diff --git a/runs.ipynb b/runs.ipynb index cecb6b01b..38cde8419 100644 --- a/runs.ipynb +++ b/runs.ipynb @@ -14,38 +14,26 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "upload: resources/grn_models/donor_0_default/pearson_causal.csv to s3://openproblems-data/resources/grn/grn_models/donor_0_default/pearson_causal.csv\n", + "delete: s3://openproblems-data/resources/grn/results/d0_hvgs_baseline/ridge.pearson_causal.pearson_causal.prediction.csv\n", + "delete: s3://openproblems-data/resources/grn/results/d0_hvgs_baseline/state.yaml\n", + "delete: s3://openproblems-data/resources/grn/results/d0_hvgs_baseline/ridge.pearson_corr.pearson_corr.prediction.csv\n", + "delete: s3://openproblems-data/resources/grn/results/d0_hvgs_baseline/scores.yaml\n", + "delete: s3://openproblems-data/resources/grn/results/d0_hvgs_baseline/trace.txt\n", + "upload: resources/results/scores/hvg_GB.csv to s3://openproblems-data/resources/grn/results/scores/hvg_GB.csv\n", + "delete: s3://openproblems-data/resources/grn/results/scores/scgen_pearson-GB.csv\n", "upload: resources/results/robustness_analysis/corr/scores_corr.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/corr/scores_corr.csv\n", - "upload: resources/results/robustness_analysis/net-100-scores.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/net-100-scores.csv\n", - "upload: resources/results/robustness_analysis/sign-10-scores.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/sign-10-scores.csv\n", - "upload: resources/results/robustness_analysis/sign-100-scores.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/sign-100-scores.csv\n", - "upload: resources/results/robustness_analysis/net-50-scores.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/net-50-scores.csv\n", - "upload: resources/results/robustness_analysis/sign-50-scores.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/sign-50-scores.csv\n", - "upload: resources/results/robustness_analysis/sign-20-scores.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/sign-20-scores.csv\n", - "upload: resources/results/robustness_analysis/sign-0-scores.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/sign-0-scores.csv\n", - "upload: resources/results/robustness_analysis/tmp/genie3.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/genie3.csv\n", - "upload: resources/results/robustness_analysis/tmp/pearson_corr.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/pearson_corr.csv\n", - "upload: resources/results/robustness_analysis/tmp/negative_control.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/negative_control.csv\n", - "upload: resources/results/robustness_analysis/tmp/positive_control.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/positive_control.csv\n", - "delete: s3://openproblems-data/resources/grn/results/scores/layers/scgen_pearson-GB.csv\n", - "upload: resources/results/robustness_analysis/tmp/collectri.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/collectri.csv\n", - "upload: resources/results/robustness_analysis/tmp/scglue.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/scglue.csv\n", - "upload: resources/results/robustness_analysis/tmp/pearson_causal.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/pearson_causal.csv\n", - "upload: resources/results/robustness_analysis/tmp/ppcor.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/ppcor.csv\n", - "upload: resources/results/robustness_analysis/tmp/portia.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/portia.csv\n", - "upload: resources/results/scores/layers/seurat_lognorm-ridge.csv to s3://openproblems-data/resources/grn/results/scores/layers/seurat_lognorm-ridge.csv\n", - "upload: resources/results/robustness_analysis/corr/corr.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/corr/corr.csv\n", - "upload: resources/results/scores/layers/seurat_pearson-ridge.csv to s3://openproblems-data/resources/grn/results/scores/layers/seurat_pearson-ridge.csv\n", - "upload: resources/results/scores/scgen_pearson-GB.csv to s3://openproblems-data/resources/grn/results/scores/scgen_pearson-GB.csv\n", - "upload: resources/results/robustness_analysis/tmp/scenic.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/scenic.csv\n", - "upload: resources/results/robustness_analysis/tmp/grnboost2.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/grnboost2.csv\n", - "upload: resources/results/robustness_analysis/tmp/celloracle.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/tmp/celloracle.csv\n" + "upload: resources/results/robustness_analysis/corr/scores_causal.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/corr/scores_causal.csv\n", + "delete: s3://openproblems-data/resources/grn/results/d0_hvgs_baseline/ridge.positive_control.positive_control.prediction.csv\n", + "upload: resources/results/robustness_analysis/corr/corr_causal.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/corr/corr_causal.csv\n", + "upload: resources/results/robustness_analysis/corr/corr.csv to s3://openproblems-data/resources/grn/results/robustness_analysis/corr/corr.csv\n" ] } ], @@ -66,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -112,11 +100,11 @@ " \n", " \n", " if models_all is None:\n", - " df_reg1 = extract_data(data, reg='reg1').drop(columns=['Mean'])\n", - " df_reg2 = extract_data(data, reg='reg2').drop(columns=['Mean'])\n", + " df_reg1 = extract_data(data, reg='reg1')\n", + " df_reg2 = extract_data(data, reg='reg2')\n", " else:\n", - " df_reg1 = extract_data(data, reg='reg1').reindex(models_all).drop(columns=['Mean'])\n", - " df_reg2 = extract_data(data, reg='reg2').reindex(models_all).drop(columns=['Mean'])\n", + " df_reg1 = extract_data(data, reg='reg1').reindex(models_all)\n", + " df_reg2 = extract_data(data, reg='reg2').reindex(models_all)\n", " # df_all = pd.concat([df_reg1, df_reg2], axis=1).fillna(0)\n", " # df_all_n = (df_all-df_all.min(axis=0))/(df_all.max(axis=0)-df_all.min(axis=0))\n", " # df_all['Rank'] = df_all_n.mean(axis=1).rank(ascending=False).astype(int)\n", @@ -124,7 +112,73 @@ " return df_all\n", "\n", "\n", - "methods = [ 'collectri', 'negative_control', 'positive_control', 'pearson_corr', 'pearson_causal', 'portia', 'ppcor', 'genie3', 'grnboost2', 'scenic', 'scglue', 'celloracle']" + "methods = [ 'collectri', 'negative_control', 'positive_control', 'pearson_corr', 'portia', 'ppcor', 'genie3', 'grnboost2', 'scenic', 'scglue', 'celloracle']" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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positive_control0.1971290.5788220.5308480.584694
pearson_corr0.2693790.5092970.7504520.542506
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" + ], + "text/plain": [ + " S1 S2 static-theta-0.0 static-theta-0.5\n", + "positive_control 0.197129 0.578822 0.530848 0.584694\n", + "pearson_corr 0.269379 0.509297 0.750452 0.542506" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "process_data(\"d0_hvgs_baseline\")" ] }, { @@ -174,6 +228,45 @@ "# d0_hvgs" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Baseline models" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'write_dir': 'resources/grn_models/d0_hvgs', 'multiomics_rna': 'resources/grn-benchmark/multiomics_rna_d0_hvg.h5ad', 'perturbation_data': 'resources/grn-benchmark/perturbation_data.h5ad', 'tf_all': 'resources/prior/tf_all.csv', 'max_n_links': 50000, 'layer': 'scgen_pearson', 'cell_type_specific': False, 'normalize': False, 'causal': True, 'prediction': 'resources/grn_models/d0_hvgs/pearson_corr.csv'}\n", + "Read data\n", + "Causal subsetting\n", + "Reading input data\n", + "Inferring GRN\n", + "{'write_dir': 'resources/grn_models/d0_hvgs', 'multiomics_rna': 'resources/grn-benchmark/perturbation_data.h5ad', 'perturbation_data': 'resources/grn-benchmark/perturbation_data.h5ad', 'tf_all': 'resources/prior/tf_all.csv', 'max_n_links': 50000, 'layer': 'scgen_pearson', 'cell_type_specific': False, 'normalize': False, 'causal': True, 'prediction': 'resources/grn_models/d0_hvgs/positive_control.csv'}\n", + "Read data\n", + "adata.X is already dense.\n", + "Causal subsetting\n" + ] + } + ], + "source": [ + "!python src/control_methods/script_all.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate scores for one layer" + ] + }, { "cell_type": "code", "execution_count": null, @@ -186,14 +279,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Submitted batch job 7746215\n" + "Submitted batch job 7747147\n" ] } ], @@ -531,314 +624,314 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
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 S1S2static-theta-0.0static-theta-0.5rankS1S2static-theta-0.0static-theta-0.5rank
collectri-0.100238-0.2111820.4893160.51489612collectri-0.100238-0.2111820.4893160.51489612
negative_control-0.043795-0.0455610.4590800.50500211negative_control-0.043795-0.0455610.4590800.50500211
positive_control0.4891470.6771550.6554070.5746082positive_control0.4891470.6771550.6554070.5746082
pearson_corr0.2386640.5146120.5295020.5242327pearson_corr0.2386640.5146120.5295020.5242327
pearson_causal0.3552560.5787530.7413280.5604904pearson_causal0.3552560.5787530.7413280.5604904
portia0.1489410.2272480.4512560.5180488portia0.1489410.2272480.4512560.5180488
ppcor0.0228460.0941070.3966800.50987410ppcor0.0228460.0941070.3966800.50987410
genie30.3726410.4903570.7540730.5765803genie30.3726410.4903570.7540730.5765803
grnboost20.3810320.4598600.7818520.6090751grnboost20.3810320.4598600.7818520.6090751
scenic0.1475530.2146940.6008390.5742946scenic0.1475530.2146940.6008390.5742946
scglue0.0783090.2388590.4486170.5270769scglue0.0783090.2388590.4486170.5270769
celloracle0.2168970.3114510.6395560.5801475celloracle0.2168970.3114510.6395560.5801475
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -2332,31 +2425,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 37, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'reg_type': 'ridge', 'write_dir': 'resources/results/robustness_analysis', 'perturbation_data': 'resources/grn-benchmark/perturbation_data.h5ad', 'cell_type_specific': False, 'normalize': False, 'multiomics_rna': 'resources/grn-benchmark/multiomics_rna_d0_hvg.h5ad', 'tf_all': 'resources/prior/tf_all.csv', 'max_n_links': 50000, 'apply_tf': False, 'subsample': -2, 'verbose': 2, 'binarize': True, 'num_workers': 20, 'consensus': 'resources/prior/consensus-num-regulators.json', 'static_only': True, 'clip_scores': True, 'layer': 'scgen_pearson', 'prediction': 'resources/results/robustness_analysis/corr/corr_causal.csv', 'causal': True}\n", - "Read data\n", - "Causal subsetting\n", - " target source weight\n", - "0 AC107068.2 NFXL1 0.831974\n", - "1 FBN1 NR4A3 0.763036\n", - "2 BIRC3 NR4A3 0.751467\n", - "3 MFSD12 HMG20B 0.747598\n", - "4 STX17-AS1 NR4A3 0.744736\n", - "Traceback (most recent call last):\n", - " File \"/home/jnourisa/projs/ongoing/task_grn_inference/src/robustness_analysis/script_all.py\", line 128, in \n", - " aa\n", - "NameError: name 'aa' is not defined\n" - ] - } - ], + "outputs": [], "source": [ - "!python src/robustness_analysis/script_all.py" + "# !python src/robustness_analysis/script_all.py\n", + "!sbatch scripts/sbatch/robustness_analysis.sh" ] }, { @@ -2368,7 +2442,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -2379,55 +2453,23 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[False True False False False False False False False True False False\n", - " False False False True False False False False False False False False\n", - " False False False False False False False False False False False True\n", - " False False False False False False False False True True False True\n", - " False False False False False True False False False False False False\n", - " False False False False False False True True True False True False\n", - " False False False False False False False False False True False True\n", - " False False True True False False False False False False True False\n", - " False False False False]\n", - "[False True True True True True True True True True False False\n", - " True True True True False False True True True False True True\n", - " True True True False True True False True False True False True\n", - " True False True True False True False True True True False True\n", - " True True True True False True False True False False True True\n", - " True True False True True True True True True True True True\n", - " True True True False True True True True False True True True\n", - " False True True True True False False False True True True True\n", - " True True True False]\n", - "[ True True True True False False True False True True True True\n", - " True True False True True False True True True False False True\n", - " True False False False True True True True True True True True\n", - " True False True True False True True False True True True True\n", - " True True False True True True True True True True True True\n", - " True True True False True True False True True True True True\n", - " True True False True False True False True False True False True\n", - " True False True True True True True True True False True True\n", - " True False True True]\n", - "[False False False True False False False False True False True False\n", - " False False False False False False False False False False False False\n", - " False False False True True False False False False False False True\n", - " False False False False False True False False True False True False\n", - " False False False True False False True True False False False False\n", - " False False False False True False False True False False True False\n", - " False True False False False False False False False False False False\n", - " False False True True False True True False False False False False\n", - " True False False False]\n" + "S1 17\n", + "S2 74\n", + "static-theta-0.0 76\n", + "static-theta-0.5 21\n" ] } ], "source": [ "for col in scores_corr.columns:\n", - " print(scores_corr[col].values np.ndarray: + ratio = [.98, .01, 0.01] + + net = np.random.choice([0, -1, 1], size=((len(gene_names), n_tf)),p=ratio) + net = pd.DataFrame(net, index=gene_names, columns=tfs) + return net + print('Inferring GRN') + net = create_negative_control(gene_names) + + pivoted_net = net.reset_index().melt(id_vars='index', var_name='source', value_name='weight') + + pivoted_net = pivoted_net.rename(columns={'index': 'target'}) + pivoted_net = pivoted_net[pivoted_net['weight'] != 0] + + pivoted_net = process_links(pivoted_net, par) + return pivoted_net diff --git a/src/control_methods/negative_control/script.py b/src/control_methods/negative_control/script.py index 2c62a4f5f..21348b2fd 100644 --- a/src/control_methods/negative_control/script.py +++ b/src/control_methods/negative_control/script.py @@ -2,6 +2,7 @@ import anndata as ad import sys import numpy as np +import sys ## VIASH START par = { @@ -12,37 +13,11 @@ } ## VIASH END print(par) - -def process_links(net, par): - net = net[net.source!=net.target] - net = net.sample(par['max_n_links']) - print(net) - return net - -print('Reading input data') -perturbation_data = ad.read_h5ad(par["perturbation_data"]) -gene_names = perturbation_data.var_names.to_numpy() -tf_all = np.loadtxt(par['tf_all'], dtype=str) - -n_tf = 500 -tfs = tf_all[:n_tf] - -def create_negative_control(gene_names) -> np.ndarray: - ratio = [.98, .01, 0.01] - - net = np.random.choice([0, -1, 1], size=((len(gene_names), n_tf)),p=ratio) - net = pd.DataFrame(net, index=gene_names, columns=tfs) - return net -print('Inferring GRN') -net = create_negative_control(gene_names) - -pivoted_net = net.reset_index().melt(id_vars='index', var_name='source', value_name='weight') - -pivoted_net = pivoted_net.rename(columns={'index': 'target'}) -pivoted_net = pivoted_net[pivoted_net['weight'] != 0] - -pivoted_net = process_links(pivoted_net, par) - -print('Saving') -pivoted_net.to_csv(par["prediction"]) +meta = { + 'resources_dir':'src/control_methods/negative_control' +} +sys.path.append(meta['resources_dir']) +from main import main +prediction = main(par) +prediction.to_csv(par["prediction"]) diff --git a/src/control_methods/positive_control/script.py b/src/control_methods/positive_control/script.py index b3772a432..de07531d0 100644 --- a/src/control_methods/positive_control/script.py +++ b/src/control_methods/positive_control/script.py @@ -25,8 +25,6 @@ print('Create causal corr net') par['causal'] = True par['multiomics_rna'] = par['perturbation_data'] -par['only_hvgs'] = False - net = create_corr_net(par) print('Output GRN') diff --git a/src/control_methods/script_all.py b/src/control_methods/script_all.py new file mode 100644 index 000000000..b0ecd1dab --- /dev/null +++ b/src/control_methods/script_all.py @@ -0,0 +1,47 @@ +import pandas as pd +import anndata as ad +import sys +import numpy as np +import os +import random + +par = { + 'write_dir': "resources/grn_models/d0_hvgs", + "multiomics_rna": "resources/grn-benchmark/multiomics_rna_d0_hvg.h5ad", + + "perturbation_data": "resources/grn-benchmark/perturbation_data.h5ad", + "tf_all": "resources/prior/tf_all.csv", + "max_n_links": 50000, + 'layer': 'scgen_pearson', + 'cell_type_specific': False, + 'normalize': False, + 'causal': True +} + +meta = { + "resources_dir": 'src/control_methods', + "util": 'src/utils' +} +sys.path.append(meta["resources_dir"]) +sys.path.append(meta["util"]) + +os.makedirs(par['write_dir'], exist_ok=True) + +from util import create_corr_net + + +#---- run for pearson_corr +par['prediction'] = f"{par['write_dir']}/pearson_corr.csv" +par['causal'] = True +net = create_corr_net(par) +net.to_csv(par['prediction']) +#---- run for negative control +from negative_control.main import main +par['prediction'] = f"{par['write_dir']}/negative_control.csv" +net = main(par) +net.to_csv(par['prediction']) +#---- run for positive_control +par['multiomics_rna'] = par['perturbation_data'] +par['prediction'] = f"{par['write_dir']}/positive_control.csv" +net = create_corr_net(par) +net.to_csv(par['prediction']) diff --git a/src/metrics/script_all.py b/src/metrics/script_all.py index 289d70235..3d10e6867 100644 --- a/src/metrics/script_all.py +++ b/src/metrics/script_all.py @@ -9,18 +9,14 @@ 'reg_type': 'ridge', 'read_dir': "resources/grn_models/d0_hvgs", 'write_dir': "resources/results/scores", - 'methods': [ 'collectri', 'negative_control', 'positive_control', 'pearson_corr', 'pearson_causal', 'portia', 'ppcor', 'genie3', 'grnboost2', 'scenic', 'scglue', 'celloracle'], - # 'layers': ['lognorm', 'pearson', 'seurat_lognorm', 'seurat_pearson', 'scgen_lognorm', 'scgen_pearson'], - 'layers': ['seurat_lognorm', 'seurat_pearson'], - - # 'layers': ['scgen_pearson'], + 'methods': [ 'collectri', 'negative_control', 'positive_control', 'pearson_corr', 'portia', 'ppcor', 'genie3', 'grnboost2', 'scenic', 'scglue', 'celloracle'], + 'layers': ['lognorm', 'pearson', 'seurat_lognorm', 'seurat_pearson', 'scgen_lognorm', 'scgen_pearson'], "perturbation_data": "resources/grn-benchmark/perturbation_data.h5ad", "tf_all": "resources/prior/tf_all.csv", "max_n_links": 50000, "apply_tf": "true", 'subsample': -2, - 'num_workers': 4, 'verbose': 1, 'binarize': True, 'num_workers': 20, diff --git a/src/robustness_analysis/script_all.py b/src/robustness_analysis/script_all.py index 71071af11..610f94c0c 100644 --- a/src/robustness_analysis/script_all.py +++ b/src/robustness_analysis/script_all.py @@ -11,7 +11,7 @@ 'write_dir': "resources/results/robustness_analysis", 'degrees': [0, 10, 20, 50, 100], 'noise_types': ["net", "sign"], - 'methods': [ 'collectri', 'negative_control', 'positive_control', 'pearson_corr', 'pearson_causal', 'portia', 'ppcor', 'genie3', 'grnboost2', 'scenic', 'scglue', 'celloracle'], + 'methods': [ 'collectri', 'negative_control', 'positive_control', 'pearson_corr', 'portia', 'ppcor', 'genie3', 'grnboost2', 'scenic', 'scglue', 'celloracle'], "perturbation_data": "resources/grn-benchmark/perturbation_data.h5ad",