF_MoDISco calculations\n",
"kerasModel = deeps_Model5(longestLength) # should fail in TF_MoDISco calculations\n",
"\n",
"\n",
"modelHistory = kerasModel.fit(\n",
" X_train, \n",
" y_train, \n",
" batch_size = 32, \n",
" epochs = numEpochs,\n",
" verbose = 0)\n",
"print(\"Finished loading model, and fit\")"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"Finished loading model, and fit\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "pgl-u7uEqzQd",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 577
},
"outputId": "be199840-a779-48c2-f592-d28ea138a5a2"
},
"cell_type": "code",
"source": [
"currentKerasPredictions = np.around(kerasModel.predict(X_test))\n",
"print(\"Predictions finished\")\n",
"#print metrics\n",
"print(\"Keras Model Test\")\n",
"print(\"Accuracy: \", metrics.accuracy_score(y_test, currentKerasPredictions))\n",
"print(\"Precision: \",metrics.precision_score(y_test, currentKerasPredictions))\n",
"print(\"Recall: \",metrics.recall_score(y_test, currentKerasPredictions))\n",
"print(\"Confusion Matrix: \\n\",metrics.confusion_matrix(y_test, currentKerasPredictions))\n",
"print(\"Classification Report: \\n\",metrics.classification_report(y_test, currentKerasPredictions))\n",
"score = kerasModel.evaluate(X_test, y_test, verbose = 1)\n",
"print(score)\n",
"plt.plot(modelHistory.history['acc'])\n",
"plt.show()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"Predictions finished\n",
"Keras Model Test\n",
"Accuracy: 0.9612903225806452\n",
"Precision: 0.9295774647887324\n",
"Recall: 0.9850746268656716\n",
"Confusion Matrix: \n",
" [[83 5]\n",
" [ 1 66]]\n",
"Classification Report: \n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.94 0.97 88\n",
" 1 0.93 0.99 0.96 67\n",
"\n",
"avg / total 0.96 0.96 0.96 155\n",
"\n",
"155/155 [==============================] - 0s 689us/step\n",
"[0.13523054593993772, 0.9612903264261061]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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WkJ2dMuH1M0m+55MTzGyC4b/TPD34vwWWAZ3A75VS64BjgENrvVcptQ14GVgz3k3b2nqD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\nPXoCf5v5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f826acb1c88>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "UIib92A_r3qq",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "aba2661e-8ec4-49b6-feb7-17d6bc6e86e8"
},
"cell_type": "code",
"source": [
"################################################################\n",
"# Load the data for DeepLift and TF MoDISco to test against#\n",
"################################################################\n",
"test_rows = []\n",
"tempLength = getBasicSequenceFromFile(testPosFile, test_rows)\n",
"\n",
"#keep longestLength the same to have matching shape between systems\n",
"seqLengths = tempLength[1]\n",
"shortIdx = tempLength[2]\n",
"shortestLength = seqLengths[shortIdx]\n",
"\n",
"X_testSequences = np.asarray(test_rows)\n",
"\n",
"X_testDL = np.array([one_hot_encode_along_channel_axis_core(seq,longestLength) for seq in X_testSequences])\n",
"\n",
"test_rows = []\n",
"print(\"data loaded\")"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"data loaded\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "2xxthzhLsGo8",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 210
},
"outputId": "5f0e4fee-8bd1-45f5-fd39-aef08e38d3d0"
},
"cell_type": "code",
"source": [
"#save the model\n",
"saved_model_file = \"keras_deepModel\" + sequenceName + saveLoadIteration + \".h5\"\n",
"saved_model = kerasModel.save(saved_model_file)\n",
"\n",
"#Load the model via the files we just saved\n",
"deepLIFT_model = kc.convert_model_from_saved_files(\n",
" h5_file=saved_model_file,\n",
" nonlinear_mxts_mode=NonlinearMxtsMode.DeepLIFT_GenomicsDefault)\n",
"\n",
"deeplift_contribs = deepLIFT_model.get_target_contribs_func(\n",
" find_scores_layer_idx=0,\n",
" target_layer_idx=-2)\n",
"deeplift_multipliers = deepLIFT_model.get_target_multipliers_func(\n",
" find_scores_layer_idx=0,\n",
" target_layer_idx=-2)\n",
"\n"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
"nonlinear_mxts_mode is set to: DeepLIFT_GenomicsDefault\n",
"For layer 1 the preceding linear layer is 0 of type Conv1D;\n",
"In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
"For layer 3 the preceding linear layer is 2 of type Conv1D;\n",
"In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
"For layer 7 the preceding linear layer is 6 of type Dense;\n",
"In accordance with nonlinear_mxts_modeDeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to RevealCancel\n",
"Heads-up: I assume sigmoid is the output layer, not an intermediate one; if it's an intermediate layer then please bug me and I will implement the grad func\n",
"For layer 10 the preceding linear layer is 9 of type Dense;\n",
"In accordance with nonlinear_mxts_modeDeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to RevealCancel\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "A3eo1RZRsIL6",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "431c1dd0-6123-49bc-f8d7-8cfb41fb4563"
},
"cell_type": "code",
"source": [
"#################################################\n",
"# Calculate the Scores and Hypothetical Scores #\n",
"# As seen in DeepLift and TF MoDISco Examples #\n",
"#################################################\n",
"hypothetical_contribs_func = get_hypothetical_contribs_func_onehot(deeplift_multipliers)\n",
"\n",
"contribs_many_refs_func = get_shuffle_seq_ref_function(\n",
" #score_computation_function is the original function to compute scores\n",
" score_computation_function=deeplift_contribs,\n",
" #shuffle_func is the function that shuffles the sequence\n",
" #On real genomic data, a dinuc shuffle is advisable due to\n",
" #the strong bias against CG dinucleotides\n",
" shuffle_func=dinuc_shuffle,\n",
" one_hot_func=lambda x: np.array([\n",
" one_hot_encode_along_channel_axis_core(seq, longestLength)\n",
" for seq in x]))\n",
"\n",
"hypothetical_contribs_many_refs_func = get_shuffle_seq_ref_function(\n",
" score_computation_function=hypothetical_contribs_func,\n",
" shuffle_func=dinuc_shuffle,\n",
" one_hot_func=lambda x: np.array([one_hot_encode_along_channel_axis_core(seq, longestLength)\n",
" for seq in x]))\n",
"\n",
"scores = np.sum(contribs_many_refs_func(\n",
" task_idx=0,\n",
" input_data_sequences=X_testSequences,\n",
" num_refs_per_seq=num_refs_per_seq,\n",
" batch_size=32,\n",
" progress_update=None), axis = 2)\n",
"\n",
"\n",
"scores = scores[:,:,None] * X_testDL\n",
"\n",
"hyp_contrib_scores = hypothetical_contribs_many_refs_func(\n",
" task_idx=0,\n",
" input_data_sequences=X_testSequences,\n",
" num_refs_per_seq=num_refs_per_seq,\n",
" batch_size=32,\n",
" progress_update=None,\n",
")\n",
"meaned = np.mean(hyp_contrib_scores, axis = -1)[:,:,None] * np.ones(np.shape(hyp_contrib_scores))\n",
"\n",
"#normailize the data as noted from TF MoDISco's best practice\n",
"hyp_contrib_scores = hyp_contrib_scores - meaned\n",
"\n",
"print(\"Scores calculated\")"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
"Scores calculated\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "bUkCNJg1s5bk",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 332
},
"outputId": "7cbf17a8-81bb-44ff-91ea-b72e622c55b2"
},
"cell_type": "code",
"source": [
"################################################################\n",
"# Visualize one sequence of PWM found by DeepLift. #\n",
"################################################################\n",
"\n",
"#select a sequence\n",
"selectedSeqID = shortIdx #perhaps pick the shortest sequence \n",
"score_for_ID = scores[selectedSeqID]\n",
"originalOnehotSeq = X_testDL[selectedSeqID]\n",
"\n",
"#apply the one-hot to the sequence weights to zero out the elements that weren't there\n",
"#make it so only the corresponding A/C/G/T value is non-zero\n",
"score_for_ID = originalOnehotSeq*score_for_ID\n",
"\n",
"#clip the score to the length of the sequence\n",
"score_for_ID = score_for_ID[:seqLengths[shortIdx]]\n",
"\n",
"#use DeepLift's visualization tech to show possible motifs\n",
"print(\"Show a the shortest sequence with weights applied\")\n",
"viz_sequence.plot_weights(score_for_ID, subticks_frequency=10)\n",
"\n",
"\n",
"\n",
"#combine all scores into one length x 4 array and visualize\n",
"scoresReshapedViz = scores\n",
"scoresReshapedViz = np.sum(scoresReshapedViz, axis=0)\n",
"print(\"Show all weights applied\")\n",
"viz_sequence.plot_weights(scoresReshapedViz, subticks_frequency=100)"
],
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"text": [
"Show a the shortest sequence with weights applied\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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/khF3aDTndsMw0I3kd9XhaS9K7OkY66BltKmEEk9MIBpIE3X8UT+Kku195AmP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\nYceNOdup+HgzPt6nFyqCo7SPfIc151Ry6TzRH3m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jztbfUGV3c706ja0H1rJmzpO8smsF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\n2yOrPhrys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"text/plain": [
"<matplotlib.figure.Figure at 0x7f8263769c50>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Show all weights applied\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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72vLpFxSvNatghWY1rJuDpw7Yx4wKKh9bWfRkewgVdKccvpG5A93YxHc/8waC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/6Ih5hxl8RcED+n0ke0rapdhBnXxcNjYvVEUpEUYMpZOhHjlX2+UpEUHVWdRJyKEGSQznKekYxA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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f82601725c0>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "CeogYx73tdqJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 8464
},
"outputId": "23596c34-ba7a-4fbd-c03b-0d2c60d3c0ff"
},
"cell_type": "code",
"source": [
"###################################################\n",
"# TF MODISCO Calculations\n",
"# we can use the scores / hyp from memory\n",
"# instead of reloading the file\n",
"###################################################\n",
"tfmodisco_results = modisco.tfmodisco_workflow.workflow.TfModiscoWorkflow(\n",
" #Slight modifications from the default settings\n",
" #sliding_window_size=21,\n",
" sliding_window_size=15,\n",
" #flank_size=10,\n",
" flank_size=5,\n",
" target_seqlet_fdr=0.01,\n",
" #note: \"min_seqlets_per_task\" is an approximate minimum\n",
" # number of seqlets to try to obtain per task. The \"approximate\"\n",
" # nature is due to complications involving excluding overlaps.\n",
" # I will refine this in future versions to be an actual minimum.\n",
" min_seqlets_per_task=200,\n",
" #metaclusters smaller than min_metacluster_size are kicked out\n",
" # if you have a small number of regions you are analyzing, you\n",
" # want to keep this number smallish\n",
" min_metacluster_size=20,\n",
" seqlets_to_patterns_factory=\n",
" modisco.tfmodisco_workflow.seqlets_to_patterns.TfModiscoSeqletsToPatternsFactory(\n",
" #trim_to_window_size=30,\n",
" trim_to_window_size=15,\n",
" #initial_flank_to_add=10,\n",
" initial_flank_to_add=5,\n",
" #kmer_len=7, num_gaps=3,\n",
" kmer_len=5, num_gaps=1,\n",
" #num_mismatches=2,\n",
" num_mismatches=0,\n",
" #If you have a small number of regions you are analyzing,\n",
" # you want to keep this number smallish\n",
" final_min_cluster_size=10)\n",
" )(\n",
" task_names=[\"task\"],\n",
" contrib_scores={\"task\": [x[:seqLengths[idx]] for (x,idx) in zip(scores,range(len(scores)))]},\n",
" hypothetical_contribs={\"task\": [x[:seqLengths[idx]] for (x,idx) in zip(hyp_contrib_scores,range(len(scores)))]},\n",
" one_hot=[x[:seqLengths[idx]] for (x,idx) in zip(X_testDL,range(len(scores)))])\n",
"\n"
],
"execution_count": 53,
"outputs": [
{
"output_type": "stream",
"text": [
"On task task\n",
"Computing windowed sums\n",
"Computing threshold\n",
"peak(mu)= 0.03328092740476131\n",
"Manually adjusting thresholds to get desired num seqlets\n",
"Mu: 3.328093e-02 +/- 1.832530e-06\n",
"Lablace_b: 0.3328654766875715 and 0.253686695345088\n",
"Thresholds: -0.8312151844650507 and 0.8977770392745733\n",
"#fdrs pass: 0 and 0\n",
"CDFs: 0.9255132181555826 and 0.9668837565349621\n",
"Est. FDRs: 1.0 and 1.0\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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mJDvKTaffKOvYlEk82MCj7Wt5b9ezXH/kKW69M4preG1CWfisOkmLXlSk3kQ/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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f824ec1a198>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Got 313 coords\n",
"After resolving overlaps, got 313 seqlets\n",
"Across all tasks, the weakest laplace threshold used was: 0.9255131181555827\n",
"313 identified in total\n",
"Reducing weak_threshold_for_counting_sign to match laplace_threshold_cdf, from 0.99 to 0.9255131181555827\n",
"2 activity patterns with support >= 20 out of 3 possible patterns\n",
"Metacluster sizes: [175, 138]\n",
"Idx to activities: {0: '-1', 1: '1'}\n",
"On metacluster 1\n",
"Metacluster size 138\n",
"Relevant tasks: ('task',)\n",
"Relevant signs: (1,)\n",
"TfModiscoSeqletsToPatternsFactory: seed=1234\n",
"(Round 1) num seqlets: 138\n",
"(Round 1) Computing coarse affmat\n",
"Beginning embedding computation\n",
"Computing embeddings\n",
"Finished embedding computation in 0.14 s\n",
"Starting affinity matrix computations\n",
"Normalization computed in 0.01 s\n",
"Cosine similarity mat computed in 0.01 s\n",
"Normalization computed in 0.0 s\n",
"Cosine similarity mat computed in 0.01 s\n",
"Finished affinity matrix computations in 0.03 s\n",
"(Round 1) Compute nearest neighbors from coarse affmat\n",
"Computed nearest neighbors in 0.0 s\n",
"(Round 1) Computing affinity matrix on nearest neighbors\n",
"Launching nearest neighbors affmat calculation job\n",
"Job completed in: 7.16 s\n",
"Launching nearest neighbors affmat calculation job\n",
"Job completed in: 0.86 s\n",
"(Round 1) Computed affinity matrix on nearest neighbors in 8.12 s\n",
"Filtered down to 137 of 138\n",
"(Round 1) Retained 137 rows out of 138 after filtering\n",
"(Round 1) Computing density adapted affmat\n",
"[t-SNE] Computing 31 nearest neighbors...\n",
"[t-SNE] Indexed 137 samples in 0.000s...\n",
"[t-SNE] Computed neighbors for 137 samples in 0.001s...\n",
"[t-SNE] Computed conditional probabilities for sample 137 / 137\n",
"[t-SNE] Mean sigma: 0.239855\n",
"(Round 1) Computing clustering\n",
"Beginning preprocessing + Louvain\n",
"Wrote graph to binary file in 0.014855623245239258 seconds\n",
"Running Louvain modularity optimization\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Louvain completed 200 runs in 2.8687610626220703 seconds\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=4)]: Done 200 out of 200 | elapsed: 2.8s finished\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Wrote graph to binary file in 0.01708054542541504 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.567894\n",
"Louvain completed 51 runs in 1.943122386932373 seconds\n",
"Preproc + Louvain took 4.8627355098724365 s\n",
"Got 5 clusters after round 1\n",
"Counts:\n",
"{1: 52, 3: 12, 0: 53, 4: 3, 2: 17}\n",
"(Round 1) Aggregating seqlets in each cluster\n",
"Aggregating for cluster 0 with 53 seqlets\n",
"Trimmed 6 out of 53\n",
"Aggregating for cluster 1 with 52 seqlets\n",
"Trimmed 5 out of 52\n",
"Aggregating for cluster 2 with 17 seqlets\n",
"Trimmed 4 out of 17\n",
"Skipped 2 seqlets\n",
"Aggregating for cluster 3 with 12 seqlets\n",
"Trimmed 3 out of 12\n",
"Aggregating for cluster 4 with 3 seqlets\n",
"Trimmed 0 out of 3\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/modisco/core.py:972: RuntimeWarning: invalid value encountered in true_divide\n",
" /self.per_position_counts[:,None]),\n",
"/usr/local/lib/python3.6/dist-packages/modisco/core.py:974: RuntimeWarning: invalid value encountered in true_divide\n",
" /self.per_position_counts[::-1,None]),\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"(Round 2) num seqlets: 117\n",
"(Round 2) Computing coarse affmat\n",
"Beginning embedding computation\n",
"Computing embeddings\n",
"Finished embedding computation in 0.05 s\n",
"Starting affinity matrix computations\n",
"Normalization computed in 0.0 s\n",
"Cosine similarity mat computed in 0.01 s\n",
"Normalization computed in 0.0 s\n",
"Cosine similarity mat computed in 0.01 s\n",
"Finished affinity matrix computations in 0.02 s\n",
"(Round 2) Compute nearest neighbors from coarse affmat\n",
"Computed nearest neighbors in 0.0 s\n",
"(Round 2) Computing affinity matrix on nearest neighbors\n",
"Launching nearest neighbors affmat calculation job\n",
"Job completed in: 0.66 s\n",
"Launching nearest neighbors affmat calculation job\n",
"Job completed in: 0.64 s\n",
"(Round 2) Computed affinity matrix on nearest neighbors in 1.37 s\n",
"Not applying filtering for rounds above first round\n",
"(Round 2) Computing density adapted affmat\n",
"[t-SNE] Computing 31 nearest neighbors...\n",
"[t-SNE] Indexed 117 samples in 0.000s...\n",
"[t-SNE] Computed neighbors for 117 samples in 0.002s...\n",
"[t-SNE] Computed conditional probabilities for sample 117 / 117\n",
"[t-SNE] Mean sigma: 0.239208\n",
"(Round 2) Computing clustering\n",
"Beginning preprocessing + Louvain\n",
"Wrote graph to binary file in 0.012565135955810547 seconds\n",
"Running Louvain modularity optimization\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
"[Parallel(n_jobs=4)]: Done 144 tasks | elapsed: 2.1s\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Louvain completed 200 runs in 2.874418020248413 seconds\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=4)]: Done 200 out of 200 | elapsed: 2.8s finished\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Wrote graph to binary file in 0.012516498565673828 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.530908\n",
"Louvain completed 51 runs in 1.9418730735778809 seconds\n",
"Preproc + Louvain took 4.862575531005859 s\n",
"Got 5 clusters after round 2\n",
"Counts:\n",
"{1: 40, 4: 7, 3: 9, 0: 50, 2: 11}\n",
"(Round 2) Aggregating seqlets in each cluster\n",
"Aggregating for cluster 0 with 50 seqlets\n",
"Trimmed 7 out of 50\n",
"Aggregating for cluster 1 with 40 seqlets\n",
"Trimmed 2 out of 40\n",
"Aggregating for cluster 2 with 11 seqlets\n",
"Trimmed 0 out of 11\n",
"Aggregating for cluster 3 with 9 seqlets\n",
"Trimmed 0 out of 9\n",
"Aggregating for cluster 4 with 7 seqlets\n",
"Trimmed 0 out of 7\n",
"Got 5 clusters\n",
"Splitting into subclusters...\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/modisco/core.py:972: RuntimeWarning: invalid value encountered in true_divide\n",
" /self.per_position_counts[:,None]),\n",
"/usr/local/lib/python3.6/dist-packages/modisco/core.py:974: RuntimeWarning: invalid value encountered in true_divide\n",
" /self.per_position_counts[::-1,None]),\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.009879589080810547 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00402855\n",
"After 2 runs, maximum modularity is Q = 0.00403855\n",
"After 3 runs, maximum modularity is Q = 0.00441552\n",
"Louvain completed 23 runs in 0.9936752319335938 seconds\n",
"Similarity is 0.7485415; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.004244327545166016 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00228522\n",
"After 2 runs, maximum modularity is Q = 0.00329258\n",
"After 3 runs, maximum modularity is Q = 0.00330384\n",
"After 4 runs, maximum modularity is Q = 0.00404889\n",
"Louvain completed 24 runs in 1.0415403842926025 seconds\n",
"Similarity is 0.70276195; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.002627134323120117 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00202393\n",
"After 11 runs, maximum modularity is Q = 0.00202394\n",
"Louvain completed 31 runs in 1.227442741394043 seconds\n",
"Similarity is 0.60023695; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0016698837280273438 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 7.81878e-05\n",
"After 3 runs, maximum modularity is Q = 0.00110646\n",
"After 15 runs, maximum modularity is Q = 0.00110647\n",
"Louvain completed 35 runs in 1.418041706085205 seconds\n",
"Similarity is 0.5877893; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0034804344177246094 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00255442\n",
"After 2 runs, maximum modularity is Q = 0.00318863\n",
"After 5 runs, maximum modularity is Q = 0.00384723\n",
"Louvain completed 25 runs in 1.051619052886963 seconds\n",
"Similarity is 0.6935103; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0018715858459472656 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.0019087\n",
"After 3 runs, maximum modularity is Q = 0.00190871\n",
"Louvain completed 23 runs in 0.9472370147705078 seconds\n",
"Similarity is 0.6134481; is_dissimilar is True\n",
"Got 4 subclusters\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.007178068161010742 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00410345\n",
"After 2 runs, maximum modularity is Q = 0.00432972\n",
"After 4 runs, maximum modularity is Q = 0.005519\n",
"Louvain completed 24 runs in 1.024519920349121 seconds\n",
"Similarity is 0.6893958; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0029268264770507812 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00459939\n",
"After 2 runs, maximum modularity is Q = 0.00492688\n",
"Louvain completed 22 runs in 0.8892815113067627 seconds\n",
"Similarity is 0.6756177; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0017116069793701172 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00372869\n",
"After 4 runs, maximum modularity is Q = 0.0037287\n",
"Louvain completed 24 runs in 0.9705264568328857 seconds\n",
"Similarity is 0.60258806; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0024323463439941406 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00218464\n",
"After 2 runs, maximum modularity is Q = 0.00357854\n",
"After 19 runs, maximum modularity is Q = 0.00357855\n",
"Louvain completed 39 runs in 1.5705034732818604 seconds\n",
"Similarity is 0.6125314; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0016317367553710938 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.000981924\n",
"Louvain completed 21 runs in 0.8404278755187988 seconds\n",
"Similarity is 0.42170915; is_dissimilar is True\n",
"Got 4 subclusters\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.001626729965209961 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.000533269\n",
"After 6 runs, maximum modularity is Q = 0.000533272\n",
"After 10 runs, maximum modularity is Q = 0.000533274\n",
"After 19 runs, maximum modularity is Q = 0.000533277\n",
"Louvain completed 39 runs in 1.5850822925567627 seconds\n",
"Similarity is 0.7074505; is_dissimilar is True\n",
"Got 2 subclusters\n",
"Merging on 12 clusters\n",
"On merging iteration 1\n",
"Computing pattern to seqlet distances\n",
"Computing pattern to pattern distances\n",
"Got 12 patterns after merging\n",
"Performing seqlet reassignment\n",
"Cross contin jaccard time taken: 0.03 s\n",
"Cross contin jaccard time taken: 0.02 s\n",
"Discarded 3 seqlets\n",
"Got 3 patterns after reassignment\n",
"Total time taken is 35.63s\n",
"On metacluster 0\n",
"Metacluster size 175\n",
"Relevant tasks: ('task',)\n",
"Relevant signs: (-1,)\n",
"TfModiscoSeqletsToPatternsFactory: seed=1234\n",
"(Round 1) num seqlets: 175\n",
"(Round 1) Computing coarse affmat\n",
"Beginning embedding computation\n",
"Computing embeddings\n",
"Finished embedding computation in 0.15 s\n",
"Starting affinity matrix computations\n",
"Normalization computed in 0.0 s\n",
"Cosine similarity mat computed in 0.01 s\n",
"Normalization computed in 0.0 s\n",
"Cosine similarity mat computed in 0.01 s\n",
"Finished affinity matrix computations in 0.03 s\n",
"(Round 1) Compute nearest neighbors from coarse affmat\n",
"Computed nearest neighbors in 0.0 s\n",
"(Round 1) Computing affinity matrix on nearest neighbors\n",
"Launching nearest neighbors affmat calculation job\n",
"Job completed in: 1.61 s\n",
"Launching nearest neighbors affmat calculation job\n",
"Job completed in: 1.55 s\n",
"(Round 1) Computed affinity matrix on nearest neighbors in 3.27 s\n",
"Filtered down to 175 of 175\n",
"(Round 1) Retained 175 rows out of 175 after filtering\n",
"(Round 1) Computing density adapted affmat\n",
"[t-SNE] Computing 31 nearest neighbors...\n",
"[t-SNE] Indexed 175 samples in 0.000s...\n",
"[t-SNE] Computed neighbors for 175 samples in 0.002s...\n",
"[t-SNE] Computed conditional probabilities for sample 175 / 175\n",
"[t-SNE] Mean sigma: 0.226198\n",
"(Round 1) Computing clustering\n",
"Beginning preprocessing + Louvain\n",
"Wrote graph to binary file in 0.018762588500976562 seconds\n",
"Running Louvain modularity optimization\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Louvain completed 200 runs in 3.045844316482544 seconds\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=4)]: Done 200 out of 200 | elapsed: 3.0s finished\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Wrote graph to binary file in 0.025402545928955078 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.576398\n",
"Louvain completed 51 runs in 1.924354076385498 seconds\n",
"Preproc + Louvain took 5.0349695682525635 s\n",
"Got 6 clusters after round 1\n",
"Counts:\n",
"{0: 70, 1: 41, 2: 37, 5: 4, 3: 18, 4: 5}\n",
"(Round 1) Aggregating seqlets in each cluster\n",
"Aggregating for cluster 0 with 70 seqlets\n",
"Trimmed 8 out of 70\n",
"Skipped 1 seqlets\n",
"Aggregating for cluster 1 with 41 seqlets\n",
"Trimmed 0 out of 41\n",
"Aggregating for cluster 2 with 37 seqlets\n",
"Trimmed 2 out of 37\n",
"Aggregating for cluster 3 with 18 seqlets\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/modisco/core.py:972: RuntimeWarning: invalid value encountered in true_divide\n",
" /self.per_position_counts[:,None]),\n",
"/usr/local/lib/python3.6/dist-packages/modisco/core.py:974: RuntimeWarning: invalid value encountered in true_divide\n",
" /self.per_position_counts[::-1,None]),\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Trimmed 2 out of 18\n",
"Aggregating for cluster 4 with 5 seqlets\n",
"Trimmed 0 out of 5\n",
"Aggregating for cluster 5 with 4 seqlets\n",
"Trimmed 0 out of 4\n",
"(Round 2) num seqlets: 162\n",
"(Round 2) Computing coarse affmat\n",
"Beginning embedding computation\n",
"Computing embeddings\n",
"Finished embedding computation in 0.05 s\n",
"Starting affinity matrix computations\n",
"Normalization computed in 0.0 s\n",
"Cosine similarity mat computed in 0.03 s\n",
"Normalization computed in 0.0 s\n",
"Cosine similarity mat computed in 0.01 s\n",
"Finished affinity matrix computations in 0.05 s\n",
"(Round 2) Compute nearest neighbors from coarse affmat\n",
"Computed nearest neighbors in 0.0 s\n",
"(Round 2) Computing affinity matrix on nearest neighbors\n",
"Launching nearest neighbors affmat calculation job\n",
"Job completed in: 1.18 s\n",
"Launching nearest neighbors affmat calculation job\n",
"Job completed in: 1.16 s\n",
"(Round 2) Computed affinity matrix on nearest neighbors in 2.44 s\n",
"Not applying filtering for rounds above first round\n",
"(Round 2) Computing density adapted affmat\n",
"[t-SNE] Computing 31 nearest neighbors...\n",
"[t-SNE] Indexed 162 samples in 0.000s...\n",
"[t-SNE] Computed neighbors for 162 samples in 0.003s...\n",
"[t-SNE] Computed conditional probabilities for sample 162 / 162\n",
"[t-SNE] Mean sigma: 0.226764\n",
"(Round 2) Computing clustering\n",
"Beginning preprocessing + Louvain\n",
"Wrote graph to binary file in 0.016628026962280273 seconds\n",
"Running Louvain modularity optimization\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Louvain completed 200 runs in 3.058574914932251 seconds\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=4)]: Done 200 out of 200 | elapsed: 3.0s finished\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Wrote graph to binary file in 0.025738000869750977 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.585031\n",
"After 18 runs, maximum modularity is Q = 0.593864\n",
"Louvain completed 68 runs in 2.608767032623291 seconds\n",
"Preproc + Louvain took 5.7332763671875 s\n",
"Got 4 clusters after round 2\n",
"Counts:\n",
"{0: 58, 3: 3, 2: 47, 1: 54}\n",
"(Round 2) Aggregating seqlets in each cluster\n",
"Aggregating for cluster 0 with 58 seqlets\n",
"Trimmed 13 out of 58\n",
"Aggregating for cluster 1 with 54 seqlets\n",
"Trimmed 13 out of 54\n",
"Aggregating for cluster 2 with 47 seqlets\n",
"Trimmed 2 out of 47\n",
"Aggregating for cluster 3 with 3 seqlets\n",
"Trimmed 0 out of 3\n",
"Dropping cluster 3 with 2 seqlets due to sign disagreement\n",
"Got 3 clusters\n",
"Splitting into subclusters...\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.007310152053833008 seconds\n",
"Running Louvain modularity optimization\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/modisco/core.py:972: RuntimeWarning: invalid value encountered in true_divide\n",
" /self.per_position_counts[:,None]),\n",
"/usr/local/lib/python3.6/dist-packages/modisco/core.py:974: RuntimeWarning: invalid value encountered in true_divide\n",
" /self.per_position_counts[::-1,None]),\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"After 1 runs, maximum modularity is Q = 0.00380211\n",
"Louvain completed 21 runs in 0.8313353061676025 seconds\n",
"Similarity is 0.8138526; is_dissimilar is False\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.005910396575927734 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00526905\n",
"After 3 runs, maximum modularity is Q = 0.00580405\n",
"After 13 runs, maximum modularity is Q = 0.00580406\n",
"Louvain completed 33 runs in 1.34907865524292 seconds\n",
"Similarity is 0.78212523; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.00432276725769043 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.0031048\n",
"After 4 runs, maximum modularity is Q = 0.00310752\n",
"After 9 runs, maximum modularity is Q = 0.00337507\n",
"After 11 runs, maximum modularity is Q = 0.00337508\n",
"Louvain completed 31 runs in 1.301016092300415 seconds\n",
"Similarity is 0.8195651; is_dissimilar is False\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.003238201141357422 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00208219\n",
"After 2 runs, maximum modularity is Q = 0.00353483\n",
"Louvain completed 22 runs in 0.9048736095428467 seconds\n",
"Similarity is 0.78727955; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0013413429260253906 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = -0.00143332\n",
"After 2 runs, maximum modularity is Q = -0.00143331\n",
"After 5 runs, maximum modularity is Q = -0.00114683\n",
"After 10 runs, maximum modularity is Q = 9.51797e-09\n",
"Louvain completed 30 runs in 1.292051076889038 seconds\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.002739429473876953 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00394256\n",
"Louvain completed 21 runs in 0.8320424556732178 seconds\n",
"Similarity is 0.5956803; is_dissimilar is True\n",
"Got 4 subclusters\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.009120702743530273 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00556807\n",
"Louvain completed 21 runs in 0.8212447166442871 seconds\n",
"Similarity is 0.7432163; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.005053281784057617 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.0058279\n",
"Louvain completed 21 runs in 0.8411784172058105 seconds\n",
"Similarity is 0.7249847; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.002474546432495117 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00460463\n",
"Louvain completed 21 runs in 0.8592042922973633 seconds\n",
"Similarity is 0.665047; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0019888877868652344 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.0020363\n",
"After 3 runs, maximum modularity is Q = 0.00344442\n",
"After 4 runs, maximum modularity is Q = 0.00344443\n",
"Louvain completed 24 runs in 1.0171363353729248 seconds\n",
"Similarity is 0.6047614; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.004622697830200195 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00470655\n",
"After 3 runs, maximum modularity is Q = 0.00470656\n",
"Louvain completed 23 runs in 0.9451451301574707 seconds\n",
"Similarity is 0.7210131; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0016984939575195312 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.000346453\n",
"After 2 runs, maximum modularity is Q = 0.000346456\n",
"Louvain completed 22 runs in 0.905515193939209 seconds\n",
"Similarity is 0.66819364; is_dissimilar is True\n",
"Inspecting for spurious merging\n",
"Wrote graph to binary file in 0.0015904903411865234 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.00397914\n",
"After 15 runs, maximum modularity is Q = 0.00397915\n",
"Louvain completed 35 runs in 1.400057077407837 seconds\n",
"Similarity is 0.5516023; is_dissimilar is True\n",
"Got 4 subclusters\n",
"Merging on 9 clusters\n",
"On merging iteration 1\n",
"Computing pattern to seqlet distances\n",
"Computing pattern to pattern distances\n",
"Got 9 patterns after merging\n",
"Performing seqlet reassignment\n",
"Cross contin jaccard time taken: 0.02 s\n",
"Cross contin jaccard time taken: 0.01 s\n",
"Got 5 patterns after reassignment\n",
"Total time taken is 32.71s\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "djl6u8_-uy2O",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 6272
},
"outputId": "6b799503-fe5c-4253-cbb9-a664de5dfbc7"
},
"cell_type": "code",
"source": [
"from matplotlib import pyplot as plt\n",
"from modisco.visualization import viz_sequence\n",
"\n",
"for metacluster_idx in tfmodisco_results.metacluster_idx_to_submetacluster_results.keys():\n",
" print(\"On metacluster\",metacluster_idx)\n",
" print(\"Activity pattern\",tfmodisco_results.metacluster_idx_to_submetacluster_results[metacluster_idx].activity_pattern)\n",
" patterns = tfmodisco_results.metacluster_idx_to_submetacluster_results[metacluster_idx].seqlets_to_patterns_result.patterns\n",
" if (len(patterns)==0):\n",
" print(\"No patterns found in metacluster\")\n",
" for pattern_idx,pattern in enumerate(patterns):\n",
" print(\"Metacluster\",metacluster_idx,\"pattern\",pattern_idx)\n",
" print(\"Num seqlets\",len(pattern.seqlets))\n",
" print(\"Seqlet positional distribution\")\n",
" positional_distribution = np.array([0.5*(x.coor.start + x.coor.end) for x in pattern.seqlets])\n",
" plt.hist(positional_distribution,bins=20)\n",
" plt.xlim(0,longestLength)\n",
" plt.show()\n",
" \n",
" print(\"Contrib scores - fwd\")\n",
" viz_sequence.plot_weights(pattern[\"task_contrib_scores\"].fwd)\n",
" print(\"Hyp contrib scores - fwd\")\n",
" viz_sequence.plot_weights(pattern[\"task_hypothetical_contribs\"].fwd)\n",
" print(\"Sequence - fwd\")\n",
" viz_sequence.plot_weights(viz_sequence.ic_scale(pattern[\"sequence\"].fwd, background=np.array([0.25, 0.25, 0.25, 0.25])))"
],
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"text": [
"On metacluster 1\n",
"Activity pattern [1]\n",
"Metacluster 1 pattern 0\n",
"Num seqlets 54\n",
"Seqlet positional distribution\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f824f77ddd8>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Contrib scores - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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HgCW5nhTMR8encSMthBRVCmYKc3wcW9LdrSexmTeJzbxJD/deGMtNuJYcxMUE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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f824f24db70>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Hyp contrib scores - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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EJwfT06w3WhItdd/vd93D6gfLyVCoDOByuuUY2eBzepj1pkP1j995mJepi3+S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\naOlIdRjb9Ed+vjtFFeH37C91UcCCsuH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xN9+dWUlU+cb0UkEi8/bP1WX52R12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rXpD5AsdJjM1dZr+3mvAeuuB/oGK+8heTz9gIYfZi62XvyfMX1snyKFGhuDJXseD1BTY0F5uQVV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\nTrn342V3qHY5AYTCUF3GMf0CGHtI/9pJogWcn7k6nYD303UOn7YtSFzOf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bkAAAQHSURB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\n/wP+B7uNfT13qwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f824ee9ee48>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Sequence - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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sx4mFy9IBzgR+LBZLCDAQeL2K6yiEEE6zIXo9q06sBKBPw76oisr8yL9YfXIV66LX0q9R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\nF0Y6hp2X6gh+PU8wHKCkNIzu11E1FVUBvzKIbdvYlo1l2Vh5EzvXSz6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02h9nMO6Xbq3A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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f824cafed30>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Metacluster 1 pattern 1\n",
"Num seqlets 26\n",
"Seqlet positional distribution\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f824f0c1dd8>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Contrib scores - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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LXbtSqLbZeD6sXcuUSpuNItL+/bwPzpgR+HmeCNasMeC118z4/XeDLhrIYGAE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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f824f0c1ef0>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Hyp contrib scores - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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p04LbGHL7dqLBg/n/l14SuZs0Ifrrr8B53fHnn/x35EiRv107on37gucuKOC/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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8243d26278>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Sequence - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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EEKcSCf4IIYTwiwsvhDlzTCoOtNxudZD03ntgtZ68efQakMnXXc7gb+P+xlfn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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f824edda438>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Metacluster 1 pattern 2\n",
"Num seqlets 25\n",
"Seqlet positional distribution\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f824f11d1d0>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Contrib scores - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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VigPGI9NgPDwDuuo/A36bZK+CvnwV9OWrYCxfiZqRa7F9u/o1iYms8hVp/vUvhvZ5otUC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AoMr5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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f824ece3b70>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Hyp contrib scores - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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uP/6gUa91Sokzf8XAgTQ6qlThhG2zsRxHj1IEmzy5ZLJ35U9KoqOmNOYtFp6U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gC0R9wsJlJ6M5+3VwRtqn8MXLsUmABYyT3xLPemckidT4xWBmPGcO5LTycdqvJKWeXaAaC9hEla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\nIPZeMQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8243ede9b0>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Sequence - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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dqqHxiSeqFfHqsm8fLF6sGiCnprbxKndtzTIxijbgyF3EyLKd/PWsHehluzF8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"text/plain": [
"<matplotlib.figure.Figure at 0x7f824ee0f2b0>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"On metacluster 0\n",
"Activity pattern [-1]\n",
"Metacluster 0 pattern 0\n",
"Num seqlets 45\n",
"Seqlet positional distribution\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f82444db898>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Contrib scores - fwd\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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MLhfcf7+oLIsGoLp3Oq2ItIlrrkmsXzZW0d4O11+fxLvvWq2bVVVI1vPOg2OO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\nk7X
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