diff --git a/examples/simulated_TAL_GATA_deeplearning/InteractiveViz_TF_MoDISco_TAL_GATA.ipynb b/examples/simulated_TAL_GATA_deeplearning/InteractiveViz_TF_MoDISco_TAL_GATA.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "oPV0Wsfg9OBZ"
+ },
+ "source": [
+ "# TF-MoDISco on the TAL GATA simulation\n",
+ "\n",
+ "### Note: we are still refining the multi-task version of TF-MoDISco. If you encounter difficulties running TF-MoDISco with multiple tasks, our recommendation is to run it on one task at a time.\n",
+ "\n",
+ "This notebook demonstrates running TF-MoDISco on importance scores obtained from the TAL-GATA simulation used in the DeepLIFT paper. See Generate Importance Scores.ipynb for a notebook demonstrating how to produce the scores. There are 3 tasks. Task 0 is positive when both TAL and GATA motifs are present in the sequence. Task 1 is positive when GATA motifs are present in the sequence. Task 2 is positive when TAL motifs are present in the sequence."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 33
+ },
+ "colab_type": "code",
+ "id": "-9R8H-A0ps_X",
+ "outputId": "c2c9e3d5-87dd-4361-882c-5afe32c2661c"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "UsageError: Line magic function `%tensorflow_version` not found.\n"
+ ]
+ }
+ ],
+ "source": [
+ "#this is needed when running in google colab to specify that version 1.x of tensorflow must\n",
+ "# be used; it just throws an error if run in a regular jupyter notebook.\n",
+ "%tensorflow_version 1.x "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 492
+ },
+ "colab_type": "code",
+ "id": "CLiK1j6A8YrA",
+ "outputId": "ba486e3c-0579-49ce-8524-01e8622c0369"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install modisco"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "en15RxNL8YFE"
+ },
+ "outputs": [],
+ "source": [
+ "from __future__ import print_function, division\n",
+ "#%matplotlib notebook\n",
+ "%matplotlib inline\n",
+ "\n",
+ "try:\n",
+ " reload # Python 2.7\n",
+ "except NameError:\n",
+ " try:\n",
+ " from importlib import reload # Python 3.4+\n",
+ " except ImportError:\n",
+ " from imp import reload # Python 3.0 - 3.3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 118
+ },
+ "colab_type": "code",
+ "id": "uVOSJpXV8aIG",
+ "outputId": "719b7b49-a273-40d9-9710-3f0f93e59ae5"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TF-MoDISco is using the TensorFlow backend.\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+ " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
+ "/Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.kde module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
+ " warnings.warn(message, FutureWarning)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import modisco\n",
+ "import sys\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "ROG0LVF_9ZZs"
+ },
+ "source": [
+ "## Grab the input data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 423
+ },
+ "colab_type": "code",
+ "id": "bZ8jaBDZ8fmm",
+ "outputId": "fd8f9d0a-0954-46bb-d7e6-c9fe2c197174"
+ },
+ "outputs": [],
+ "source": [
+ "#grab scores for tfmodisco\n",
+ "#!/usr/bin/env bash\n",
+ "![[ -f scores.h5 ]] || curl -o scores.h5 https://raw.githubusercontent.com/AvantiShri/model_storage/23d8f3ffc89af210f6f0bf7e65585eff259ba672/modisco/scores.h5\n",
+ "![[ -f sequences.simdata.gz ]] || wget https://raw.githubusercontent.com/AvantiShri/model_storage/db919b12f750e5844402153233249bb3d24e9e9a/deeplift/genomics/sequences.simdata.gz\n",
+ "![[ -f test.txt.gz ]] || wget https://raw.githubusercontent.com/AvantiShri/model_storage/9aadb769735c60eb90f7d3d896632ac749a1bdd2/deeplift/genomics/test.txt.gz"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "_ShCbHRM92_y"
+ },
+ "source": [
+ "## Functions for one-hot encoding sequences¶"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "KawKTu5P8-c6"
+ },
+ "outputs": [],
+ "source": [
+ "#Functions for one-hot encoding sequences\n",
+ "import gzip\n",
+ "\n",
+ "def one_hot_encode_along_channel_axis(sequence):\n",
+ " to_return = np.zeros((len(sequence),4), dtype=np.int8)\n",
+ " seq_to_one_hot_fill_in_array(zeros_array=to_return,\n",
+ " sequence=sequence, one_hot_axis=1)\n",
+ " return to_return\n",
+ "\n",
+ "def seq_to_one_hot_fill_in_array(zeros_array, sequence, one_hot_axis):\n",
+ " assert one_hot_axis==0 or one_hot_axis==1\n",
+ " if (one_hot_axis==0):\n",
+ " assert zeros_array.shape[1] == len(sequence)\n",
+ " elif (one_hot_axis==1): \n",
+ " assert zeros_array.shape[0] == len(sequence)\n",
+ " #will mutate zeros_array\n",
+ " for (i,char) in enumerate(sequence):\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",
+ "#read in the data in the testing set\n",
+ "test_ids_fh = gzip.open(\"test.txt.gz\",\"rb\")\n",
+ "ids_to_load = set([x.rstrip() for x in test_ids_fh])\n",
+ "\n",
+ "fasta_sequences = []\n",
+ "for i,a_line in enumerate(gzip.open(\"sequences.simdata.gz\",\"rb\")):\n",
+ " if (i==0):\n",
+ " next\n",
+ " a_line = a_line.rstrip()\n",
+ " seq_id,seq_fasta,embeddings,task1,task2,task3 = a_line.split(b\"\\t\")\n",
+ " if seq_id in ids_to_load:\n",
+ " fasta_sequences.append(seq_fasta.decode(\"utf-8\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "m1xkAlvW97vL"
+ },
+ "source": [
+ "## Prepare the data for input into TF-MoDISCo\n",
+ "\n",
+ "You need a numpy array of importance scores and hypothetical importance scores for every task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "xahZGqrA9Jpq"
+ },
+ "outputs": [],
+ "source": [
+ "import h5py\n",
+ "from collections import OrderedDict\n",
+ "\n",
+ "task_to_scores = OrderedDict()\n",
+ "task_to_hyp_scores = OrderedDict()\n",
+ "\n",
+ "f = h5py.File(\"scores.h5\",\"r\")\n",
+ "tasks = f[\"contrib_scores\"].keys()\n",
+ "n = 400 #since this is just a test run, for speed I am limiting to 100 sequences\n",
+ "for task in tasks:\n",
+ " #Note that the sequences can be of variable lengths;\n",
+ " #in this example they all have the same length (200bp) but that is\n",
+ " #not necessary.\n",
+ " task_to_scores[task] = [np.array(x) for x in f['contrib_scores'][task][:n]]\n",
+ " task_to_hyp_scores[task] = [np.array(x) for x in f['hyp_contrib_scores'][task][:n]]\n",
+ "\n",
+ "onehot_data = [one_hot_encode_along_channel_axis(seq) for seq in fasta_sequences][:n]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "hQEQgz1w-QhL"
+ },
+ "source": [
+ "Double check by plotting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 440
+ },
+ "colab_type": "code",
+ "id": "Ky6nlCFs-NcP",
+ "outputId": "218cb336-dfc5-4c03-9102-c0368c150946"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import modisco.visualization\n",
+ "from modisco.visualization import viz_sequence\n",
+ "\n",
+ "viz_sequence.plot_weights(task_to_scores['task0'][0], subticks_frequency=20)\n",
+ "viz_sequence.plot_weights(task_to_hyp_scores['task0'][0], subticks_frequency=20)\n",
+ "viz_sequence.plot_weights(onehot_data[0], subticks_frequency=20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "uuvSgm62-Whl"
+ },
+ "source": [
+ "Run TF-MoDISco\n",
+ "TF-MoDISco first identifies seqlets, then splits the seqlets into \"metaclusters\" according to their pattern of activity across all the tasks, and then performs clustering within each task. Since there are 3 tasks, there are 27 possible metaclusters (consisting of a +1, -1 or 0 for each task). Consistent with the simulation, the [+1, +1, 0], [+1, 0, +1], [0, 0, +1] and [0, +1, 0] metaclusters turn up motifs.\n",
+ "\n",
+ "To demonstrate customization, the code below has slight modifications from default settings in the following ways:\n",
+ "\n",
+ "- Because the TAL and GATA motifs are relatively short compared to something like CTCF, it uses a sliding window size of 15 (rather than the default of 21) and flanks of 5 (rather than the default of 10). The sliding window size and flanks should be adjusted according to the expected length of the core motif and its flanks. If the window size or flank sizes are too long, you risk picking up more noise.\n",
+ "- During the seqlet clustering, motifs are trimmed to the central trim_to_window_size bp with the highest importance. trim_to_window_size is set to 10 rather than the default of 30. After the trimming is done, the seqlet is expanded on either side by initial_flank_to_add. This is set to 3 rather than the default of 10.\n",
+ "- The final_min_cluster_size is set to 60 rather than the default of 30. This is used to filter out small clusters with relatively weak support (in this case, fewer than 60 seqlets).\n",
+ "- It uses kmers of length 5 with 1 gap and no mismatches to compute the \"quick and dirty\" affinity matrix across all seqlets. The \"quick and dirty\" affinity matrix is used both for noise filtering and as a first pass to speed up computation of the continuous jaccard affinity matrix (the latter affinities are only computed between seqlets deemed to be close together by the \"quick and dirty\" method). I made the kmer length smaller to keep memory usage down when testing on my macbook pro. The default is to use kmers of length 8 with 3 gaps and 2 mismatches, which tends to run out of memory on many systems (I would change the default but want to avoid breaking reproducibility for older users)\n",
+ "- target_seqlet_fdr controls the noisiness of the seqlets. For a particular task, \"significant\" seqlets are identified by first smoothing importance scores with a window of size sliding_window_size and then fitting a laplace distribution to the left and right tails. This laplace distribution is assumed to represent the null distribution of window importance scores (note: as an alternative, it's possible to supply an empirical null distribution; see examples/H1ESC_Nanog_gkmsvm/TF MoDISco Nanog.ipynb for an example). A threshold is then identified such that the false discovery rate (computed as the ratio of the expected fraction of windows with a certain score in the null distribution relative to the observed fraction of windows with that score) is less that target_seqlet_fdr. Note: if the number of sliding windows that pass the FDR threshold is smaller than min_passing_windows_frac (default value 0.03 at the time of writing) or larger than max_passing_windows_frac (default value of 0.2 at the time of writing), the threshold will be adjusted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "colab_type": "code",
+ "id": "--8gp-i2-TOm",
+ "outputId": "18c39c16-c46e-47ad-eb88-afa41c43b2fd",
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MEMORY 0.320466944\n",
+ "On task task0\n",
+ "Computing windowed sums on original\n",
+ "Generating null dist\n",
+ "peak(mu)= -0.025378776089753956\n",
+ "Computing threshold\n",
+ "Thresholds from null dist were -1.507358968257904 and 1.5035333633422852\n",
+ "Final raw thresholds are -1.507358968257904 and 1.5035333633422852\n",
+ "Final transformed thresholds are -0.8831854838709677 and 0.8829569892473118\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 620 coords\n",
+ "On task task1\n",
+ "Computing windowed sums on original\n",
+ "Generating null dist\n",
+ "peak(mu)= 0.002913441493175924\n",
+ "Computing threshold\n",
+ "Thresholds from null dist were -1.6024236977100372 and 0.8689517974853516\n",
+ "Final raw thresholds are -1.6024236977100372 and 0.8689517974853516\n",
+ "Final transformed thresholds are -0.9164112903225806 and 0.8961962365591398\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 510 coords\n",
+ "On task task2\n",
+ "Computing windowed sums on original\n",
+ "Generating null dist\n",
+ "peak(mu)= -0.006281149625778198\n",
+ "Computing threshold\n",
+ "Thresholds from null dist were -1.2921525835990906 and 0.8464264869689941\n",
+ "Final raw thresholds are -1.2921525835990906 and 0.8464264869689941\n",
+ "Final transformed thresholds are -0.8990860215053763 and 0.8863844086021505\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 506 coords\n",
+ "After resolving overlaps, got 966 seqlets\n",
+ "Across all tasks, the weakest transformed threshold used was: 0.8828569892473118\n",
+ "MEMORY 0.327684096\n",
+ "966 identified in total\n",
+ "2 activity patterns with support >= 100 out of 27 possible patterns\n",
+ "Metacluster sizes: [452, 430]\n",
+ "Idx to activities: {0: '1,0,1', 1: '1,1,0'}\n",
+ "MEMORY 0.32770048\n",
+ "On metacluster 1\n",
+ "Metacluster size 430\n",
+ "Relevant tasks: ('task0', 'task1')\n",
+ "Relevant signs: (1, 1)\n",
+ "WARNING:tensorflow:From /Users/avantishrikumar/Research/tfmodisco/modisco/backend/tensorflow_backend.py:87: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+ "\n",
+ "TfModiscoSeqletsToPatternsFactory: seed=1234\n",
+ "(Round 1) num seqlets: 430\n",
+ "(Round 1) Computing coarse affmat\n",
+ "MEMORY 0.327397376\n",
+ "Beginning embedding computation\n",
+ "Computing embeddings\n",
+ "WARNING:tensorflow:From /Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/avantishrikumar/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Finished embedding computation in 0.45 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 0.02 s\n",
+ "(Round 1) Compute nearest neighbors from coarse affmat\n",
+ "MEMORY 0.36007936\n",
+ "Computed nearest neighbors in 0.04 s\n",
+ "MEMORY 0.36141056\n",
+ "(Round 1) Computing affinity matrix on nearest neighbors\n",
+ "MEMORY 0.36141056\n",
+ "Launching nearest neighbors affmat calculation job\n",
+ "MEMORY 0.367468544\n",
+ "Parallel runs completed\n",
+ "MEMORY 0.3761152\n",
+ "Job completed in: 4.3 s\n",
+ "MEMORY 0.377602048\n",
+ "Launching nearest neighbors affmat calculation job\n",
+ "MEMORY 0.376225792\n",
+ "Parallel runs completed\n",
+ "MEMORY 0.377622528\n",
+ "Job completed in: 4.7 s\n",
+ "MEMORY 0.37910528\n",
+ "(Round 1) Computed affinity matrix on nearest neighbors in 9.17 s\n",
+ "MEMORY 0.374779904\n",
+ "Filtered down to 416 of 430\n",
+ "(Round 1) Retained 416 rows out of 430 after filtering\n",
+ "MEMORY 0.374956032\n",
+ "(Round 1) Computing density adapted affmat\n",
+ "MEMORY 0.373579776\n",
+ "[t-SNE] Computing 31 nearest neighbors...\n",
+ "[t-SNE] Indexed 416 samples in 0.000s...\n",
+ "[t-SNE] Computed neighbors for 416 samples in 0.004s...\n",
+ "[t-SNE] Computed conditional probabilities for sample 416 / 416\n",
+ "[t-SNE] Mean sigma: 0.197686\n",
+ "(Round 1) Computing clustering\n",
+ "MEMORY 0.363884544\n",
+ "Beginning preprocessing + Leiden\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 0%| | 0/50 [00:00, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.48971911657436695\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 2%|▏ | 1/50 [00:00<00:06, 7.47it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.4905305462937447\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 42%|████▏ | 21/50 [00:01<00:02, 10.64it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.4907527848844986\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 50/50 [00:05<00:00, 9.64it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 13 clusters after round 1\n",
+ "Counts:\n",
+ "{2: 47, 5: 39, 3: 44, 8: 18, 6: 37, 0: 74, 9: 12, 1: 57, 4: 40, 7: 32, 10: 9, 12: 3, 11: 4}\n",
+ "MEMORY 0.357941248\n",
+ "(Round 1) Aggregating seqlets in each cluster\n",
+ "MEMORY 0.357941248\n",
+ "Aggregating for cluster 0 with 74 seqlets\n",
+ "MEMORY 0.357941248\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Trimmed 5 out of 74\n",
+ "Skipped 6 seqlets\n",
+ "Aggregating for cluster 1 with 57 seqlets\n",
+ "MEMORY 0.357941248\n",
+ "Trimmed 1 out of 57\n",
+ "Skipped 6 seqlets\n",
+ "Aggregating for cluster 2 with 47 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 4 out of 47\n",
+ "Skipped 9 seqlets\n",
+ "Aggregating for cluster 3 with 44 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 2 out of 44\n",
+ "Skipped 4 seqlets\n",
+ "Aggregating for cluster 4 with 40 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 0 out of 40\n",
+ "Skipped 6 seqlets\n",
+ "Aggregating for cluster 5 with 39 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 2 out of 39\n",
+ "Skipped 5 seqlets\n",
+ "Aggregating for cluster 6 with 37 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 0 out of 37\n",
+ "Skipped 10 seqlets\n",
+ "Removed 3 duplicate seqlets\n",
+ "Aggregating for cluster 7 with 32 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 1 out of 32\n",
+ "Skipped 3 seqlets\n",
+ "Aggregating for cluster 8 with 18 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 0 out of 18\n",
+ "Skipped 2 seqlets\n",
+ "Aggregating for cluster 9 with 12 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 0 out of 12\n",
+ "Skipped 1 seqlets\n",
+ "Aggregating for cluster 10 with 9 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 0 out of 9\n",
+ "Skipped 1 seqlets\n",
+ "Aggregating for cluster 11 with 4 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 0 out of 4\n",
+ "Skipped 1 seqlets\n",
+ "Aggregating for cluster 12 with 3 seqlets\n",
+ "MEMORY 0.357945344\n",
+ "Trimmed 0 out of 3\n",
+ "(Round 2) num seqlets: 339\n",
+ "(Round 2) Computing coarse affmat\n",
+ "MEMORY 0.357945344\n",
+ "Beginning embedding computation\n",
+ "Computing embeddings\n",
+ "Finished embedding computation in 0.31 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 0.02 s\n",
+ "(Round 2) Compute nearest neighbors from coarse affmat\n",
+ "MEMORY 0.366112768\n",
+ "Computed nearest neighbors in 0.04 s\n",
+ "MEMORY 0.3663872\n",
+ "(Round 2) Computing affinity matrix on nearest neighbors\n",
+ "MEMORY 0.3663872\n",
+ "Launching nearest neighbors affmat calculation job\n",
+ "MEMORY 0.36747264\n",
+ "Parallel runs completed\n",
+ "MEMORY 0.374185984\n",
+ "Job completed in: 3.27 s\n",
+ "MEMORY 0.375107584\n",
+ "Launching nearest neighbors affmat calculation job\n",
+ "MEMORY 0.374022144\n",
+ "Parallel runs completed\n",
+ "MEMORY 0.3762176\n",
+ "Job completed in: 3.26 s\n",
+ "MEMORY 0.3762176\n",
+ "(Round 2) Computed affinity matrix on nearest neighbors in 6.68 s\n",
+ "MEMORY 0.371769344\n",
+ "Not applying filtering for rounds above first round\n",
+ "MEMORY 0.371769344\n",
+ "(Round 2) Computing density adapted affmat\n",
+ "MEMORY 0.370683904\n",
+ "[t-SNE] Computing 31 nearest neighbors...\n",
+ "[t-SNE] Indexed 339 samples in 0.001s...\n",
+ "[t-SNE] Computed neighbors for 339 samples in 0.003s...\n",
+ "[t-SNE] Computed conditional probabilities for sample 339 / 339\n",
+ "[t-SNE] Mean sigma: 0.202385\n",
+ "(Round 2) Computing clustering\n",
+ "MEMORY 0.364630016\n",
+ "Beginning preprocessing + Leiden\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 0%| | 0/50 [00:00, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.48245362449089485\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 12%|█▏ | 6/50 [00:00<00:03, 11.69it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.48274476652156467\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 24%|██▍ | 12/50 [00:01<00:03, 11.19it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.4828944885269286\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 28%|██▊ | 14/50 [00:01<00:03, 10.88it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.4841651612393314\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 92%|█████████▏| 46/50 [00:04<00:00, 9.99it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.4841946301767339\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 50/50 [00:04<00:00, 10.67it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 15 clusters after round 2\n",
+ "Counts:\n",
+ "{6: 26, 2: 46, 3: 35, 8: 14, 7: 23, 11: 6, 4: 31, 5: 28, 9: 7, 1: 48, 0: 59, 14: 2, 10: 7, 12: 4, 13: 3}\n",
+ "MEMORY 0.359653376\n",
+ "(Round 2) Aggregating seqlets in each cluster\n",
+ "MEMORY 0.359653376\n",
+ "Aggregating for cluster 0 with 59 seqlets\n",
+ "MEMORY 0.359653376\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Trimmed 0 out of 59\n",
+ "Aggregating for cluster 1 with 48 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 7 out of 48\n",
+ "Aggregating for cluster 2 with 46 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 46\n",
+ "Removed 2 duplicate seqlets\n",
+ "Aggregating for cluster 3 with 35 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 35\n",
+ "Removed 3 duplicate seqlets\n",
+ "Aggregating for cluster 4 with 31 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 31\n",
+ "Removed 1 duplicate seqlets\n",
+ "Aggregating for cluster 5 with 28 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 5 out of 28\n",
+ "Aggregating for cluster 6 with 26 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 26\n",
+ "Removed 1 duplicate seqlets\n",
+ "Aggregating for cluster 7 with 23 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 1 out of 23\n",
+ "Aggregating for cluster 8 with 14 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 14\n",
+ "Removed 2 duplicate seqlets\n",
+ "Aggregating for cluster 9 with 7 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 7\n",
+ "Removed 2 duplicate seqlets\n",
+ "Aggregating for cluster 10 with 7 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 7\n",
+ "Removed 1 duplicate seqlets\n",
+ "Aggregating for cluster 11 with 6 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 6\n",
+ "Removed 1 duplicate seqlets\n",
+ "Aggregating for cluster 12 with 4 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 4\n",
+ "Aggregating for cluster 13 with 3 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 3\n",
+ "Aggregating for cluster 14 with 2 seqlets\n",
+ "MEMORY 0.359653376\n",
+ "Trimmed 0 out of 2\n",
+ "Removed 1 duplicate seqlets\n",
+ "Got 15 clusters\n",
+ "Splitting into subclusters...\n",
+ "MEMORY 0.359653376\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.010904073715209961 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.00403376\n",
+ "Louvain completed 21 runs in 0.31067609786987305 seconds\n",
+ "Similarity is 0.94495195; is_dissimilar is False\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.0042569637298583984 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.00292104\n",
+ "Louvain completed 21 runs in 0.23947811126708984 seconds\n",
+ "Similarity is 0.9287914; is_dissimilar is False\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.0053060054779052734 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.00229859\n",
+ "After 2 runs, maximum modularity is Q = 0.0022986\n",
+ "After 3 runs, maximum modularity is Q = 0.00239885\n",
+ "Louvain completed 23 runs in 0.27719569206237793 seconds\n",
+ "Similarity is 0.95961654; is_dissimilar is False\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.002979278564453125 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.000323913\n",
+ "Louvain completed 21 runs in 0.24344897270202637 seconds\n",
+ "Similarity is 0.9565493; is_dissimilar is False\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.002705097198486328 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.000491458\n",
+ "After 2 runs, maximum modularity is Q = 0.00102329\n",
+ "After 19 runs, maximum modularity is Q = 0.0010233\n",
+ "Louvain completed 39 runs in 0.44466423988342285 seconds\n",
+ "Similarity is 0.9187425; is_dissimilar is False\n",
+ "Merging on 15 clusters\n",
+ "MEMORY 0.359067648\n",
+ "On merging iteration 1\n",
+ "Computing pattern to seqlet distances\n",
+ "Computing pattern to pattern distances\n",
+ "Collapsing 1 & 7 with prob 0.00010821509139562128 and sim 1.959041536212592\n",
+ "Collapsing 1 & 5 with prob 6.896857960322775e-05 and sim 1.9588828088319594\n",
+ "Collapsing 2 & 7 with prob 9.992099184352932e-06 and sim 1.955667034297618\n",
+ "Collapsing 2 & 5 with prob 3.1729896693096234e-05 and sim 1.950573001945009\n",
+ "Collapsing 5 & 7 with prob 2.608559755424074e-05 and sim 1.9466473645705553\n",
+ "Collapsing 4 & 6 with prob 3.8128416491381845e-06 and sim 1.941499870826022\n",
+ "Collapsing 0 & 10 with prob 4.847296991854773e-06 and sim 1.9404022160442373\n",
+ "Collapsing 2 & 6 with prob 1.7755364100458913e-06 and sim 1.9393011301017133\n",
+ "Collapsing 2 & 4 with prob 9.209342348591746e-06 and sim 1.9388920745278098\n",
+ "Collapsing 0 & 7 with prob 9.766174718368726e-06 and sim 1.9350322173625005\n",
+ "Collapsing 0 & 4 with prob 1.3711612473089189e-06 and sim 1.934675059380602\n",
+ "Collapsing 3 & 6 with prob 3.192309506327678e-06 and sim 1.9342126566830375\n",
+ "Collapsing 0 & 6 with prob 1.0486648096135339e-06 and sim 1.9323116350373053\n",
+ "Collapsing 3 & 5 with prob 1.0661836790873608e-05 and sim 1.9311205342318687\n",
+ "Collapsing 5 & 6 with prob 4.841349704694653e-06 and sim 1.9278788756187435\n",
+ "Collapsing 1 & 2 with prob 2.993341783530882e-05 and sim 1.9263343268183881\n",
+ "Collapsing 6 & 7 with prob 5.936521461596988e-06 and sim 1.9243331853700039\n",
+ "Collapsing 2 & 3 with prob 4.137046625798272e-06 and sim 1.9220706457528287\n",
+ "Collapsing 0 & 5 with prob 1.6829677809323514e-05 and sim 1.9211645182588817\n",
+ "Collapsing 0 & 2 with prob 0.00025755810601835297 and sim 1.9181484326991278\n",
+ "Collapsing 3 & 7 with prob 1.2070856833649016e-05 and sim 1.9172934691767307\n",
+ "Collapsing 1 & 3 with prob 9.778722372151225e-06 and sim 1.9097593644096444\n",
+ "Collapsing 7 & 10 with prob 1.0312878775250276e-06 and sim 1.9088361130724152\n",
+ "Collapsing 0 & 1 with prob 1.7525100510278003e-05 and sim 1.9080365013747114\n",
+ "Collapsing 5 & 10 with prob 1.6657172974053997e-06 and sim 1.9055149499519726\n",
+ "Collapsing 2 & 10 with prob 4.187523161222658e-06 and sim 1.896904619009965\n",
+ "Collapsing 1 & 10 with prob 5.390553125792238e-06 and sim 1.8934567633542074\n",
+ "Collapsing 1 & 6 with prob 1.4291132728427248e-06 and sim 1.8830612425063842\n",
+ "Trimmed 0 out of 63\n",
+ "Trimmed 0 out of 86\n",
+ "Trimmed 0 out of 130\n",
+ "Trimmed 0 out of 55\n",
+ "Trimmed 6 out of 65\n",
+ "Trimmed 0 out of 185\n",
+ "Trimmed 0 out of 244\n",
+ "Trimmed 0 out of 276\n",
+ "On merging iteration 2\n",
+ "Computing pattern to seqlet distances\n",
+ "Computing pattern to pattern distances\n",
+ "Collapsing 0 & 4 with prob 3.292668837229365e-05 and sim 1.782329826230128\n",
+ "Trimmed 4 out of 280\n",
+ "On merging iteration 3\n",
+ "Computing pattern to seqlet distances\n",
+ "Computing pattern to pattern distances\n",
+ "Got 6 patterns after merging\n",
+ "MEMORY 0.359387136\n",
+ "Performing seqlet reassignment\n",
+ "MEMORY 0.359387136\n",
+ "Cross contin jaccard time taken: 3.26 s\n",
+ "Cross contin jaccard time taken: 0.02 s\n",
+ "Skipped 1 seqlets\n",
+ "Got 1 patterns after reassignment\n",
+ "MEMORY 0.359784448\n",
+ "Total time taken is 41.99s\n",
+ "MEMORY 0.359784448\n",
+ "On metacluster 0\n",
+ "Metacluster size 452\n",
+ "Relevant tasks: ('task0', 'task2')\n",
+ "Relevant signs: (1, 1)\n",
+ "TfModiscoSeqletsToPatternsFactory: seed=1234\n",
+ "(Round 1) num seqlets: 452\n",
+ "(Round 1) Computing coarse affmat\n",
+ "MEMORY 0.360382464\n",
+ "Beginning embedding computation\n",
+ "Computing embeddings\n",
+ "Finished embedding computation in 0.47 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 0.01 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",
+ "MEMORY 0.37441536\n",
+ "Computed nearest neighbors in 0.05 s\n",
+ "MEMORY 0.37441536\n",
+ "(Round 1) Computing affinity matrix on nearest neighbors\n",
+ "MEMORY 0.37441536\n",
+ "Launching nearest neighbors affmat calculation job\n",
+ "MEMORY 0.37681152\n",
+ "Parallel runs completed\n",
+ "MEMORY 0.38500352\n",
+ "Job completed in: 5.56 s\n",
+ "MEMORY 0.38664192\n",
+ "Launching nearest neighbors affmat calculation job\n",
+ "MEMORY 0.386576384\n",
+ "Parallel runs completed\n",
+ "MEMORY 0.38682624\n",
+ "Job completed in: 5.64 s\n",
+ "MEMORY 0.38846464\n",
+ "(Round 1) Computed affinity matrix on nearest neighbors in 11.41 s\n",
+ "MEMORY 0.382541824\n",
+ "Filtered down to 435 of 452\n",
+ "(Round 1) Retained 435 rows out of 452 after filtering\n",
+ "MEMORY 0.382541824\n",
+ "(Round 1) Computing density adapted affmat\n",
+ "MEMORY 0.382476288\n",
+ "[t-SNE] Computing 31 nearest neighbors...\n",
+ "[t-SNE] Indexed 435 samples in 0.000s...\n",
+ "[t-SNE] Computed neighbors for 435 samples in 0.005s...\n",
+ "[t-SNE] Computed conditional probabilities for sample 435 / 435\n",
+ "[t-SNE] Mean sigma: 0.189410\n",
+ "(Round 1) Computing clustering\n",
+ "MEMORY 0.374464512\n",
+ "Beginning preprocessing + Leiden\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 0%| | 0/50 [00:00, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.5554494141896046\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 4%|▍ | 2/50 [00:00<00:03, 12.46it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.5564208956619566\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 6%|▌ | 3/50 [00:00<00:04, 11.56it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.5567853955560109\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 14%|█▍ | 7/50 [00:00<00:04, 9.93it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.5570657214503386\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 50/50 [00:04<00:00, 10.47it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 9 clusters after round 1\n",
+ "Counts:\n",
+ "{0: 130, 2: 70, 1: 70, 3: 55, 4: 40, 8: 2, 6: 24, 5: 34, 7: 10}\n",
+ "MEMORY 0.365400064\n",
+ "(Round 1) Aggregating seqlets in each cluster\n",
+ "MEMORY 0.365400064\n",
+ "Aggregating for cluster 0 with 130 seqlets\n",
+ "MEMORY 0.365400064\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Trimmed 7 out of 130\n",
+ "Skipped 17 seqlets\n",
+ "Aggregating for cluster 1 with 70 seqlets\n",
+ "MEMORY 0.365400064\n",
+ "Trimmed 9 out of 70\n",
+ "Skipped 10 seqlets\n",
+ "Aggregating for cluster 2 with 70 seqlets\n",
+ "MEMORY 0.365400064\n",
+ "Trimmed 1 out of 70\n",
+ "Skipped 10 seqlets\n",
+ "Aggregating for cluster 3 with 55 seqlets\n",
+ "MEMORY 0.365400064\n",
+ "Trimmed 4 out of 55\n",
+ "Skipped 11 seqlets\n",
+ "Aggregating for cluster 4 with 40 seqlets\n",
+ "MEMORY 0.365400064\n",
+ "Trimmed 0 out of 40\n",
+ "Skipped 6 seqlets\n",
+ "Aggregating for cluster 5 with 34 seqlets\n",
+ "MEMORY 0.365400064\n",
+ "Trimmed 5 out of 34\n",
+ "Skipped 2 seqlets\n",
+ "Aggregating for cluster 6 with 24 seqlets\n",
+ "MEMORY 0.365400064\n",
+ "Trimmed 2 out of 24\n",
+ "Skipped 4 seqlets\n",
+ "Aggregating for cluster 7 with 10 seqlets\n",
+ "MEMORY 0.365400064\n",
+ "Trimmed 0 out of 10\n",
+ "Skipped 1 seqlets\n",
+ "Aggregating for cluster 8 with 2 seqlets\n",
+ "MEMORY 0.365400064\n",
+ "Trimmed 0 out of 2\n",
+ "Skipped 1 seqlets\n",
+ "(Round 2) num seqlets: 327\n",
+ "(Round 2) Computing coarse affmat\n",
+ "MEMORY 0.365400064\n",
+ "Beginning embedding computation\n",
+ "Computing embeddings\n",
+ "Finished embedding computation in 0.34 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 0.02 s\n",
+ "(Round 2) Compute nearest neighbors from coarse affmat\n",
+ "MEMORY 0.370204672\n",
+ "Computed nearest neighbors in 0.04 s\n",
+ "MEMORY 0.370204672\n",
+ "(Round 2) Computing affinity matrix on nearest neighbors\n",
+ "MEMORY 0.370204672\n",
+ "Launching nearest neighbors affmat calculation job\n",
+ "MEMORY 0.371253248\n",
+ "Parallel runs completed\n",
+ "MEMORY 0.377790464\n",
+ "Job completed in: 2.87 s\n",
+ "MEMORY 0.378646528\n",
+ "Launching nearest neighbors affmat calculation job\n",
+ "MEMORY 0.377597952\n",
+ "Parallel runs completed\n",
+ "MEMORY 0.37971968\n",
+ "Job completed in: 2.8 s\n",
+ "MEMORY 0.37971968\n",
+ "(Round 2) Computed affinity matrix on nearest neighbors in 5.8 s\n",
+ "MEMORY 0.376377344\n",
+ "Not applying filtering for rounds above first round\n",
+ "MEMORY 0.376377344\n",
+ "(Round 2) Computing density adapted affmat\n",
+ "MEMORY 0.375328768\n",
+ "[t-SNE] Computing 31 nearest neighbors...\n",
+ "[t-SNE] Indexed 327 samples in 0.000s...\n",
+ "[t-SNE] Computed neighbors for 327 samples in 0.003s...\n",
+ "[t-SNE] Computed conditional probabilities for sample 327 / 327\n",
+ "[t-SNE] Mean sigma: 0.190154\n",
+ "(Round 2) Computing clustering\n",
+ "MEMORY 0.3666944\n",
+ "Beginning preprocessing + Leiden\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 0%| | 0/50 [00:00, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.5479075946056328\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 18%|█▊ | 9/50 [00:00<00:03, 11.77it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.5479372423730524\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 74%|███████▍ | 37/50 [00:02<00:00, 14.92it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quality: 0.5482526048758475\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 50/50 [00:03<00:00, 14.06it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 8 clusters after round 2\n",
+ "Counts:\n",
+ "{0: 59, 2: 51, 3: 48, 5: 35, 4: 48, 1: 56, 6: 26, 7: 4}\n",
+ "MEMORY 0.362962944\n",
+ "(Round 2) Aggregating seqlets in each cluster\n",
+ "MEMORY 0.362962944\n",
+ "Aggregating for cluster 0 with 59 seqlets\n",
+ "MEMORY 0.362962944\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Trimmed 0 out of 59\n",
+ "Aggregating for cluster 1 with 56 seqlets\n",
+ "MEMORY 0.362962944\n",
+ "Trimmed 4 out of 56\n",
+ "Aggregating for cluster 2 with 51 seqlets\n",
+ "MEMORY 0.362962944\n",
+ "Trimmed 0 out of 51\n",
+ "Skipped 1 seqlets\n",
+ "Aggregating for cluster 3 with 48 seqlets\n",
+ "MEMORY 0.362962944\n",
+ "Trimmed 7 out of 48\n",
+ "Aggregating for cluster 4 with 48 seqlets\n",
+ "MEMORY 0.362962944\n",
+ "Trimmed 2 out of 48\n",
+ "Aggregating for cluster 5 with 35 seqlets\n",
+ "MEMORY 0.362962944\n",
+ "Trimmed 1 out of 35\n",
+ "Skipped 1 seqlets\n",
+ "Aggregating for cluster 6 with 26 seqlets\n",
+ "MEMORY 0.362962944\n",
+ "Trimmed 1 out of 26\n",
+ "Aggregating for cluster 7 with 4 seqlets\n",
+ "MEMORY 0.362962944\n",
+ "Trimmed 0 out of 4\n",
+ "Got 8 clusters\n",
+ "Splitting into subclusters...\n",
+ "MEMORY 0.362962944\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.007914066314697266 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.00227626\n",
+ "Louvain completed 21 runs in 0.2538471221923828 seconds\n",
+ "Similarity is 0.96925807; is_dissimilar is False\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.006300926208496094 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.00230415\n",
+ "Louvain completed 21 runs in 0.24361300468444824 seconds\n",
+ "Similarity is 0.96899205; is_dissimilar is False\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.006196022033691406 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.00235718\n",
+ "After 11 runs, maximum modularity is Q = 0.00235719\n",
+ "Louvain completed 31 runs in 0.35912203788757324 seconds\n",
+ "Similarity is 0.96052647; is_dissimilar is False\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.004577159881591797 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.00125521\n",
+ "After 3 runs, maximum modularity is Q = 0.00125522\n",
+ "Louvain completed 23 runs in 0.2657630443572998 seconds\n",
+ "Similarity is 0.9618193; is_dissimilar is False\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.004706144332885742 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.000181289\n",
+ "After 2 runs, maximum modularity is Q = 0.000628284\n",
+ "After 3 runs, maximum modularity is Q = 0.000628288\n",
+ "After 4 runs, maximum modularity is Q = 0.00063525\n",
+ "After 5 runs, maximum modularity is Q = 0.000635251\n",
+ "After 6 runs, maximum modularity is Q = 0.000635254\n",
+ "After 7 runs, maximum modularity is Q = 0.000635255\n",
+ "Louvain completed 27 runs in 0.3477931022644043 seconds\n",
+ "Similarity is 0.9765953; is_dissimilar is False\n",
+ "Inspecting for spurious merging\n",
+ "Wrote graph to binary file in 0.0030851364135742188 seconds\n",
+ "Running Louvain modularity optimization\n",
+ "After 1 runs, maximum modularity is Q = 0.00456616\n",
+ "After 19 runs, maximum modularity is Q = 0.00456617\n",
+ "Louvain completed 39 runs in 0.4204599857330322 seconds\n",
+ "Similarity is 0.94317603; is_dissimilar is False\n",
+ "Merging on 8 clusters\n",
+ "MEMORY 0.362184704\n",
+ "On merging iteration 1\n",
+ "Computing pattern to seqlet distances\n",
+ "Computing pattern to pattern distances\n",
+ "Collapsing 1 & 4 with prob 0.0002067281574470614 and sim 1.976476658900082\n",
+ "Collapsing 0 & 2 with prob 0.00022124049869275863 and sim 1.971579017619503\n",
+ "Collapsing 2 & 4 with prob 1.8791840350905866e-05 and sim 1.951501640876167\n",
+ "Collapsing 3 & 4 with prob 7.23683419252228e-06 and sim 1.9467783822295426\n",
+ "Collapsing 1 & 2 with prob 2.5721007376863484e-05 and sim 1.9405171974106554\n",
+ "Collapsing 1 & 3 with prob 8.479080563635449e-06 and sim 1.937925727492081\n",
+ "Collapsing 1 & 6 with prob 5.183669806896477e-05 and sim 1.935791555966858\n",
+ "Collapsing 3 & 6 with prob 4.626910300141632e-06 and sim 1.931123739477634\n",
+ "Collapsing 0 & 6 with prob 5.945253362473767e-06 and sim 1.926481830871755\n",
+ "Collapsing 0 & 1 with prob 2.608718013399344e-05 and sim 1.9242088190459632\n",
+ "Collapsing 0 & 4 with prob 3.065849538264967e-06 and sim 1.922575635068685\n",
+ "Collapsing 4 & 5 with prob 2.4627383501438077e-06 and sim 1.9190827458553787\n",
+ "Collapsing 5 & 6 with prob 2.5994031930364462e-06 and sim 1.916426275793734\n",
+ "Collapsing 1 & 5 with prob 2.766854188117575e-06 and sim 1.9089840253863368\n",
+ "Collapsing 4 & 6 with prob 3.1139306374555657e-06 and sim 1.9015893568245548\n",
+ "Collapsing 2 & 5 with prob 2.2563890274233244e-06 and sim 1.8641256376288091\n",
+ "Trimmed 0 out of 98\n",
+ "Trimmed 0 out of 109\n",
+ "Trimmed 0 out of 207\n",
+ "Trimmed 0 out of 248\n",
+ "Trimmed 0 out of 273\n",
+ "Trimmed 0 out of 306\n",
+ "On merging iteration 2\n",
+ "Computing pattern to seqlet distances\n",
+ "Computing pattern to pattern distances\n",
+ "Got 2 patterns after merging\n",
+ "MEMORY 0.363900928\n",
+ "Performing seqlet reassignment\n",
+ "MEMORY 0.363900928\n",
+ "Cross contin jaccard time taken: 0.01 s\n",
+ "Cross contin jaccard time taken: 0.01 s\n",
+ "Skipped 7 seqlets\n",
+ "Got 1 patterns after reassignment\n",
+ "MEMORY 0.363900928\n",
+ "Total time taken is 36.34s\n",
+ "MEMORY 0.363900928\n"
+ ]
+ }
+ ],
+ "source": [
+ "import h5py\n",
+ "import numpy as np\n",
+ "import modisco\n",
+ "\n",
+ "from importlib import reload\n",
+ "import h5py\n",
+ "import numpy as np\n",
+ "import modisco\n",
+ "import modisco.cluster.phenograph.core\n",
+ "reload(modisco.cluster.phenograph.core)\n",
+ "import modisco.cluster.phenograph.cluster\n",
+ "reload(modisco.cluster.phenograph.cluster)\n",
+ "import modisco.cluster.phenograph\n",
+ "reload(modisco.cluster.phenograph)\n",
+ "import modisco.cluster.core\n",
+ "reload(modisco.cluster.core)\n",
+ "import modisco.cluster\n",
+ "reload(modisco.cluster)\n",
+ "import modisco.affinitymat.core\n",
+ "reload(modisco.affinitymat.core)\n",
+ "import modisco.affinitymat.transformers\n",
+ "reload(modisco.affinitymat.transformers)\n",
+ "import modisco.tfmodisco_workflow.seqlets_to_patterns\n",
+ "reload(modisco.tfmodisco_workflow.seqlets_to_patterns)\n",
+ "import modisco.tfmodisco_workflow.workflow\n",
+ "reload(modisco.tfmodisco_workflow.workflow)\n",
+ "import modisco.nearest_neighbors\n",
+ "reload(modisco.nearest_neighbors)\n",
+ "import modisco.affinitymat\n",
+ "reload(modisco.affinitymat)\n",
+ "import modisco.aggregator\n",
+ "reload(modisco.aggregator)\n",
+ "import modisco.value_provider\n",
+ "reload(modisco.value_provider)\n",
+ "import modisco.core\n",
+ "reload(modisco.core)\n",
+ "import modisco.coordproducers\n",
+ "reload(modisco.coordproducers)\n",
+ "import modisco.metaclusterers\n",
+ "reload(modisco.metaclusterers)\n",
+ "import modisco.clusterinit.memeinit\n",
+ "reload(modisco.clusterinit.memeinit)\n",
+ "\n",
+ "from collections import OrderedDict\n",
+ "\n",
+ "task_names = [\"task0\", \"task1\", \"task2\"]\n",
+ "#null_per_pos_scores = modisco.coordproducers.LogPercentileGammaNullDist(num_to_samp=20000)\n",
+ "#per_position_contrib_scores = OrderedDict([ \n",
+ "# (x, modisco.coordproducers.per_sequence_zscore_log_percentile_transform(\n",
+ " #max_num_to_use_for_percentile=20000,\n",
+ "# score_track=[np.sum(s,axis=1) for s in task_to_scores[x]])) \n",
+ "# for x in task_names])\n",
+ "\n",
+ "tfmodisco_results = modisco.tfmodisco_workflow.workflow.TfModiscoWorkflow(\n",
+ " #Slight modifications from the default settings\n",
+ " sliding_window_size=15,\n",
+ " flank_size=5,\n",
+ " target_seqlet_fdr=0.01,\n",
+ " min_passing_windows_frac=0.03,\n",
+ " seqlets_to_patterns_factory=\n",
+ " modisco.tfmodisco_workflow.seqlets_to_patterns.TfModiscoSeqletsToPatternsFactory(\n",
+ " #Note: as of version 0.5.6.0, it's possible to use the results of a motif discovery\n",
+ " # software like MEME to improve the TF-MoDISco clustering. To use the meme-based\n",
+ " # initialization, you would specify the initclusterer_factory as shown in the\n",
+ " # commented-out code below:\n",
+ " #initclusterer_factory=modisco.clusterinit.memeinit.MemeInitClustererFactory( \n",
+ " # meme_command=\"meme\", base_outdir=\"meme_out\", \n",
+ " # max_num_seqlets_to_use=10000, nmotifs=10, n_jobs=1),\n",
+ " trim_to_window_size=15,\n",
+ " initial_flank_to_add=5,\n",
+ " kmer_len=5, num_gaps=1,\n",
+ " num_mismatches=0,\n",
+ " final_min_cluster_size=30)\n",
+ " )(\n",
+ " task_names=task_names,\n",
+ " contrib_scores=task_to_scores,\n",
+ " hypothetical_contribs=task_to_hyp_scores,\n",
+ " one_hot=onehot_data,\n",
+ " #per_position_contrib_scores=per_position_contrib_scores\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "ibQhUzCN_S0j"
+ },
+ "source": [
+ "## Save Results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 70
+ },
+ "colab_type": "code",
+ "id": "YAOto6Yx-5qD",
+ "outputId": "8873b422-ac37-4fc8-b5b0-332b9d85c085"
+ },
+ "outputs": [],
+ "source": [
+ "import h5py\n",
+ "import modisco.util\n",
+ "reload(modisco.util)\n",
+ "![[ -e results.hdf5 ]] && rm results.hdf5\n",
+ "grp = h5py.File(\"results.hdf5\")\n",
+ "tfmodisco_results.save_hdf5(grp)\n",
+ "grp.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "UxjUsY4B_Zcf"
+ },
+ "source": [
+ "## Print results directly from hdf5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "colab_type": "code",
+ "id": "SvRgsV6D_WYR",
+ "outputId": "9465678a-3ad7-4cf0-e821-a0891ef956b5",
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Metaclusters heatmap\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "metacluster_0\n",
+ "activity pattern: [1 0 1]\n",
+ "metacluster_0 pattern_0\n",
+ "total seqlets: 303\n",
+ "Task 0 hypothetical scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 0 actual importance scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 1 hypothetical scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 1 actual importance scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 2 hypothetical scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 2 actual importance scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "onehot, fwd and rev:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "metacluster_1\n",
+ "activity pattern: [1 1 0]\n",
+ "metacluster_1 pattern_0\n",
+ "total seqlets: 301\n",
+ "Task 0 hypothetical scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 0 actual importance scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 1 hypothetical scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 1 actual importance scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 2 hypothetical scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Task 2 actual importance scores:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "onehot, fwd and rev:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from collections import Counter\n",
+ "from modisco.visualization import viz_sequence\n",
+ "reload(viz_sequence)\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "import modisco.affinitymat.core\n",
+ "reload(modisco.affinitymat.core)\n",
+ "import modisco.cluster.phenograph.core\n",
+ "reload(modisco.cluster.phenograph.core)\n",
+ "import modisco.cluster.phenograph.cluster\n",
+ "reload(modisco.cluster.phenograph.cluster)\n",
+ "import modisco.cluster.core\n",
+ "reload(modisco.cluster.core)\n",
+ "import modisco.aggregator\n",
+ "reload(modisco.aggregator)\n",
+ "\n",
+ "hdf5_results = h5py.File(\"results.hdf5\",\"r\")\n",
+ "\n",
+ "print(\"Metaclusters heatmap\")\n",
+ "import seaborn as sns\n",
+ "activity_patterns = np.array(hdf5_results['metaclustering_results']['attribute_vectors'])[\n",
+ " np.array(\n",
+ " [x[0] for x in sorted(\n",
+ " enumerate(hdf5_results['metaclustering_results']['metacluster_indices']),\n",
+ " key=lambda x: x[1])])]\n",
+ "sns.heatmap(activity_patterns, center=0)\n",
+ "plt.show()\n",
+ "\n",
+ "metacluster_names = [\n",
+ " x.decode(\"utf-8\") for x in \n",
+ " list(hdf5_results[\"metaclustering_results\"]\n",
+ " [\"all_metacluster_names\"][:])]\n",
+ "\n",
+ "all_patterns = []\n",
+ "background = np.array([0.27, 0.23, 0.23, 0.27])\n",
+ "\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",
+ " if (len(all_pattern_names)==0):\n",
+ " print(\"No motifs found for this activity pattern\")\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",
+ " print(\"total seqlets:\",len(pattern[\"seqlets_and_alnmts\"][\"seqlets\"]))\n",
+ " print(\"Task 0 hypothetical scores:\")\n",
+ " viz_sequence.plot_weights(pattern[\"task0_hypothetical_contribs\"][\"fwd\"])\n",
+ " print(\"Task 0 actual importance scores:\")\n",
+ " viz_sequence.plot_weights(pattern[\"task0_contrib_scores\"][\"fwd\"])\n",
+ " print(\"Task 1 hypothetical scores:\")\n",
+ " viz_sequence.plot_weights(pattern[\"task1_hypothetical_contribs\"][\"fwd\"])\n",
+ " print(\"Task 1 actual importance scores:\")\n",
+ " viz_sequence.plot_weights(pattern[\"task1_contrib_scores\"][\"fwd\"])\n",
+ " print(\"Task 2 hypothetical scores:\")\n",
+ " viz_sequence.plot_weights(pattern[\"task2_hypothetical_contribs\"][\"fwd\"])\n",
+ " print(\"Task 2 actual importance scores:\")\n",
+ " viz_sequence.plot_weights(pattern[\"task2_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",
+ "hdf5_results.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "UFQSYr9q_qI9"
+ },
+ "source": [
+ "## Load the saved hdf5 file\n",
+ "Load the results object from the saved file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "WVtrMZ9o_mu0"
+ },
+ "outputs": [],
+ "source": [
+ "import h5py\n",
+ "import numpy as np\n",
+ "import modisco\n",
+ "from modisco.tfmodisco_workflow import workflow\n",
+ "\n",
+ "track_set = modisco.tfmodisco_workflow.workflow.prep_track_set(\n",
+ " task_names=tasks,\n",
+ " contrib_scores=task_to_scores,\n",
+ " hypothetical_contribs=task_to_hyp_scores,\n",
+ " one_hot=onehot_data)\n",
+ "\n",
+ "grp = h5py.File(\"results.hdf5\",\"r\")\n",
+ "loaded_tfmodisco_results =\\\n",
+ " workflow.TfModiscoResults.from_hdf5(grp, track_set=track_set)\n",
+ "grp.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "GYkC2dIY0kaF"
+ },
+ "outputs": [],
+ "source": [
+ "pattern = (loaded_tfmodisco_results.\n",
+ " metacluster_idx_to_submetacluster_results[\"metacluster_0\"].\n",
+ " seqlets_to_patterns_result.patterns[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "#I don't know why it seems necessary for this to appear twice?\n",
+ "#It doesn't work when I just have '%matplotlib notebook' once\n",
+ "# (I end up with a blank plot below if I just have it once)\n",
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Computing pairwise similarities\n",
+ "Computing tsne embedding\n",
+ "Computed embedding\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('
');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '
');\n",
+ " var titletext = $(\n",
+ " '
');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('
');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $(' ');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $(' ');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('
');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $(' ');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $(' ');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $(' ');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $(' ');\n",
+ "\n",
+ " var fmt_picker = $(' ');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " ' ', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"
\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html(' ');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = ' ';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('
');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $(' ');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $(' ');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('
');\n",
+ " var button = $(' ');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import modisco.visualization\n",
+ "reload(modisco.visualization)\n",
+ "reload(modisco.visualization.interactive)\n",
+ "\n",
+ "modisco.visualization.interactive.make_interactive_plot(\n",
+ " pattern=pattern,\n",
+ " track_names_and_signs=[(\"task0_contrib_scores\", 1)],\n",
+ " perplexity=10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "include_colab_link": true,
+ "name": "(On Google Colab) With Hit Scoring TF MoDISco TAL GATA.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/modisco.egg-info/PKG-INFO b/modisco.egg-info/PKG-INFO
index ff30d8d..6a91141 100644
--- a/modisco.egg-info/PKG-INFO
+++ b/modisco.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: modisco
-Version: 0.5.7.0
+Version: 0.5.7.1
Summary: TF MOtif Discovery from Importance SCOres
Home-page: https://github.com/kundajelab/tfmodisco
License: UNKNOWN
diff --git a/modisco/visualization/__init__.py b/modisco/visualization/__init__.py
index e69de29..4c6b245 100644
--- a/modisco/visualization/__init__.py
+++ b/modisco/visualization/__init__.py
@@ -0,0 +1,3 @@
+from __future__ import division, print_function, absolute_import
+from . import interactive
+from . import viz_sequence
diff --git a/modisco/visualization/interactive.py b/modisco/visualization/interactive.py
new file mode 100644
index 0000000..dcf9e1a
--- /dev/null
+++ b/modisco/visualization/interactive.py
@@ -0,0 +1,159 @@
+from matplotlib.widgets import RectangleSelector
+from matplotlib.path import Path
+import matplotlib.pyplot as plt
+from . import viz_sequence
+from .. import affinitymat
+import sklearn.manifold
+import numpy as np
+
+
+def l1_norm_features(features_mat):
+ return features_mat/np.sum(np.abs(features_mat), axis=1)[:,None]
+
+
+def compute_pairwise_continjacc_simmat(pattern, track_names_and_signs):
+ flattened_contrib_scores_vector = np.array([
+ np.sum([seqlet[track_name].fwd.flatten()*sign
+ for track_name,sign in track_names_and_signs], axis=0)
+ for seqlet in pattern.seqlets])
+ normed_flattened_contrib_scores_vector =\
+ l1_norm_features(flattened_contrib_scores_vector)
+ sim_mat = np.zeros((len(pattern.seqlets), len(pattern.seqlets)))
+ for i in range(len(pattern.seqlets)):
+ sim_mat[i] = affinitymat.core.contin_jaccard_vec_mat_sim(
+ a_row=normed_flattened_contrib_scores_vector[i],
+ mat=normed_flattened_contrib_scores_vector)
+ return sim_mat
+
+
+def get_tsne_embedding(pattern, track_names_and_signs, perplexity,
+ seed=1234):
+ print("Computing pairwise similarities")
+ pairwise_simmat = compute_pairwise_continjacc_simmat(
+ pattern=pattern,
+ track_names_and_signs=track_names_and_signs)
+ print("Computing tsne embedding")
+ tsne_embedding = (sklearn.manifold.TSNE(metric="precomputed",
+ verbose=0,
+ perplexity=perplexity,
+ random_state=seed)
+ .fit_transform(1/(np.maximum(pairwise_simmat, 1e-7) )))
+ #1/(pairwise_simmat) mapps the affinities to distances.
+ print("Computed embedding")
+ return tsne_embedding
+
+
+def make_interactive_plot(pattern, track_names_and_signs,
+ figsize=(10,7), height_ratios=[2,1,1],
+ perplexity=10):
+
+ tsne_embedding = get_tsne_embedding(
+ pattern=pattern,
+ track_names_and_signs=track_names_and_signs,
+ perplexity=perplexity)
+
+ fig, ax = plt.subplots(nrows=3, ncols=1,
+ gridspec_kw={'height_ratios': height_ratios},
+ figsize=figsize)
+
+ pts = ax[0].scatter(tsne_embedding[:, 0], tsne_embedding[:, 1])
+ selector = SelectFromCollection(ax[0], pts)
+
+ def accept(event):
+ selected_indices = selector.ind
+ all_seqlets = pattern.seqlets
+ ax[0].set_title("Number of points selected: "
+ +str(len(selected_indices)))
+ ax[1].clear()
+ ax[2].clear()
+ mean_contrib = np.mean(np.array([
+ all_seqlets[idx][track_name].fwd*sign
+ for idx in selected_indices
+ for (track_name, sign) in track_names_and_signs]), axis=0)
+ mean_onehot = np.mean(np.array([
+ all_seqlets[idx]["sequence"].fwd
+ for idx in selected_indices]), axis=0)
+ viz_sequence.plot_weights_given_ax(ax=ax[1], array=mean_contrib,
+ height_padding_factor=0.2,
+ length_padding=1.0,
+ subticks_frequency=2, highlight={})
+ viz_sequence.plot_weights_given_ax(ax=ax[2], array=mean_onehot,
+ height_padding_factor=0.2,
+ length_padding=1.0,
+ subticks_frequency=2, highlight={})
+ fig.canvas.draw()
+
+ fig.canvas.mpl_connect("button_release_event", accept)
+ plt.show()
+
+
+class SelectFromCollection(object):
+ """Select points from a matplotlib collection using `RectangleSelector`.
+
+ Selected indices are saved in the `ind` attribute. This tool fades out the
+ points that are not part of the selection (i.e., reduces their alpha
+ values). If your collection has alpha < 1, this tool will permanently
+ alter the alpha values.
+
+ Note that this tool selects collection objects based on their *origins*
+ (i.e., `offsets`).
+
+ Parameters
+ ----------
+ ax : :class:`~matplotlib.axes.Axes`
+ Axes to interact with.
+
+ collection : :class:`matplotlib.collections.Collection` subclass
+ Collection you want to select from.
+
+ alpha_other : 0 <= float <= 1
+ To highlight a selection, this tool sets all selected points to an
+ alpha value of 1 and non-selected points to `alpha_other`.
+ """
+
+ def __init__(self, ax, collection, alpha_other=0.3):
+ self.ax = ax
+ self.canvas = ax.figure.canvas
+ self.collection = collection
+ self.alpha_other = alpha_other
+
+ self.xys = np.array(collection.get_offsets())
+ self.Npts = len(self.xys)
+
+ # Ensure that we have separate colors for each object
+ self.fc = collection.get_facecolors()
+ assert len(self.fc)==1
+ self.orig_fc = np.array(self.fc[0])
+ if len(self.fc) == 0:
+ raise ValueError('Collection must have a facecolor')
+ elif len(self.fc) == 1:
+ self.fc = np.tile(self.fc, (self.Npts, 1))
+
+ self.selector = RectangleSelector(ax,
+ onselect=self.onselect, useblit=False)
+ self.ind = []
+
+ def onselect(self, eclick, erelease):
+ lowerx = min(eclick.xdata, erelease.xdata)
+ upperx = max(eclick.xdata, erelease.xdata)
+ lowery = min(eclick.ydata, erelease.ydata)
+ uppery = max(eclick.ydata, erelease.ydata)
+ self.ind = np.nonzero((self.xys[:,0] >= lowerx)
+ *(self.xys[:,0] <= upperx)
+ *(self.xys[:,1] >= lowery)
+ *(self.xys[:,1] <= uppery))[0]
+ self.fc[:, -1] = self.alpha_other
+ self.fc[:, 0:3] = self.orig_fc[None,:3]
+ #Red color for selection
+ self.fc[self.ind, -1] = 1
+ self.fc[self.ind, 0] = 1
+ self.fc[self.ind, 1] = 0
+ self.fc[self.ind, 2] = 0
+ self.collection.set_facecolors(self.fc)
+ self.canvas.draw_idle()
+
+ def disconnect(self):
+ self.selector.disconnect_events()
+ self.fc[:, -1] = 1
+ self.collection.set_facecolors(self.fc)
+ self.canvas.draw_idle()
diff --git a/setup.py b/setup.py
index b293ba4..f672c7d 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
description='TF MOtif Discovery from Importance SCOres',
long_description="""Algorithm for discovering consolidated patterns from base-pair-level importance scores""",
url='https://github.com/kundajelab/tfmodisco',
- version='0.5.7.0',
+ version='0.5.7.1',
packages=find_packages(),
package_data={
'': ['cluster/phenograph/louvain/*convert*', 'cluster/phenograph/louvain/*community*', 'cluster/phenograph/louvain/*hierarchy*']