From c4acbf5e8e9347f6f37cd84d3cff047c69f276ab Mon Sep 17 00:00:00 2001 From: nkempynck Date: Wed, 26 Jun 2024 17:17:08 +0200 Subject: [PATCH] added first part of pattern analysis (atac only) --- docs/tutorials/mouse_biccn.ipynb | 292 +++++++-- pyproject.toml | 1 + src/crested/pl/patterns/__init__.py | 2 +- src/crested/pl/patterns/_modisco_results.py | 107 ++++ src/crested/tl/__init__.py | 2 +- src/crested/tl/_modisco_utils.py | 122 ++++ src/crested/tl/_tfmodisco.py | 654 +++++++++++++++++++- 7 files changed, 1132 insertions(+), 48 deletions(-) create mode 100644 src/crested/tl/_modisco_utils.py diff --git a/docs/tutorials/mouse_biccn.ipynb b/docs/tutorials/mouse_biccn.ipynb index 986d5c8..157c114 100644 --- a/docs/tutorials/mouse_biccn.ipynb +++ b/docs/tutorials/mouse_biccn.ipynb @@ -18,11 +18,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-06-26 14:46:47.842156: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2024-06-26 14:46:47.881644: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "2024-06-26 17:04:44.489666: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-06-26 17:04:44.528740: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-06-26 14:46:49.415214: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "2024-06-26 14:46:51.642221: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78790 MB memory: -> device: 0, name: NVIDIA H100 80GB HBM3, pci bus id: 0000:55:00.0, compute capability: 9.0\n" + "2024-06-26 17:04:45.814551: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2024-06-26 17:04:47.555413: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78790 MB memory: -> device: 0, name: NVIDIA H100 80GB HBM3, pci bus id: 0000:55:00.0, compute capability: 9.0\n" ] } ], @@ -67,7 +67,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-06-26T14:46:57.920142+0200 INFO Extracting values from 19 bigWig files...\n" + "2024-06-26T17:04:53.906464+0200 INFO Extracting values from 19 bigWig files...\n" ] }, { @@ -1459,7 +1459,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1467,12 +1467,12 @@ "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n", - "2024-06-26T14:58:27.262228+0200 INFO Plotting bar plots for region: chr18:4369383-4371497, models: ['crested1']\n" + "2024-06-26T16:12:06.742377+0200 INFO Plotting bar plots for region: chr9:113095246-113097360, models: ['crested1']\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -1482,7 +1482,7 @@ } ], "source": [ - "idx = 32\n", + "idx = 7211\n", "chrom=test_df.iloc[idx]['chr']\n", "start=test_df.iloc[idx]['start']\n", "end=test_df.iloc[idx]['end']\n", @@ -1552,52 +1552,32 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['L5ET']\n", - "2024-06-26T14:58:31.495910+0200 INFO Calculating contribution scores for 1 class(es) and 1 region(s).\n" + "['L6b']\n", + "2024-06-26T16:13:10.860596+0200 INFO Calculating contribution scores for 1 class(es) and 1 region(s).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Region: 100%|██████████| 1/1 [00:02<00:00, 2.27s/it]\n" + "Region: 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]\n" ] } ], "source": [ - "cts =['L5ET']\n", + "cts =['L6b']\n", "scores, one_hot_encoded_sequences = evaluator.calculate_contribution_scores_regions(\n", - " region_idx = region, method='mutagenesis', class_names=cts\n", + " region_idx = region, method='expected_integrated_grad', class_names=cts\n", ")" ] }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.650580644607544" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scores.max()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1607,19 +1587,19 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-06-26T14:59:13.091642+0200 INFO Plotting contribution scores for 1 sequence(s)\n" + "2024-06-26T16:13:30.100093+0200 INFO Plotting contribution scores for 1 sequence(s)\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1630,7 +1610,7 @@ ], "source": [ "crested.pl.patterns.contribution_scores(\n", - " scores, one_hot_encoded_sequences, labels=cts, zoom_n_bases=500, method='mutagenesis'\n", + " scores, one_hot_encoded_sequences, labels=cts, zoom_n_bases=500#, method='mutagenesis'\n", ")" ] }, @@ -3057,7 +3037,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -3222,6 +3202,238 @@ "crested.pl.patterns.modisco_results(classes=['L5ET'], contribution='postive', contribution_dir='modisco_results2', num_seq=500, y_max=0.07, viz='contrib')" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading file modisco_results2/Astro_modisco_results.h5...\n", + "Match between Astro_pos_patterns_pattern_0 and Astro_neg_patterns_pattern_5\n", + "Match between Astro_pos_patterns_pattern_4 and Astro_pos_patterns_pattern_1\n", + "Reading file modisco_results2/Endo_modisco_results.h5...\n", + "Match between Endo_neg_patterns_pattern_10 and Astro_neg_patterns_pattern_0\n", + "Match between Endo_pos_patterns_pattern_3 and Endo_pos_patterns_pattern_0\n", + "Reading file modisco_results2/L2_3IT_modisco_results.h5...\n", + "Match between L2_3IT_neg_patterns_pattern_1 and Astro_neg_patterns_pattern_0\n", + "Match between L2_3IT_pos_patterns_pattern_0 and L2_3IT_neg_patterns_pattern_5\n", + "Match between L2_3IT_pos_patterns_pattern_10 and Astro_pos_patterns_pattern_0\n", + "Match between L2_3IT_pos_patterns_pattern_7 and L2_3IT_pos_patterns_pattern_3\n", + "Reading file modisco_results2/L5ET_modisco_results.h5...\n", + "Match between L5ET_neg_patterns_pattern_1 and Astro_neg_patterns_pattern_0\n", + "Match between L5ET_pos_patterns_pattern_0 and Astro_pos_patterns_pattern_0\n", + "Match between L5ET_pos_patterns_pattern_1 and L2_3IT_pos_patterns_pattern_7\n", + "Match between L5ET_pos_patterns_pattern_10 and L2_3IT_pos_patterns_pattern_8\n", + "Match between L5ET_pos_patterns_pattern_11 and L2_3IT_pos_patterns_pattern_0\n", + "Match between L5ET_pos_patterns_pattern_2 and L2_3IT_pos_patterns_pattern_4\n", + "Match between L5ET_pos_patterns_pattern_4 and Astro_pos_patterns_pattern_2\n", + "Match between L5ET_pos_patterns_pattern_5 and L2_3IT_pos_patterns_pattern_1\n", + "Match between L5ET_pos_patterns_pattern_9 and L5ET_pos_patterns_pattern_10\n", + "Reading file modisco_results2/L5IT_modisco_results.h5...\n", + "Match between L5IT_neg_patterns_pattern_0 and Astro_neg_patterns_pattern_0\n", + "Match between L5IT_pos_patterns_pattern_0 and L5ET_pos_patterns_pattern_6\n", + "Match between L5IT_pos_patterns_pattern_1 and L5ET_pos_patterns_pattern_11\n", + "Match between L5IT_pos_patterns_pattern_2 and L5ET_pos_patterns_pattern_10\n", + "Match between L5IT_pos_patterns_pattern_5 and L2_3IT_pos_patterns_pattern_1\n", + "Match between L5IT_pos_patterns_pattern_6 and L2_3IT_pos_patterns_pattern_7\n", + "Reading file modisco_results2/L5_6NP_modisco_results.h5...\n", + "Match between L5_6NP_pos_patterns_pattern_0 and L5IT_pos_patterns_pattern_0\n", + "Match between L5_6NP_pos_patterns_pattern_1 and L5IT_pos_patterns_pattern_0\n", + "Match between L5_6NP_pos_patterns_pattern_2 and L5IT_pos_patterns_pattern_4\n", + "Match between L5_6NP_pos_patterns_pattern_3 and L5_6NP_neg_patterns_pattern_0\n", + "Match between L5_6NP_pos_patterns_pattern_5 and L5ET_pos_patterns_pattern_10\n", + "Match between L5_6NP_pos_patterns_pattern_8 and L2_3IT_pos_patterns_pattern_1\n", + "Match between L5_6NP_pos_patterns_pattern_9 and L2_3IT_pos_patterns_pattern_7\n", + "Reading file modisco_results2/L6CT_modisco_results.h5...\n", + "Match between L6CT_neg_patterns_pattern_0 and L5_6NP_neg_patterns_pattern_3\n", + "Match between L6CT_neg_patterns_pattern_1 and Astro_neg_patterns_pattern_0\n", + "Match between L6CT_pos_patterns_pattern_0 and Astro_pos_patterns_pattern_0\n", + "Match between L6CT_pos_patterns_pattern_1 and L5ET_pos_patterns_pattern_11\n", + "Match between L6CT_pos_patterns_pattern_2 and L5IT_pos_patterns_pattern_0\n", + "Match between L6CT_pos_patterns_pattern_3 and L5IT_pos_patterns_pattern_4\n", + "Match between L6CT_pos_patterns_pattern_6 and L2_3IT_pos_patterns_pattern_1\n", + "Reading file modisco_results2/L6IT_modisco_results.h5...\n", + "Match between L6IT_neg_patterns_pattern_0 and L6CT_neg_patterns_pattern_1\n", + "Match between L6IT_pos_patterns_pattern_0 and L5ET_pos_patterns_pattern_11\n", + "Match between L6IT_pos_patterns_pattern_1 and Astro_pos_patterns_pattern_0\n", + "Match between L6IT_pos_patterns_pattern_2 and L5IT_pos_patterns_pattern_0\n", + "Match between L6IT_pos_patterns_pattern_3 and L5ET_pos_patterns_pattern_2\n", + "Match between L6IT_pos_patterns_pattern_4 and L5IT_pos_patterns_pattern_4\n", + "Match between L6IT_pos_patterns_pattern_6 and L2_3IT_pos_patterns_pattern_1\n", + "Match between L6IT_pos_patterns_pattern_7 and L6CT_pos_patterns_pattern_5\n", + "Reading file modisco_results2/L6b_modisco_results.h5...\n", + "Match between L6b_neg_patterns_pattern_0 and L6CT_neg_patterns_pattern_0\n", + "Match between L6b_neg_patterns_pattern_2 and L6IT_neg_patterns_pattern_1\n", + "Match between L6b_pos_patterns_pattern_0 and Astro_pos_patterns_pattern_0\n", + "Match between L6b_pos_patterns_pattern_1 and L6IT_pos_patterns_pattern_7\n", + "Match between L6b_pos_patterns_pattern_2 and L5_6NP_pos_patterns_pattern_5\n", + "Match between L6b_pos_patterns_pattern_3 and L5IT_pos_patterns_pattern_4\n", + "Match between L6b_pos_patterns_pattern_4 and L2_3IT_pos_patterns_pattern_7\n", + "Match between L6b_pos_patterns_pattern_5 and L5_6NP_pos_patterns_pattern_5\n", + "Match between L6b_pos_patterns_pattern_8 and L5ET_pos_patterns_pattern_11\n", + "Reading file modisco_results2/Lamp5_modisco_results.h5...\n", + "Match between Lamp5_neg_patterns_pattern_1 and L2_3IT_neg_patterns_pattern_4\n", + "Match between Lamp5_neg_patterns_pattern_4 and L6IT_neg_patterns_pattern_0\n", + "Match between Lamp5_pos_patterns_pattern_0 and Astro_pos_patterns_pattern_0\n", + "Match between Lamp5_pos_patterns_pattern_1 and L5_6NP_pos_patterns_pattern_5\n", + "Match between Lamp5_pos_patterns_pattern_2 and L5IT_pos_patterns_pattern_4\n", + "Match between Lamp5_pos_patterns_pattern_4 and Lamp5_pos_patterns_pattern_2\n", + "Match between Lamp5_pos_patterns_pattern_5 and Astro_pos_patterns_pattern_1\n", + "Match between Lamp5_pos_patterns_pattern_6 and Astro_pos_patterns_pattern_1\n", + "Reading file modisco_results2/Micro_PVM_modisco_results.h5...\n", + "Match between Micro_PVM_neg_patterns_pattern_0 and L6IT_neg_patterns_pattern_0\n", + "Match between Micro_PVM_neg_patterns_pattern_1 and L2_3IT_neg_patterns_pattern_0\n", + "Match between Micro_PVM_pos_patterns_pattern_0 and Endo_pos_patterns_pattern_2\n", + "Match between Micro_PVM_pos_patterns_pattern_8 and L5_6NP_pos_patterns_pattern_5\n", + "Reading file modisco_results2/OPC_modisco_results.h5...\n", + "Match between OPC_neg_patterns_pattern_0 and Micro_PVM_neg_patterns_pattern_0\n", + "Match between OPC_pos_patterns_pattern_1 and Astro_pos_patterns_pattern_11\n", + "Match between OPC_pos_patterns_pattern_2 and Astro_pos_patterns_pattern_0\n", + "Match between OPC_pos_patterns_pattern_3 and Astro_pos_patterns_pattern_13\n", + "Match between OPC_pos_patterns_pattern_4 and Astro_pos_patterns_pattern_0\n", + "Match between OPC_pos_patterns_pattern_5 and Astro_pos_patterns_pattern_11\n", + "Match between OPC_pos_patterns_pattern_7 and L5IT_pos_patterns_pattern_0\n", + "Match between OPC_pos_patterns_pattern_8 and Astro_pos_patterns_pattern_5\n", + "Reading file modisco_results2/Oligo_modisco_results.h5...\n", + "Match between Oligo_neg_patterns_pattern_0 and Micro_PVM_neg_patterns_pattern_0\n", + "Match between Oligo_pos_patterns_pattern_0 and OPC_pos_patterns_pattern_0\n", + "Match between Oligo_pos_patterns_pattern_1 and Astro_pos_patterns_pattern_0\n", + "Match between Oligo_pos_patterns_pattern_2 and L5IT_pos_patterns_pattern_0\n", + "Match between Oligo_pos_patterns_pattern_4 and Astro_pos_patterns_pattern_13\n", + "Reading file modisco_results2/Pvalb_modisco_results.h5...\n", + "Match between Pvalb_neg_patterns_pattern_5 and Micro_PVM_neg_patterns_pattern_0\n", + "Match between Pvalb_neg_patterns_pattern_6 and Micro_PVM_neg_patterns_pattern_0\n", + "Match between Pvalb_pos_patterns_pattern_1 and L5_6NP_pos_patterns_pattern_5\n", + "Match between Pvalb_pos_patterns_pattern_10 and Astro_pos_patterns_pattern_10\n", + "Match between Pvalb_pos_patterns_pattern_2 and L5IT_pos_patterns_pattern_0\n", + "Match between Pvalb_pos_patterns_pattern_6 and Astro_pos_patterns_pattern_1\n", + "Match between Pvalb_pos_patterns_pattern_7 and Lamp5_pos_patterns_pattern_4\n", + "Match between Pvalb_pos_patterns_pattern_8 and Pvalb_pos_patterns_pattern_4\n", + "Reading file modisco_results2/Sncg_modisco_results.h5...\n", + "Match between Sncg_neg_patterns_pattern_0 and Micro_PVM_neg_patterns_pattern_0\n", + "Match between Sncg_pos_patterns_pattern_4 and Astro_pos_patterns_pattern_2\n", + "Match between Sncg_pos_patterns_pattern_5 and Astro_pos_patterns_pattern_0\n", + "Reading file modisco_results2/Sst_modisco_results.h5...\n", + "Match between Sst_neg_patterns_pattern_0 and L6CT_neg_patterns_pattern_0\n", + "Match between Sst_neg_patterns_pattern_1 and Micro_PVM_neg_patterns_pattern_0\n", + "Match between Sst_pos_patterns_pattern_0 and L5IT_pos_patterns_pattern_0\n", + "Match between Sst_pos_patterns_pattern_1 and Astro_pos_patterns_pattern_1\n", + "Match between Sst_pos_patterns_pattern_2 and Pvalb_pos_patterns_pattern_9\n", + "Match between Sst_pos_patterns_pattern_3 and Astro_pos_patterns_pattern_2\n", + "Match between Sst_pos_patterns_pattern_4 and L6IT_pos_patterns_pattern_3\n", + "Reading file modisco_results2/SstChodl_modisco_results.h5...\n", + "Match between SstChodl_neg_patterns_pattern_1 and Sst_pos_patterns_pattern_11\n", + "Match between SstChodl_neg_patterns_pattern_3 and L6CT_neg_patterns_pattern_0\n", + "Match between SstChodl_neg_patterns_pattern_4 and SstChodl_neg_patterns_pattern_3\n", + "Match between SstChodl_pos_patterns_pattern_0 and Sst_pos_patterns_pattern_11\n", + "Match between SstChodl_pos_patterns_pattern_1 and Astro_pos_patterns_pattern_13\n", + "Match between SstChodl_pos_patterns_pattern_2 and Sncg_pos_patterns_pattern_0\n", + "Match between SstChodl_pos_patterns_pattern_3 and Sst_pos_patterns_pattern_2\n", + "Match between SstChodl_pos_patterns_pattern_4 and L6IT_pos_patterns_pattern_3\n", + "Match between SstChodl_pos_patterns_pattern_5 and SstChodl_pos_patterns_pattern_0\n", + "Reading file modisco_results2/VLMC_modisco_results.h5...\n", + "Match between VLMC_neg_patterns_pattern_0 and Micro_PVM_neg_patterns_pattern_0\n", + "Match between VLMC_neg_patterns_pattern_4 and L2_3IT_neg_patterns_pattern_0\n", + "Match between VLMC_pos_patterns_pattern_0 and Astro_pos_patterns_pattern_0\n", + "Match between VLMC_pos_patterns_pattern_1 and Endo_pos_patterns_pattern_3\n", + "Match between VLMC_pos_patterns_pattern_3 and Endo_pos_patterns_pattern_1\n", + "Match between VLMC_pos_patterns_pattern_4 and Endo_pos_patterns_pattern_3\n", + "Match between VLMC_pos_patterns_pattern_5 and SstChodl_pos_patterns_pattern_5\n", + "Match between VLMC_pos_patterns_pattern_6 and Endo_pos_patterns_pattern_3\n", + "Reading file modisco_results2/Vip_modisco_results.h5...\n", + "Match between Vip_neg_patterns_pattern_1 and Micro_PVM_neg_patterns_pattern_0\n", + "Match between Vip_pos_patterns_pattern_1 and VLMC_pos_patterns_pattern_0\n", + "Match between Vip_pos_patterns_pattern_2 and Sncg_pos_patterns_pattern_0\n", + "Match between Vip_pos_patterns_pattern_3 and SstChodl_pos_patterns_pattern_5\n", + "Match between Vip_pos_patterns_pattern_4 and SstChodl_pos_patterns_pattern_5\n", + "Merging patterns: VLMC_pos_patterns_pattern_0 and OPC_pos_patterns_pattern_6 with similarity 0.59369957447052\n", + "Merging patterns: Astro_pos_patterns_pattern_1 and SstChodl_pos_patterns_pattern_3 with similarity 0.5669545531272888\n", + "Merging patterns: Astro_pos_patterns_pattern_1 and Sncg_pos_patterns_pattern_0 with similarity 0.6913373470306396\n", + "Merging patterns: Astro_pos_patterns_pattern_10 and Astro_pos_patterns_pattern_6 with similarity 0.6019420027732849\n", + "Merging patterns: OPC_pos_patterns_pattern_5 and SstChodl_pos_patterns_pattern_1 with similarity 0.5549869537353516\n", + "Merging patterns: OPC_pos_patterns_pattern_5 and Astro_pos_patterns_pattern_5 with similarity 0.5727839469909668\n", + "Merging patterns: Astro_pos_patterns_pattern_3 and Oligo_pos_patterns_pattern_0 with similarity 0.7017951607704163\n", + "Merging patterns: Astro_pos_patterns_pattern_7 and OPC_pos_patterns_pattern_10 with similarity 0.6586654186248779\n", + "Merging patterns: L5ET_pos_patterns_pattern_11 and L2_3IT_pos_patterns_pattern_5 with similarity 0.635272204875946\n", + "Merging patterns: L2_3IT_pos_patterns_pattern_2 and L2_3IT_pos_patterns_pattern_7 with similarity 0.583046555519104\n", + "Merging patterns: L2_3IT_pos_patterns_pattern_2 and L2_3IT_pos_patterns_pattern_6 with similarity 0.5981428623199463\n", + "Merging patterns: L2_3IT_pos_patterns_pattern_2 and Lamp5_pos_patterns_pattern_4 with similarity 0.6374804973602295\n", + "Merging patterns: L2_3IT_pos_patterns_pattern_2 and Pvalb_pos_patterns_pattern_4 with similarity 0.699141800403595\n", + "Merging patterns: L5IT_pos_patterns_pattern_0 and Vip_pos_patterns_pattern_4 with similarity 0.7188781499862671\n", + "Merging patterns: SstChodl_neg_patterns_pattern_3 and SstChodl_neg_patterns_pattern_6 with similarity 0.5787173509597778\n", + "Merging patterns: L5_6NP_pos_patterns_pattern_4 and Vip_pos_patterns_pattern_0 with similarity 0.5547376871109009\n", + "Merging patterns: L6CT_neg_patterns_pattern_2 and L6b_neg_patterns_pattern_2 with similarity 0.6674162149429321\n", + "Merging patterns: Micro_PVM_pos_patterns_pattern_2 and Micro_PVM_pos_patterns_pattern_3 with similarity 0.5594419836997986\n", + "Merging patterns: Pvalb_pos_patterns_pattern_0 and SstChodl_pos_patterns_pattern_11 with similarity 0.6141945123672485\n", + "Merging patterns: Pvalb_pos_patterns_pattern_3 and Sst_pos_patterns_pattern_7 with similarity 0.8019014596939087\n", + "Merging patterns: Sncg_pos_patterns_pattern_7 and Vip_pos_patterns_pattern_9 with similarity 0.5833488702774048\n", + "Merging patterns: SstChodl_pos_patterns_pattern_1 and Astro_pos_patterns_pattern_2 with similarity 0.6626706123352051\n", + "Merging patterns: Vip_pos_patterns_pattern_0 and Sncg_pos_patterns_pattern_1 with similarity 0.5737963318824768\n", + "Discarded 1 patterns below IC threshold 0.15 and with a single class instance:\n", + "['Endo_neg_patterns_pattern_1']\n" + ] + }, + { + "data": { + "text/plain": [ + "(19, 194)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matched_files = crested.tl.match_h5_files_to_classes(contribution_dir='modisco_results2', classes=list(adata.obs_names))\n", + "all_patterns = crested.tl.process_patterns(matched_files, sim_threshold=0.55, trim_ic_threshold=0.1, discard_ic_threshold=0.15, verbose=True)\n", + "pattern_matrix = crested.tl.create_pattern_matrix(classes=list(adata.obs_names), all_patterns=all_patterns, normalize=True)\n", + "pattern_matrix.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import crested" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pat_seqs = crested.tl.generate_nucleotide_sequences(all_patterns)\n", + "crested.pl.patterns.create_clustermap(pattern_matrix, list(adata.obs_names), figsize=(25,8), grid=True, pat_seqs=pat_seqs)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/pyproject.toml b/pyproject.toml index b796b44..4145df1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,6 +34,7 @@ dependencies = [ "seaborn", "cmake", "modisco-lite", + "vizsequence" ] [project.optional-dependencies] diff --git a/src/crested/pl/patterns/__init__.py b/src/crested/pl/patterns/__init__.py index 08e7c74..f3a7fae 100644 --- a/src/crested/pl/patterns/__init__.py +++ b/src/crested/pl/patterns/__init__.py @@ -1,2 +1,2 @@ from ._contribution_scores import contribution_scores -from ._modisco_results import modisco_results +from ._modisco_results import modisco_results, create_clustermap diff --git a/src/crested/pl/patterns/_modisco_results.py b/src/crested/pl/patterns/_modisco_results.py index 8d38561..84bf8a1 100644 --- a/src/crested/pl/patterns/_modisco_results.py +++ b/src/crested/pl/patterns/_modisco_results.py @@ -5,6 +5,8 @@ import modiscolite as modisco import numpy as np from loguru import logger +import pandas as pd +import seaborn as sns from crested._logging import log_and_raise from crested.pl._utils import render_plot @@ -290,3 +292,108 @@ def modisco_results( kwargs["height"] = 2 * max_num_patterns render_plot(fig, **kwargs) + +def plot_custom_xticklabels( + ax: plt.Axes, + sequences: List[Tuple[str, np.ndarray]], + col_order: List[int], + fontsize: int = 10, + dy: float = 0.012 +) -> None: + """ + Plot custom x-tick labels with varying letter heights. + + Parameters: + - ax (plt.Axes): The axes object to plot on. + - sequences (list): List of tuples containing sequences and their corresponding heights. + - col_order (list): List of column indices after clustering. + - fontsize (int): Base font size for the letters. + - dy (float): Vertical adjustment factor for letter heights. + """ + ax.set_xticks(np.arange(len(sequences))) + ax.set_xticklabels([]) + ax.tick_params(axis='x', which='both', length=0) + + for i, original_index in enumerate(col_order): + sequence, heights = sequences[original_index] + y_position = -0.02 + for j, (char, height) in enumerate(zip(sequence, heights)): + char_fontsize = height * fontsize + text = ax.text(i, y_position, char, ha='center', va='center', color='black', + transform=ax.get_xaxis_transform(), fontsize=char_fontsize, rotation=270) + renderer = ax.figure.canvas.get_renderer() + char_width = text.get_window_extent(renderer=renderer).width + y_position -= dy + +def create_clustermap( + pattern_matrix: np.ndarray, + classes: List[str], + figsize: Tuple[int, int] = (15, 13), + grid: bool = False, + color_palette: Union[str, List[str]] = "hsv", + cmap: str = 'coolwarm', + center: float = 0, + method: str = 'average', + fig_path: Optional[str] = None, + pat_seqs: Optional[List[Tuple[str, np.ndarray]]] = None, + dy: float = 0.012 +) -> sns.matrix.ClusterGrid: + """ + Create a clustermap from the given pattern matrix and class labels with customizable options. + + Parameters: + - pattern_matrix (np.ndarray): 2D NumPy array containing pattern data. + - classes (list): List of class labels. + - figsize (tuple): Size of the figure. + - grid (bool): Whether to add a grid to the heatmap. + - color_palette (str or list): Color palette for the row colors. + - cmap (str): Colormap for the clustermap. + - center (float): Value at which to center the colormap. + - method (str): Clustering method to use (e.g., 'average', 'single', 'complete'). + - fig_path (str, optional): Path to save the figure. + - pat_seqs (list, optional): List of sequences to use as xticklabels. + - dy (float): Vertical adjustment factor for letter heights. + + Returns: + - sns.matrix.ClusterGrid: The clustermap object. + """ + data = pd.DataFrame(pattern_matrix) + + if isinstance(color_palette, str): + palette = sns.color_palette(color_palette, len(set(classes))) + else: + palette = color_palette + + class_lut = dict(zip(set(classes), palette)) + row_colors = pd.Series(classes).map(class_lut) + + xtick_labels = False if pat_seqs is not None else True + + g = sns.clustermap( + data, + cmap=cmap, + figsize=figsize, + row_colors=row_colors, + yticklabels=classes, + center=center, + xticklabels=xtick_labels, + method=method + ) + col_order = g.dendrogram_col.reordered_ind + + for label in class_lut: + g.ax_col_dendrogram.bar(0, 0, color=class_lut[label], label=label, linewidth=0) + + if grid: + ax = g.ax_heatmap + ax.grid(True, which='both', color='grey', linewidth=0.25) + g.fig.canvas.draw() + + if pat_seqs is not None: + plot_custom_xticklabels(g.ax_heatmap, pat_seqs, col_order, dy=dy) + + if fig_path is not None: + plt.savefig(fig_path) + + plt.show() + return g diff --git a/src/crested/tl/__init__.py b/src/crested/tl/__init__.py index 42f87fd..23ef53d 100644 --- a/src/crested/tl/__init__.py +++ b/src/crested/tl/__init__.py @@ -1,4 +1,4 @@ from . import data, losses, metrics, zoo from ._configs import TaskConfig, default_configs from ._crested import Crested -from ._tfmodisco import tfmodisco +from ._tfmodisco import tfmodisco, match_h5_files_to_classes, process_patterns, create_pattern_matrix, generate_nucleotide_sequences diff --git a/src/crested/tl/_modisco_utils.py b/src/crested/tl/_modisco_utils.py new file mode 100644 index 0000000..59b74c6 --- /dev/null +++ b/src/crested/tl/_modisco_utils.py @@ -0,0 +1,122 @@ +from __future__ import annotations + +import h5py +import html5lib +import modiscolite as modisco +from modiscolite.core import TrackSet, Seqlet, SeqletSet +import numpy as np +import os +import pandas as pd +from sklearn.decomposition import PCA +from typing import Dict, List, Tuple, Callable, Optional, Union +from vizsequence.viz_sequence import * + +def l1(X: np.ndarray) -> np.ndarray: + """ + Normalizes the input array using the L1 norm. + + Parameters: + - X (np.ndarray): Input array. + + Returns: + - np.ndarray: L1 normalized array. + """ + abs_sum = np.sum(np.abs(X)) + return X if abs_sum == 0 else (X / abs_sum) + +def get_2d_data_from_patterns( + pattern: Dict, + transformer: str = 'l1', + include_hypothetical: bool = True +) -> Tuple[np.ndarray, np.ndarray]: + """ + Gets 2D data from patterns using specified transformer. + + Parameters: + - pattern (dict): Dictionary containing pattern data. + - transformer (str): Transformer function to use ('l1' or 'magnitude'). + - include_hypothetical (bool): Whether to include hypothetical contributions. + + Returns: + - tuple: Forward and reverse 2D data arrays. + """ + func = l1 if transformer == 'l1' else magnitude + tracks = ['hypothetical_contribs', 'contrib_scores'] if include_hypothetical else ['contrib_scores'] + + all_fwd_data, all_rev_data = [], [] + snippets = [pattern[track] for track in tracks] + + fwd_data = np.concatenate([func(snippet) for snippet in snippets], axis=1) + rev_data = np.concatenate([func(snippet[::-1, ::-1]) for snippet in snippets], axis=1) + + all_fwd_data.append(fwd_data) + all_rev_data.append(rev_data) + + return np.array(all_fwd_data), np.array(all_rev_data) + + +def pad_pattern(pattern: Dict, pad_len: int = 2) -> Dict: + """ + Pads the pattern with zeros. + + Parameters: + - pattern (dict): Dictionary containing the pattern data. + - pad_len (int): Length of padding. + + Returns: + - dict: Padded pattern. + """ + p0 = pattern.copy() + p0['contrib_scores'] = np.concatenate((np.zeros((pad_len, 4)), p0['contrib_scores'], np.zeros((pad_len, 4)))) + p0['hypothetical_contribs'] = np.concatenate((np.zeros((pad_len, 4)), p0['hypothetical_contribs'], np.zeros((pad_len, 4)))) + return p0 + +def match_score_patterns(a: Dict, b: Dict) -> float: + """ + Computes the match score between two patterns. + + Parameters: + - a (dict): First pattern. + - b (dict): Second pattern. + + Returns: + - float: Match score between the patterns. + """ + a = pad_pattern(a) + fwd_data_A, rev_data_A = get_2d_data_from_patterns(a) + fwd_data_B, rev_data_B = get_2d_data_from_patterns(b) + X = fwd_data_B if fwd_data_B.shape[1] <= fwd_data_A.shape[1] else fwd_data_A + Y = fwd_data_A if fwd_data_B.shape[1] <= fwd_data_A.shape[1] else fwd_data_B + sim_fwd_pattern = np.array(modisco.affinitymat.jaccard(X, Y).squeeze()) + X = fwd_data_B if fwd_data_B.shape[1] <= fwd_data_A.shape[1] else rev_data_A + Y = rev_data_A if fwd_data_B.shape[1] <= fwd_data_A.shape[1] else fwd_data_B + sim_rev_pattern = np.array(modisco.affinitymat.jaccard(X, Y).squeeze()) + + return max(sim_fwd_pattern[0], sim_rev_pattern[0]) + +def read_html_to_dataframe(source: str): + """ + Reads an HTML table from the Modisco report function into a DataFrame. + + Parameters: + - source: str - The URL or file path to the HTML content. + + Returns: + - DataFrame containing the HTML table or an error message if no table is found. + """ + try: + # Attempt to read the HTML content + dfs = pd.read_html(source) + + # Check if any tables were found + if not dfs: + return "No tables found in the HTML content." + + # Return the first DataFrame + return dfs[0] + except ValueError as e: + # Handle the case where no tables are found + return f"Error: {str(e)}" + except Exception as e: + # Handle any other unexpected exceptions + return f"An error occurred: {str(e)}" \ No newline at end of file diff --git a/src/crested/tl/_tfmodisco.py b/src/crested/tl/_tfmodisco.py index cb7c544..6c57d57 100644 --- a/src/crested/tl/_tfmodisco.py +++ b/src/crested/tl/_tfmodisco.py @@ -1,16 +1,24 @@ -"""Code adapted from https://github.com/jmschrei/tfmodisco-lite/blob/main/modisco.""" - from __future__ import annotations import os +from loguru import logger + import anndata -import modiscolite +import h5py +import modiscolite as modisco +from modiscolite.core import TrackSet, Seqlet, SeqletSet import numpy as np +import pandas as pd import re -from loguru import logger +from scipy.cluster.hierarchy import linkage, leaves_list +from sklearn.decomposition import PCA +from typing import Dict, List, Tuple, Callable, Optional, Union +from vizsequence.viz_sequence import * from crested._logging import log_and_raise +from crested.pl.patterns._modisco_results import _trim_pattern_by_ic, _get_ic +from ._modisco_utils import * def _calculate_window_offsets(center: int, window_size: int) -> tuple: @@ -68,6 +76,9 @@ def tfmodisco( ... adata, class_names=["Astro", "Vip"], output_dir="modisco_results" ... ) """ + + """Code adapted from https://github.com/jmschrei/tfmodisco-lite/blob/main/modisco.""" + if not os.path.exists(output_dir): os.makedirs(output_dir) @@ -114,7 +125,7 @@ def tfmodisco( # Check if the modisco results .h5 file does not exist for the class if not os.path.exists(output_file): logger.info(f"Running modisco for class: {class_name}") - pos_patterns, neg_patterns = modiscolite.tfmodisco.TFMoDISco( + pos_patterns, neg_patterns = modisco.tfmodisco.TFMoDISco( hypothetical_contribs=attributions, one_hot=sequences, max_seqlets_per_metacluster=max_seqlets, @@ -125,7 +136,7 @@ def tfmodisco( verbose=verbose, ) - modiscolite.io.save_hdf5(output_file, pos_patterns, neg_patterns, window_size=window) + modisco.io.save_hdf5(output_file, pos_patterns, neg_patterns, window_size=window) # Generate the modisco report if report: @@ -140,3 +151,634 @@ def tfmodisco( except KeyError as e: logger.error(f"Missing data for class: {class_name}, error: {e}") + +def add_pattern_to_dict( + p: Dict[str, np.ndarray], + idx: int, + cell_type: str, + pos_pattern: bool, + all_patterns: Dict +) -> Dict: + """ + Adds a pattern to the dictionary. + + Parameters: + - p (dict): Pattern to add. + - idx (int): Index for the new pattern. + - cell_type (str): Cell type of the pattern. + - pos_pattern (bool): Indicates if the pattern is a positive pattern. + - all_patterns (dict): Dictionary containing all patterns. + + Returns: + - dict: Updated dictionary with the new pattern. + """ + all_patterns[str(idx)] = {} + all_patterns[str(idx)]['pattern'] = p + all_patterns[str(idx)]['ic'] = np.mean(_get_ic(p['contrib_scores'], pos_pattern)) + all_patterns[str(idx)]['classes'] = {} + all_patterns[str(idx)]['classes'][cell_type] = p + return all_patterns + +def match_to_patterns( + p: Dict, + idx: int, + cell_type: str, + pattern_id: str, + pos_pattern: bool, + all_patterns: Dict[str, Dict[str, Union[str, List[float]]]], + sim_threshold: float = 0.5, + ic_threshold: float = 0.15, + verbose: bool = False +) -> Dict: + """ + Matches the pattern to existing patterns and updates the dictionary. + + Parameters: + - p (dict): Pattern to match. + - idx (int): Index of the pattern. + - cell_type (str): Cell type of the pattern. + - pattern_id (str): ID of the pattern. + - pos_pattern (bool): Indicates if the pattern is a positive pattern. + - all_patterns (dict): Dictionary containing all patterns. + - sim_threshold (float): Similarity threshold for matching patterns. + - ic_threshold (float): Information content threshold for matching patterns. + - verbose (bool): Flag to enable verbose output. + + Returns: + - dict: Updated dictionary with matched patterns. + """ + p['id'] = pattern_id + if not all_patterns: + return add_pattern_to_dict(p, 0, cell_type, pos_pattern, all_patterns) + + match = False + match_idx = None + max_sim = 0 + + for pat_idx, pattern in enumerate(all_patterns.keys()): + sim = match_score_patterns(p, all_patterns[pattern]['pattern']) + if sim > sim_threshold: + match = True + if sim > max_sim: + max_sim = sim + match_idx = pat_idx + + if not match: + pattern_idx = len(all_patterns.keys()) + return add_pattern_to_dict(p, pattern_idx, cell_type, pos_pattern, all_patterns) + + if verbose: + print(f'Match between {pattern_id} and {all_patterns[str(match_idx)]["pattern"]["id"]}') + all_patterns[str(match_idx)]['classes'][cell_type] = p + p_ic = np.mean(_get_ic(p['contrib_scores'], pos_pattern)) + if p_ic > all_patterns[str(match_idx)]['ic']: + all_patterns[str(match_idx)]['ic'] = p_ic + all_patterns[str(match_idx)]['pattern'] = p + + return all_patterns + +def post_hoc_merging( + all_patterns: Dict, + sim_threshold: float = 0.5, + ic_discard_threshold: float = 0.15, + verbose: bool = False +) -> Dict: + """ + Double-checks the similarity of all patterns and merges them if they exceed the threshold. + Filters out patterns with IC below the discard threshold at the last step and updates the keys. + + Parameters: + - all_patterns (dict): Dictionary of all patterns with metadata. + - sim_threshold (float): Similarity threshold for merging patterns. + - ic_discard_threshold (float): IC threshold below which patterns are discarded. + - verbose (bool): Flag to enable verbose output of merged patterns. + + Returns: + - dict: Updated patterns after merging and filtering with sequential keys. + """ + pattern_list = list(all_patterns.items()) + + while True: + merged_patterns = {} + new_index = 0 + merged_indices = set() + any_merged = False + + for i, (idx1, pattern1) in enumerate(pattern_list): + if idx1 in merged_indices: + continue + merged_indices.add(idx1) + merged_pattern = pattern1.copy() + + for j, (idx2, pattern2) in enumerate(pattern_list): + if i >= j or idx2 in merged_indices: + continue + + sim = match_score_patterns(pattern1['pattern'], pattern2['pattern']) + if sim > sim_threshold: + merged_indices.add(idx2) + merged_pattern = merge_patterns(merged_pattern, pattern2) + any_merged = True + if verbose: + print(f'Merging patterns: {pattern1["pattern"]["id"]} and {pattern2["pattern"]["id"]} with similarity {sim}') + + merged_patterns[str(new_index)] = merged_pattern + new_index += 1 + + if not any_merged: + break + + pattern_list = list(merged_patterns.items()) + + # Final filtering based on IC discard threshold and class count + filtered_patterns = {} + discarded_ids = [] + + for k, v in merged_patterns.items(): + if v['ic'] >= ic_discard_threshold or len(v['classes']) > 1: + filtered_patterns[k] = v + else: + discarded_ids.append(v['pattern']['id']) + + if verbose: + discarded_count = len(merged_patterns) - len(filtered_patterns) + print(f'Discarded {discarded_count} patterns below IC threshold {ic_discard_threshold} and with a single class instance:') + print(discarded_ids) + + # Reindex the filtered patterns + final_patterns = {str(new_idx): v for new_idx, (k, v) in enumerate(filtered_patterns.items())} + + return final_patterns + +def merge_patterns( + pattern1: Dict, + pattern2: Dict +) -> Dict: + """ + Merges two patterns into one. The resulting pattern will have the highest IC pattern as the representative pattern. + + Parameters: + - pattern1 (dict): First pattern with metadata. + - pattern2 (dict): Second pattern with metadata. + + Returns: + - dict: Merged pattern with updated metadata. + """ + merged_classes = {**pattern1['classes'], **pattern2['classes']} + + if pattern2['ic'] > pattern1['ic']: + representative_pattern = pattern2['pattern'] + highest_ic = pattern2['ic'] + else: + representative_pattern = pattern1['pattern'] + highest_ic = pattern1['ic'] + + return { + 'pattern': representative_pattern, + 'ic': highest_ic, + 'classes': merged_classes + } + +def pattern_similarity( + all_patterns: Dict, + idx1: int, + idx2: int, + plot: bool = False +) -> float: + """ + Computes the similarity between two patterns. + + Parameters: + - all_patterns (dict): Dictionary containing all patterns. + - idx1 (int): Index of the first pattern. + - idx2 (int): Index of the second pattern. + - plot (bool): Whether to plot the patterns. + + Returns: + - float: Similarity score between the two patterns. + """ + sim = max( + match_score_patterns(all_patterns[str(idx1)]['pattern'], all_patterns[str(idx2)]['pattern']), + match_score_patterns(all_patterns[str(idx2)]['pattern'], all_patterns[str(idx1)]['pattern']) + ) + if plot: + plot_patterns(all_patterns, [idx1, idx2]) + return sim + +def normalize_rows(arr: np.ndarray) -> np.ndarray: + """ + Normalize the rows of an array such that the positive values are scaled by their maximum + and negative values by their minimum absolute value. + + Parameters: + - arr (np.ndarray): Input array to be normalized. + + Returns: + - np.ndarray: The row-normalized array. + """ + normalized_array = np.zeros_like(arr) + + for i in range(arr.shape[0]): + pos_values = arr[i, arr[i] > 0] + neg_values = arr[i, arr[i] < 0] + + if pos_values.size > 0: + max_pos = np.max(pos_values) + normalized_array[i, arr[i] > 0] = pos_values / max_pos + + if neg_values.size > 0: + min_neg = np.min(neg_values) + normalized_array[i, arr[i] < 0] = neg_values / abs(min_neg) + + return normalized_array + +def find_pattern(id_: str, pattern_dict: Dict) -> Union[int, None]: + """ + Finds the index of a pattern by its ID. + + Parameters: + - id_ (str): The ID of the pattern to find. + - pattern_dict (dict): A dictionary containing pattern data. + + Returns: + - int or None: The index of the pattern if found, otherwise None. + """ + for idx, p in enumerate(pattern_dict): + if id_ == pattern_dict[p]['pattern']['id']: + return idx + for c in pattern_dict[p]['classes']: + if id_ == pattern_dict[p]['classes'][c]['id']: + return idx + return None + +def match_h5_files_to_classes(contribution_dir: str, classes: List[str]) -> Dict[str, Optional[str]]: + """ + Matches .h5 files in a given directory with a list of class names and returns a dictionary mapping. + + Parameters: + - contribution_dir (str): Directory containing .h5 files. + - classes (list): List of class names to match against file names. + + Returns: + - dict: A dictionary where keys are class names and values are paths to the corresponding .h5 files if matched, None otherwise. + """ + h5_files = [file for file in os.listdir(contribution_dir) if file.endswith('.h5')] + matched_files = {class_name: None for class_name in classes} + + for file in h5_files: + base_name = os.path.splitext(file)[0][:-16] + for class_name in classes: + if base_name == class_name: + matched_files[class_name] = os.path.join(contribution_dir, file) + break + + return matched_files + +def process_patterns( + matched_files: Dict[str, Optional[str]], + sim_threshold: float = 0.5, + trim_ic_threshold: float = 0.1, + discard_ic_threshold: float = 0.15, + verbose: bool = False +) -> Dict[str, Dict[str, Union[str, List[float]]]]: + """ + Process genomic patterns from matched HDF5 files, trim based on information content, and match to known patterns. + + Parameters: + - matched_files (dict): Dictionary with class names as keys and paths to HDF5 files as values. + - sim_threshold (float): Similarity threshold for matching patterns. + - trim_ic_threshold (float): Information content threshold for trimming patterns. + - discard_ic_threshold (float): Information content threshold for discarding patterns. + - verbose (bool): Flag to enable verbose output. + + Returns: + - dict: All processed patterns with metadata. + """ + all_patterns = {} + counter = 0 + + for ct_idx, cell_type in enumerate(matched_files.keys()): + if verbose: + print(f'Reading file {matched_files[cell_type]}...') + if matched_files[cell_type] is None: + continue + try: + hdf5_results = h5py.File(matched_files[cell_type], "r") + except: + print(f'File error at {matched_files[cell_type]}') + continue + + metacluster_names = list(hdf5_results.keys()) + patterns = [] + pattern_ids = [] + pos_patterns = [] + + for metacluster_name in metacluster_names: + for p in hdf5_results[metacluster_name]: + pattern_ids.append(f"{cell_type.replace(' ', '_')}_{metacluster_name}_{p}") + patterns.append(hdf5_results[metacluster_name][p]) + pos_pat = metacluster_name == 'pos_patterns' + pos_patterns.append(pos_pat) + + trimmed_patterns = [ + _trim_pattern_by_ic(pattern, pos_pattern, trim_ic_threshold) for pattern, pos_pattern in zip(patterns, pos_patterns) + ] + + for idx, p in enumerate(trimmed_patterns): + all_patterns = match_to_patterns(p, idx, cell_type, pattern_ids[idx], pos_patterns[idx], all_patterns, sim_threshold, discard_ic_threshold, verbose) + + all_patterns = post_hoc_merging(all_patterns=all_patterns, sim_threshold=sim_threshold, ic_discard_threshold=discard_ic_threshold, verbose=verbose) + + return all_patterns + +def create_pattern_matrix( + classes: List[str], + all_patterns: Dict[str, Dict[str, Union[str, List[float]]]], + normalize: bool = False +) -> np.ndarray: + """ + Create a pattern matrix from classes and patterns, with optional normalization. + + Parameters: + - classes (list): List of class labels. + - all_patterns (dict): Dictionary containing pattern data. + - normalize (bool): Flag to indicate whether to normalize the rows of the matrix. + + Returns: + - np.ndarray: The resulting pattern matrix, optionally normalized. + """ + pattern_matrix = np.zeros((len(classes), len(all_patterns.keys()))) + + for p_idx in all_patterns: + p_classes = list(all_patterns[p_idx]['classes'].keys()) + for ct in p_classes: + idx = np.argwhere(np.array(classes) == ct)[0][0] + pattern_matrix[idx, int(p_idx)] = np.mean(all_patterns[p_idx]['classes'][ct]['contrib_scores']) + + # Filter out columns that are all zeros + filtered_array = pattern_matrix[:, ~np.all(pattern_matrix == 0, axis=0)] + + if normalize: + filtered_array = normalize_rows(filtered_array) + + return filtered_array + +def generate_nucleotide_sequences(all_patterns: Dict) -> List[Tuple[str, np.ndarray]]: + """ + Generate nucleotide sequences from pattern data. + + Parameters: + - all_patterns (dict): Dictionary containing pattern data. + + Returns: + - list: List of tuples containing sequences and their normalized heights. + """ + nucleotide_map = {0: 'A', 1: 'C', 2: 'G', 3: 'T'} + pat_seqs = [] + + for p in all_patterns: + c = np.abs(all_patterns[p]['pattern']['contrib_scores']) + max_indices = np.argmax(c, axis=1) + max_values = np.max(c, axis=1) + max_height = np.max(max_values) + normalized_heights = max_values / max_height if max_height != 0 else max_values + sequence = ''.join([nucleotide_map[idx] for idx in max_indices]) + prefix = p + ':' + sequence = prefix + sequence + normalized_heights = np.concatenate((np.ones(len(prefix)), normalized_heights)) + pat_seqs.append((sequence, normalized_heights)) + + return pat_seqs + +def generate_image_paths( + pattern_matrix: np.ndarray, + all_patterns: Dict, + classes: List[str], + contribution_dir: str +) -> List[str]: + """ + Generate image paths for each pattern in the filtered array. + + Parameters: + - pattern_matrix (ndarray): Filtered 2D array of pattern data. + - all_patterns (dict): Dictionary containing pattern data. + - classes (list): List of class labels. + - contribution_dir (str): Directory containing contribution scores and images. + + Returns: + - list: List of image paths corresponding to the patterns. + """ + image_paths = [] + + for i in range(pattern_matrix.shape[1]): + pattern_id = all_patterns[str(i)]['pattern']['id'] + pattern_class_parts = pattern_id.split('_')[:-4] + pattern_class = '_'.join(pattern_class_parts) if len(pattern_class_parts) > 1 else pattern_class_parts[0] + + id_split = pattern_id.split('_') + pos_neg = 'pos_patterns.' if id_split[-4] == 'pos' else 'neg_patterns.' + im_dir = contribution_dir + im_path = f"{im_dir}{pattern_class}_report/trimmed_logos/{pos_neg}pattern_{id_split[-1]}.cwm.fwd.png" + image_paths.append(im_path) + + return image_paths + +def generate_html_paths( + all_patterns: Dict, + classes: List[str], + contribution_dir: str +) -> List[str]: + """ + Generate html paths for each pattern in the filtered array. + + Parameters: + - pattern_matrix (ndarray): Filtered 2D array of pattern data. + - all_patterns (dict): Dictionary containing pattern data. + - classes (list): List of class labels. + - contribution_dir (str): Directory containing contribution scores and images. + + Returns: + - list: List of image paths corresponding to the patterns. + """ + html_paths = [] + + for i, p_idx in enumerate(all_patterns): + pattern_id = all_patterns[str(i)]['pattern']['id'] + pattern_class_parts = pattern_id.split('_')[:-4] + pattern_class = '_'.join(pattern_class_parts) if len(pattern_class_parts) > 1 else pattern_class_parts[0] + + html_dir = os.path.join(contribution_dir, pattern_class+'_report') + html_paths.append(os.path.join(html_dir, 'motifs.html')) + + return html_paths + + +def find_pattern_matches( + all_patterns: Dict, + html_paths: List[str], + q_val_thr: float = 0.05 +) -> Dict[int, Dict[str, List[str]]]: + """ + Finds and filters pattern matches from the modisco-lite list of patterns to the motif database from the corresponding HTML paths. + + Parameters: + - all_patterns (Dict): A dictionary of patterns with metadata. + - html_paths (List[str]): A list of file paths to HTML files containing motif databases. + - q_val_thr (float): The threshold for q-value filtering. Default is 0.05. + + Returns: + - Dict[int, Dict[str, List[str]]]: A dictionary with pattern indices as keys and a dictionary of matches as values. + """ + + pattern_match_dict: Dict[int, Dict[str, List[str]]] = {} + + for i, p_idx in enumerate(all_patterns): + df_motif_database = read_html_to_dataframe(html_paths[i]) + pattern_id = all_patterns[p_idx]['pattern']['id'] + pattern_id_parts = pattern_id.split('_') + pattern_id = pattern_id_parts[-4] + '_' + pattern_id_parts[-3] + '.' + pattern_id_parts[-2] + '_' + pattern_id_parts[-1] + matching_row = df_motif_database.loc[df_motif_database['pattern'] == pattern_id] + + # Process the matching row if found + if not matching_row.empty: + matches = [] + for j in range(3): + qval_column = f'qval{j}' + match_column = f'match{j}' + if qval_column in matching_row.columns and match_column in matching_row.columns: + qval = matching_row[qval_column].values[0] + if qval < q_val_thr: + match = matching_row[match_column].values[0] + matches.append(match) + + matches_filt = [] + for match in matches: + if match.startswith('metacluster'): + match_parts = match.split('.')[2:] + match = '.'.join(match_parts) + matches_filt.append(match) + + if matches_filt: + pattern_match_dict[p_idx] = {'matches': matches_filt} + else: + print(f"No matching row found for pattern_id '{pattern_id}'") + + return pattern_match_dict + +def read_motif_to_tf_file(file_path: str) -> pd.DataFrame: + """ + Reads a TSV file mapping motifs to transcription factors (TFs) into a DataFrame. + + Parameters: + - file_path (str): The path to the TSV file containing motif to TF mappings. + + Returns: + - pd.DataFrame: A DataFrame containing the motif to TF mappings. + """ + return pd.read_csv(file_path, sep='\t') + +def create_pattern_tf_dict( + pattern_match_dict: Dict, + motif_to_tf_df: pd.DataFrame, + all_patterns: Dict, + cols: List[str] +) -> Tuple[Dict, np.ndarray]: + """ + Creates a dictionary mapping patterns to their associated transcription factors (TFs) and other metadata. + + Parameters: + - pattern_match_dict (Dict): A dictionary with pattern indices and their matches. + - motif_to_tf_df (pd.DataFrame): A DataFrame containing motif to TF mappings. + - all_patterns (List[Dict[str, Any]]): A list of patterns with metadata. + - cols (List[str]): A list of column names to extract TF annotations from. + + Returns: + - Tuple[Dict, np.ndarray]: A tuple containing the pattern to TF mappings dictionary and an array of all unique TFs. + """ + pattern_tf_dict: Dict[int, Dict[str, Any]] = {} + all_tfs: List[str] = [] + + for i, p_idx in enumerate(pattern_match_dict): + matches = pattern_match_dict[p_idx]['matches'] + if len(matches) == 0: + continue + + tf_list: List[List[str]] = [] + for match in matches: + matching_row = motif_to_tf_df.loc[motif_to_tf_df['Motif_name'] == match] + if not matching_row.empty: + for col in cols: + annot = matching_row[col].values[0] + if not pd.isna(annot): + annot_list = annot.split(', ') + tf_list.append(annot_list) + all_tfs.append(annot_list) + + if len(tf_list) > 0: + # Flatten the list of lists and get unique TFs + tf_list_flat = [item for sublist in tf_list for item in sublist] + unique_tfs = np.unique(tf_list_flat) + + pattern_tf_dict[p_idx] = { + 'pattern_info': all_patterns[p_idx], + 'tfs': unique_tfs, + 'matches': matches + } + + # Flatten all_tfs list and get unique TFs + all_tfs_flat = [item for sublist in all_tfs for item in sublist] + unique_all_tfs = np.sort(np.unique(all_tfs_flat)) + + return pattern_tf_dict, unique_all_tfs + + +def create_tf_ct_matrix( + pattern_tf_dict: Dict, + all_patterns: Dict, + df: pd.DataFrame, + classes: List[str], + log_transform: bool = True, + normalize: bool = True +) -> Tuple[np.ndarray, List[str]]: + """ + Creates a tensor (matrix) of transcription factor (TF) expression and cell type contributions. + + Parameters: + - pattern_tf_dict (Dict[int, Dict[str, Any]]): A dictionary with pattern indices and their TFs. + - all_patterns (List[Dict[str, Any]]): A list of patterns with metadata. + - df (pd.DataFrame): A DataFrame containing gene expression data. + - classes (List[str]): A list of cell type classes. + - log_transform (bool): Whether to apply log transformation to the gene expression values. Default is True. + - normalize (bool): Whether to normalize the contribution scores across the cell types. Default is True. + + Returns: + - Tuple[np.ndarray, List[str]]: A tuple containing the TF-cell type matrix and the list of TF pattern annotations. + """ + total_tf_patterns = sum(len(pattern_tf_dict[p]['tfs']) for p in pattern_tf_dict) + tf_ct_matrix = np.zeros((len(classes), total_tf_patterns, 2)) + tf_pattern_annots = [] + + counter = 0 + for p_idx in pattern_tf_dict: + ct_contribs = np.zeros((len(classes))) + for ct in all_patterns[p_idx]['classes']: + idx = np.argwhere(np.array(classes) == ct)[0][0] + ct_contribs[idx] = np.mean(all_patterns[p_idx]['classes'][ct]['contrib_scores']) + + for tf in pattern_tf_dict[p_idx]['tfs']: + if tf in df.columns: + tf_gex = df[tf].values + if log_transform: + tf_gex = np.log(tf_gex + 1) + + tf_ct_matrix[:, counter, 0] = tf_gex + tf_ct_matrix[:, counter, 1] = ct_contribs + counter += 1 + tf_pattern_annot = tf + '_pattern_' + str(p_idx) + tf_pattern_annots.append(tf_pattern_annot) + + tf_ct_matrix = tf_ct_matrix[:, :len(tf_pattern_annots), :] + if normalize: + tf_ct_matrix[:, :, 1] = normalize_rows(tf_ct_matrix[:, :, 1]) + + return tf_ct_matrix, tf_pattern_annots