
                        
                            import pandas as pd
import numpy as np
import plotly.graph_objects as go
from IPython.display import display, Markdown
import json
import requests
import time
from random import sample
from math import log2
from maayanlab_bioinformatics.dge import characteristic_direction, limma_voom
from maayanlab_bioinformatics.plotting import bridge_plot
import matplotlib.pyplot as plt
from os.path import exists


                        
                            import warnings
warnings.filterwarnings('ignore')


                        
                            # Set KO gene
ko_gene = 'NR0B1'

# Set working directory
l1000_data_dir = '../L1000_data'

# Set parameters 
low_expression_threshold = 0.3


                        
                            try: 
    expr_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv", sep='\t', index_col=0)
except: 
    l1000_data_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_data.tsv", sep='\t')

    l1000_data_list = []
    l1000_meta_list = []
    for row in l1000_data_df.itertuples(): 
        try:
            temp_df = pd.read_csv(row.persistent_id, sep='\t', index_col=0)
        except:
            print(f"Unable to access data from row {row.Index} at {row.persistent_id}")
            continue
        for col in temp_df.columns:
            l1000_meta_list.append([col] + l1000_data_df.loc[row.Index].tolist())
        l1000_data_list.append(temp_df)
    expr_df = pd.concat(l1000_data_list, axis=1)
expr_df.head()


                        
                            if not exists(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv"): 
    expr_df.to_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv", sep='\t', index=True)
else: 
    print(f"File at '{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv' already exists!")

File at '../L1000_data/NR0B1_L1000_CRISPRKO_fulldata.tsv' already exists!


                        
                            try: 
    meta_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv", sep='\t', index_col=0)
except:
    meta_df = pd.DataFrame(l1000_meta_list, columns=['id'] + l1000_data_df.columns.tolist()).set_index('id')
if 'batch' not in meta_df.columns:
    meta_df['batch'] = meta_df.index.map(lambda x: '_'.join(x.split('_')[:3]))
meta_df.head()


                        
                            if not exists(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv"): 
    meta_df.to_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv", sep='\t', index=True)
else: 
    print(f"File at '{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv' already exists!")

File at '../L1000_data/NR0B1_L1000_CRISPRKO_metadata.tsv' already exists!


                        
                            batches = meta_df['batch'].unique()
len(batches)

9


                        
                            ctrl_data_df = pd.read_csv(f"{l1000_data_dir}/L1000_Controls.tsv", sep='\t')
ctrl_data_df = ctrl_data_df[ctrl_data_df['batch'].isin(batches)]
set(batches).difference(ctrl_data_df['batch'])

set()


                        
                            ctrl_data_df.head()


                        
                            try: 
    ctrl_expr_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv", sep='\t', index_col=0)
    ctrl_meta_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv", sep='\t', index_col=0)
except:
    ctrl_data_list = []
    ctrl_meta_list = []
    for row in ctrl_data_df.itertuples():
        try: 
            temp_df = pd.read_csv(row.persistent_id, sep='\t', index_col=0)
        except:
            print(f"Unable to access data from row {row.Index} at {row.persistent_id}")
            continue
        for col in temp_df.columns: 
            ctrl_meta_list.append([col, row.batch])
        ctrl_data_list.append(temp_df)

    ctrl_expr_df = pd.concat(ctrl_data_list, axis=1)
    ctrl_meta_df = pd.DataFrame(ctrl_meta_list, columns=['id', 'batch']).set_index('id')


                        
                            ctrl_expr_df.head()


                        
                            ctrl_meta_df.head()


                        
                            if not exists(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv"): 
    ctrl_expr_df.to_csv(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv", sep='\t', index=True)
else: 
    print(f"File at '{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv' already exists!")

File at '../L1000_data/NR0B1_L1000_Controls_fulldata.tsv' already exists!


                        
                            if not exists(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv"): 
    ctrl_meta_df.to_csv(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv", sep='\t', index=True)
else: 
    print(f"File at '{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv' already exists!")

File at '../L1000_data/NR0B1_L1000_Controls_metadata.tsv' already exists!


                        
                            combined_df = pd.concat([
    expr_df.groupby(expr_df.index).mean(), 
    ctrl_expr_df.groupby(ctrl_expr_df.index).mean()
], axis=1)
combined_df.head()


                        
                            datasets = {
    'metadata': meta_df, 
    'ctrl_metadata': ctrl_meta_df,
    'rawdata': combined_df
}


                        
                            batch_profiles = {x: {'perts': [], 'ctrls': []} for x in batches}
for b in batches: 
    batch_profiles[b]['perts'] = meta_df[meta_df['batch'] == b].index.tolist()
    batch_profiles[b]['ctrls'] = ctrl_meta_df[ctrl_meta_df['batch'] == b].index.tolist()


                        
                            batch_signatures = {'cd': {}, 'limma': {}, 'fc': {}}


                        
                            # Function for computing signatures with characteristic direction
def cd_signature(ctrl_ids, case_ids, dataset, normalization):
  
    signature = characteristic_direction(
        dataset[normalization].loc[:, ctrl_ids], 
        dataset[normalization].loc[:, case_ids], 
        calculate_sig=True
    )
    signature = signature.sort_values("CD-coefficient", ascending=False)
    
    return signature


                        
                            for b in batches: 
    batch_signatures['cd'][b] = cd_signature(
        batch_profiles[b]['ctrls'], 
        batch_profiles[b]['perts'],
        datasets, 
        'rawdata'
    )


                        
                            # Function for computing signatures
def limma(ctrl_ids, case_ids, dataset, normalization):
    
    signature = limma_voom.limma_voom_differential_expression(
        dataset[normalization].loc[:, ctrl_ids],
        dataset[normalization].loc[:, case_ids],
        filter_genes=False
    )
    signature = signature.sort_values("t", ascending=False)

    return signature


                        
                            for b in batches: 
    batch_signatures['limma'][b] = limma(
        batch_profiles[b]['ctrls'], 
        batch_profiles[b]['perts'], 
        datasets, 
        'rawdata'
    )

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                            # Function for computing signatures with fold change
def logFC(ctrl_ids, case_ids, dataset, normalization):

    case_mean = dataset[normalization].loc[:, case_ids].mean(axis=1)
    ctrl_mean = dataset[normalization].loc[:, ctrl_ids].mean(axis=1)
    
    signature = case_mean / ctrl_mean

    signature = signature.apply(lambda x: log2(x+1)).rename('logFC')
    
    return pd.DataFrame(signature.sort_values(ascending=False))


                        
                            for b in batches: 
    batch_signatures['fc'][b] = logFC(
        batch_profiles[b]['ctrls'], 
        batch_profiles[b]['perts'],
        datasets,
        'rawdata'
    )


                        
                            batch_signatures['cd_all']= pd.concat([
    df[['CD-coefficient']].rename(columns={'CD-coefficient': b}) for (b, df) in batch_signatures['cd'].items()
], axis=1).sort_index()
batch_signatures['limma_all'] = pd.concat([
    df[['t']].rename(columns={'t': b}) for (b, df) in batch_signatures['limma'].items()
], axis=1).sort_index()
batch_signatures['fc_all'] = pd.concat([
    df.rename(b) for b, df in batch_signatures['fc'].items()
], axis=1).sort_index()

for k in batch_signatures.keys(): 
    if k.endswith('_all'): 
        method = k.split('_')[0].upper()
        display(Markdown(f"All {method} batch signatures"))
        display(batch_signatures[k])


                        
                            # Function to get Enrichr Results
def Enrichr_API(enrichr_gene_list, library_name, signame, method, direction):

    all_ranks = []
    all_terms = []
    all_pvalues =[] 
    all_adjusted_pvalues = []

    ENRICHR_URL = 'https://maayanlab.cloud/Enrichr/addList'
    genes_str = '\n'.join(enrichr_gene_list)
    description = f'Dex benchmark {signame} {method} {direction} genes'
    payload = {
        'list': (None, genes_str),
        'description': (None, description)
    }

    response = requests.post(ENRICHR_URL, files=payload)
    if not response.ok:
        raise Exception('Error uploading gene list')

    list_data = json.loads(response.text)
    time.sleep(0.5)
    ENRICHR_URL = 'https://maayanlab.cloud/Enrichr/enrich'
    response = requests.get(
        ENRICHR_URL + f"?userListId={list_data['userListId']}&backgroundType={library_name}"
    )
    if not response.ok:
        raise Exception('Error fetching enrichment results')

    enrich_data = json.loads(response.text)
    results_df  = pd.DataFrame(enrich_data[library_name])
    all_ranks.append(list(results_df[0]))
    all_terms.append(list(results_df[1]))
    all_pvalues.append(list(results_df[2]))
    all_adjusted_pvalues.append(list(results_df[6]))

    res_dict = {
        'ranks': all_ranks, 
        'terms': all_terms, 
        'pvals': all_pvalues,
        'adjpvals': all_adjusted_pvalues, 
        'shortid': str(list_data['shortId'])
    }

    return res_dict


                        
                            # Get gene lists to put into Enrichr
gene_lists = {}
for m in batch_signatures.keys():
    if 'all' in m:
        mname = m.split('_')[0]
        gene_lists[mname] = {'up': {}, 'down': {}, 'combined': {}}
        for sig in batch_signatures[m]: 
            gene_lists[mname]['up'][sig] = batch_signatures[m][sig].sort_values(ascending=False).index.tolist()[:250]
            gene_lists[mname]['down'][sig] = batch_signatures[m][sig].sort_values(ascending=True).index.tolist()[:250]
            gene_lists[mname]['combined'][sig] = gene_lists[mname]['up'][sig] + gene_lists[mname]['down'][sig]


                        
                            # Get results
results = {}
for m in gene_lists.keys():
    results[m] = {'up': {}, 'down': {}, 'combined': {}}
    for d in gene_lists[m].keys():
        for sig in gene_lists[m][d].keys():
            results[m][d][sig]= Enrichr_API(
                gene_lists[m][d][sig], 'ChEA_2022', sig, m, d
            )


                        
                            # Extract dexamethasone target rankings
# Initialize lists for storing target information
ranks = []


                        
                            display(Markdown(f"## {ko_gene} Term Rankings"))


                        
                            for m in results.keys(): 
    for d in results[m].keys():
        for sig in results[m][d].keys():
            for i in range(len(results[m][d][sig]['ranks'][0])): 
                if ko_gene in results[m][d][sig]['terms'][0][i]: 
                    ranks.append([
                        f"{sig}:{m}:{d}",
                        results[m][d][sig]['terms'][0][i], 
                        results[m][d][sig]['ranks'][0][i],
                        results[m][d][sig]['pvals'][0][i]
                    ])
full_df = pd.DataFrame(ranks, columns=['Gene_Set', 'Term', 'Rank', 'p-value'])
full_df['Method'] = full_df['Gene_Set'].apply(lambda x: x.split(':')[1])
full_df['Direction'] = full_df['Gene_Set'].apply(lambda x : x.split(':')[2])
full_df['Method_Direction'] = full_df.apply(lambda row: row['Method'] + ':' + row['Direction'], axis=1)


                        
                            up_df = full_df[full_df['Direction']=='up']
down_df = full_df[full_df['Direction']=='down']
combined_df = full_df[full_df['Direction']=='combined']


                        
                            display(Markdown(f"Mean rank of {ko_gene} terms from ChEA 2022 for up genes from each method."))
display(up_df.groupby(['Method']).mean(numeric_only=True).sort_values(['Rank', 'Method']).drop(['p-value'], axis=1))
display(Markdown(f"Mean rank of {ko_gene} terms from ChEA 2022 for down genes from each method."))
display(down_df.groupby(['Method']).mean(numeric_only=True).sort_values(['Rank', 'Method']).drop(['p-value'], axis=1))
display(Markdown(f"Mean rank of {ko_gene} terms from ChEA 2022 for combined up and down genes from each method."))
display(combined_df.groupby(['Method']).mean(numeric_only=True).sort_values(['Rank', 'Method']).drop(['p-value'], axis=1))


                        
                            color_dict = {
    'up': 'crimson',
    'down': '#636EFA', 
    'combined': 'gray'
}

fig1 = go.Figure()
for gs in full_df.groupby('Method_Direction').mean().sort_values('Rank').index:
    fig1.add_trace(
        go.Box(
            y=full_df[full_df['Method_Direction']==gs]['Rank'].tolist(),
            name=gs.split(':')[0].upper() + ':' + gs.split(':')[1],
            marker_color=color_dict[gs.split(':')[1]]
        )
    )
fig1.update_layout(
    title_text=f"{ko_gene} Term Rankings for L1000 Gene Sets by Method and Direction",
    xaxis={
        'title': {'text': 'Method:Direction'}, 
    },
    yaxis={
        'title': {'text': 'Rank'}
    },
    showlegend=False
)
fig1.show("png")


                        
                            color_dict = {
    'cd': 'green',
    'limma': '#b76935', 
    'fc': 'purple'
}

fig1 = go.Figure()
for gs in full_df.groupby('Method_Direction').mean().sort_values('Rank').index:
    fig1.add_trace(
        go.Box(
            y=full_df[full_df['Method_Direction']==gs]['Rank'].tolist(),
            name=gs.split(':')[0].upper() + ':' + gs.split(':')[1],
            marker_color=color_dict[gs.split(':')[0]]
        )
    )
fig1.update_layout(
    title_text=f"{ko_gene} Term Rankings for L1000 Gene Sets by Method and Direction",
    xaxis={
        'title': {'text': 'Method:Direction'}, 
    },
    yaxis={
        'title': {'text': 'Rank'}
    },
    showlegend=False
)
fig1.show("png")


                        
                            full_df['Batch'] = full_df['Gene_Set'].apply(lambda x: x.split(':')[0])
fig1 = go.Figure()
for gs in full_df['Batch'].unique():
    fig1.add_trace(
        go.Box(
            y=full_df[full_df['Batch']==gs]['Rank'].tolist(),
            name=gs,
            marker_color='#636EFA'
        )
    )
fig1.update_layout(
    title_text=f"{ko_gene} Term Rankings for L1000 Gene Sets by Batch",
    xaxis={
        'title': {'text': 'Batch'}, 
    },
    yaxis={
        'title': {'text': 'Rank'}
    },
    showlegend=False
)
fig1.show("png")


                        
                            def getEnrichrGeneSets(libname, termlist): 
    term_dict = {}
    liburl = f'https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=json&libraryName={libname}'
    response = requests.get(liburl)
    if not response.ok:
        raise Exception('Error fetching library from Enrichr')
    for term in termlist:
        geneset = response.json()[libname]['terms'][term].keys()
        term_dict[term] = geneset
    return term_dict

target_genesets = getEnrichrGeneSets('ChEA_2022', full_df['Term'].unique())


                        
                            def methodBridgePlot(gsname, method, colname, abs_val=False): 
    geneset = target_genesets[gsname]
    y = []

    for k in batch_signatures[method].keys():
        if abs_val: 
            sig = batch_signatures[method][k][colname].apply(abs)
        else: 
            sig = batch_signatures[method][k][colname]
        select = pd.Series(
            [x.upper() in geneset for x in sig.sort_values(ascending=False).index.tolist()]
        )   
        x, y_temp = bridge_plot(select)
        y.append(y_temp)
    
    x = x/len(x)
    return x, np.mean(y, axis=0)

def randomBridgePlot(gsname): 
    all_x = []
    all_y = []

    for _ in range(10):
        rand_sig = sample(
            batch_signatures['cd_all'].index.tolist(), 
            batch_signatures['cd_all'].shape[0]
        )
        select = pd.Series([
            x.upper() in target_genesets[gsname] for x in rand_sig
        ])
        x, y = bridge_plot(select)
        x = x/len(x)
        all_x.append(x)
        all_y.append(y)

    return all_x, all_y


                        
                            colnames = {
    'cd': 'CD-coefficient', 
    'limma': 't', 
    'fc': 'logFC'
}


                        
                            def build_res(abs_val=False):
    res = {}
    for term in target_genesets.keys():
        res[term] = {'random': {'x': [], 'y': []}}
        rand_x, rand_y = randomBridgePlot(term)
        res[term]['random']['x'] += rand_x
        res[term]['random']['y'] += rand_y
        for method in batch_signatures.keys():
            if 'all' not in method:
                temp_x, temp_y = methodBridgePlot(term, method, colnames[method], abs_val=abs_val)
                if method in res[term].keys():
                    res[term][method]['x'].append(temp_x)
                    res[term][method]['y'].append(temp_y)
                else: 
                    res[term][method] = {'x': [temp_x], 'y': [temp_y]}
    return res

method_results = build_res(abs_val=False)
method_results_abs = build_res(abs_val=True)


                        
                            def drawBridgePlot(abs_val=False):
    if abs_val:
        res = method_results_abs
    else: 
        res = method_results
    for term in res.keys():
        for sig in res[term].keys(): 
            term_x = np.mean(res[term][sig]['x'], axis=0)
            term_y = np.mean(res[term][sig]['y'], axis=0)
            if sig == 'random': 
                plt.plot(term_x, term_y, label=sig, color='gray')
            elif sig == 'CD': 
                plt.plot(term_x-(0.01*max(term_x)), term_y, label=sig)
            else:
                plt.plot(term_x, term_y, label=sig)

        plt.axhline(y=0, color='black', linestyle='dashed')
        plt.legend(bbox_to_anchor=(1, 1))
        if abs_val:
            plt.title(f"{term.split(' ')[0]} Target Gene Rankings for Each Method (Abs Val)")
        else:
            plt.title(f"{term.split(' ')[0]} Target Gene Rankings for Each Method")
        plt.show()


                        
                            drawBridgePlot()

	XPR032_A375.311_96H_X1_B38:N14	XPR032_A375.311_96H_X2_B38:N14	XPR032_A375.311_96H_X3_B38:N14	XPR032_A375.311_96H_X1_B38:P20	XPR032_A375.311_96H_X3_B38:P20	XPR032_A549.311_96H_X1_B38:N14	XPR032_A549.311_96H_X2_B38:N14	XPR032_A549.311_96H_X3_B38:N14	XPR032_A549.311_96H_X1_B38:P20	XPR032_A549.311_96H_X2_B38:P20	...	XPR032_U251MG.311_96H_X2_B38:N14	XPR032_U251MG.311_96H_X3_B38:N14	XPR032_U251MG.311_96H_X1_B38:P20	XPR032_U251MG.311_96H_X2_B38:P20	XPR032_U251MG.311_96H_X3_B38:P20	XPR032_YAPC.311_96H_X4.L2_B41:N14	XPR032_YAPC.311_96H_X5.L2_B41:N14	XPR032_YAPC.311_96H_X6.L2_B41:N14	XPR032_YAPC.311_96H_X4.L2_B41:P20	XPR032_YAPC.311_96H_X6.L2_B41:P20
symbol
DDR1	5.255000	5.089600	5.427650	5.166525	5.239900	5.506400	5.4724	5.4069	5.7438	5.462400	...	5.63815	5.825700	6.23110	5.26800	5.916700	6.679475	6.938175	5.278950	7.19605	7.62890
PAX8	4.913075	5.132925	5.074600	4.669800	5.265650	4.016100	4.6268	4.2078	4.2377	4.310450	...	4.42210	4.625200	4.62415	4.53150	5.175500	4.408500	4.002800	4.260275	4.15570	3.35210
GUCA1A	5.193900	5.247600	4.621200	5.694850	5.050300	4.331350	4.3682	4.2829	4.3346	4.207350	...	4.98960	4.619000	4.86720	4.67185	4.904350	5.476200	5.236925	5.535125	5.46450	5.54065
EPHB3	5.674850	6.362250	6.259725	5.560650	6.506450	6.481000	6.4755	6.9908	6.4902	6.621600	...	7.15630	7.442451	7.25270	6.36430	7.246000	7.240800	7.576700	7.964250	7.88650	7.55025
ESRRA	7.840600	7.572600	7.368150	7.538750	7.622751	7.349851	7.5800	7.1017	7.1989	7.012701	...	6.22650	6.905600	6.75130	6.59240	7.017099	8.261250	8.548100	7.938550	8.24955	8.39360

	tissue	disease	cell_line	pert_name	pert_time	pert_type	data_level	creation_time	persistent_id	pert_dose	batch
id
XPR032_A375.311_96H_X1_B38:N14	skin of body	melanoma	A375.311	NR0B1	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR032_A375.311_96H
XPR032_A375.311_96H_X2_B38:N14	skin of body	melanoma	A375.311	NR0B1	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR032_A375.311_96H
XPR032_A375.311_96H_X3_B38:N14	skin of body	melanoma	A375.311	NR0B1	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR032_A375.311_96H
XPR032_A375.311_96H_X1_B38:P20	skin of body	melanoma	A375.311	NR0B1	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR032_A375.311_96H
XPR032_A375.311_96H_X3_B38:P20	skin of body	melanoma	A375.311	NR0B1	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR032_A375.311_96H

	local_id	persistent_id	batch
10777	L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_D10_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR032_A375.311_96H
10778	L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_E14_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR032_A375.311_96H
10779	L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_F21_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR032_A375.311_96H
10780	L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_G09_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR032_A375.311_96H
10781	L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_H16_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR032_A375.311_96H

	XPR032_A375.311_96H_X1_B38:D10	XPR032_A375.311_96H_X2_B38:D10	XPR032_A375.311_96H_X3_B38:D10	XPR032_A375.311_96H_X1_B38:E14	XPR032_A375.311_96H_X2_B38:E14	XPR032_A375.311_96H_X3_B38:E14	XPR032_A375.311_96H_X1_B38:F21	XPR032_A375.311_96H_X2_B38:F21	XPR032_A375.311_96H_X3_B38:F21	XPR032_A375.311_96H_X1_B38:G09	...	XPR032_YAPC.311_96H_X6.L2_B41:I09	XPR032_YAPC.311_96H_X4.L2_B41:K10	XPR032_YAPC.311_96H_X5.L2_B41:K10	XPR032_YAPC.311_96H_X6.L2_B41:K10	XPR032_YAPC.311_96H_X4.L2_B41:K20	XPR032_YAPC.311_96H_X5.L2_B41:K20	XPR032_YAPC.311_96H_X6.L2_B41:K20	XPR032_YAPC.311_96H_X4.L2_B41:P03	XPR032_YAPC.311_96H_X5.L2_B41:P03	XPR032_YAPC.311_96H_X6.L2_B41:P03
symbol
NAT2	8.064151	9.469100	7.601850	8.033700	9.220400	8.24655	8.894925	8.72050	9.17865	8.68495	...	11.370251	10.716150	10.178425	11.328951	9.41990	11.02845	12.07100	11.243151	9.276251	9.758101
ADA	6.507850	6.016400	7.335375	6.141600	5.903550	6.85880	6.393500	6.74075	6.35025	5.83335	...	4.890375	5.205100	3.744300	5.539550	5.21020	4.89420	4.83905	4.656200	4.270800	5.249350
CDH2	4.857850	4.623050	5.011100	4.752725	4.761450	4.68530	5.200500	4.61915	4.61665	4.86035	...	4.382100	4.921475	4.550850	4.611150	4.30870	4.04470	4.89275	5.437850	4.703550	5.628650
AKT3	4.182400	1.805000	4.125150	3.381000	3.420925	4.00585	4.777750	4.22330	6.42225	2.92995	...	0.000000	1.254300	0.000000	0.000000	0.00000	0.00000	0.00000	0.000000	0.000000	0.000000
MED6	5.369700	6.277175	7.007350	6.533900	6.349325	6.65425	5.729750	6.16220	6.22880	6.02475	...	6.200250	6.642900	5.949200	7.149099	6.65355	6.47650	6.49045	7.638025	6.261400	7.256250

	batch
id
XPR032_A375.311_96H_X1_B38:D10	XPR032_A375.311_96H
XPR032_A375.311_96H_X2_B38:D10	XPR032_A375.311_96H
XPR032_A375.311_96H_X3_B38:D10	XPR032_A375.311_96H
XPR032_A375.311_96H_X1_B38:E14	XPR032_A375.311_96H
XPR032_A375.311_96H_X2_B38:E14	XPR032_A375.311_96H

Benchmarking the Recovery of Known Targets from L1000 CRISPR KO Data: Batch Version¶

KO Gene: NR0B1

Load in Data¶

Load in Control Data¶

Normalize Data¶

Combine data and remove duplicate genes¶

Compute Signatures: Batch Perturbations vs. Batch Controls¶

Characteristic Direction¶

Limma¶

Fold Change¶

All signatures¶

Enrichment Analysis Rankings¶

NR0B1 Term Rankings¶

Bridge plots¶

	XPR032_A375.311_96H_X1_B38:N14	XPR032_A375.311_96H_X2_B38:N14	XPR032_A375.311_96H_X3_B38:N14	XPR032_A375.311_96H_X1_B38:P20	XPR032_A375.311_96H_X3_B38:P20	XPR032_A549.311_96H_X1_B38:N14	XPR032_A549.311_96H_X2_B38:N14	XPR032_A549.311_96H_X3_B38:N14	XPR032_A549.311_96H_X1_B38:P20	XPR032_A549.311_96H_X2_B38:P20	...	XPR032_YAPC.311_96H_X6.L2_B41:I09	XPR032_YAPC.311_96H_X4.L2_B41:K10	XPR032_YAPC.311_96H_X5.L2_B41:K10	XPR032_YAPC.311_96H_X6.L2_B41:K10	XPR032_YAPC.311_96H_X4.L2_B41:K20	XPR032_YAPC.311_96H_X5.L2_B41:K20	XPR032_YAPC.311_96H_X6.L2_B41:K20	XPR032_YAPC.311_96H_X4.L2_B41:P03	XPR032_YAPC.311_96H_X5.L2_B41:P03	XPR032_YAPC.311_96H_X6.L2_B41:P03
symbol
A1CF	5.042300	4.88865	4.503100	5.114400	4.19680	5.7100	5.7842	6.03365	6.00650	5.556000	...	11.807100	11.71735	12.049049	11.65135	12.454750	11.67805	11.785749	11.98595	12.474900	12.255699
A2M	8.136351	9.75640	7.550050	9.257950	8.29125	8.0900	8.1192	7.12570	6.50205	6.652599	...	7.871700	7.97915	7.558400	7.86500	7.667699	7.43105	8.419125	8.57765	7.610650	6.937225
A4GALT	5.682650	5.72150	5.574750	5.623775	5.77445	5.9049	5.6885	5.44520	5.58240	5.806400	...	5.152150	5.92100	5.048950	6.11105	5.527200	5.35310	5.177000	5.31725	5.868650	5.505100
A4GNT	5.345350	5.15115	5.101900	5.487600	4.99815	4.7636	4.7672	4.78310	4.81630	4.724250	...	10.035549	11.00025	10.085800	10.36765	10.302500	9.89095	9.880751	10.71460	10.327775	10.306700
AAAS	7.843300	8.01230	7.421226	7.221250	7.34165	7.4766	7.2301	7.06610	7.10430	7.214500	...	6.554150	6.45785	6.389650	6.15305	6.222600	6.66180	6.661350	6.67710	6.641350	6.521500

	XPR032_AGS.311_96H	XPR032_A549.311_96H	XPR032_BICR6.311_96H	XPR032_ES2.311_96H	XPR032_PC3.311B_96H	XPR032_U251MG.311_96H	XPR032_A375.311_96H	XPR032_HT29.311_96H	XPR032_YAPC.311_96H
symbol
A1CF	-0.017783	-0.016047	-0.020645	-0.022145	-0.020482	-0.019187	-0.020061	-0.020211	-0.017267
A2M	0.005073	0.004325	-0.002482	0.001378	0.010943	0.009085	0.004170	0.003904	0.008592
A4GALT	0.001997	-0.001746	0.002162	-0.001375	0.000410	-0.000217	0.000460	0.000720	0.001741
A4GNT	-0.017431	-0.014972	-0.015936	-0.018799	-0.017925	-0.019558	-0.016629	-0.018877	-0.013562
AAAS	0.002736	0.003185	0.001409	0.004325	0.001021	0.003434	0.002492	0.002569	0.003237
...	...	...	...	...	...	...	...	...	...
ZXDB	-0.000282	-0.000234	0.000028	-0.002297	0.001384	-0.000324	-0.000004	-0.000076	-0.001289
ZXDC	-0.006142	-0.010458	-0.003045	-0.003486	-0.004996	-0.008848	-0.000090	0.002266	-0.002561
ZYX	0.012263	0.009031	0.010796	0.016862	0.014329	0.013464	0.014974	0.007596	0.009235
ZZEF1	-0.006011	-0.013921	-0.008275	-0.012102	-0.005447	-0.010448	-0.010300	-0.009300	-0.013139
ZZZ3	0.004252	0.006931	0.005963	0.010460	0.009018	0.005085	0.007604	0.006008	0.003209

	XPR032_AGS.311_96H	XPR032_A549.311_96H	XPR032_BICR6.311_96H	XPR032_ES2.311_96H	XPR032_PC3.311B_96H	XPR032_U251MG.311_96H	XPR032_A375.311_96H	XPR032_HT29.311_96H	XPR032_YAPC.311_96H
A1CF	-31.401609	-50.202253	-92.215935	-57.170962	-40.826071	-40.648398	-43.490817	-52.801217	-32.479343
A2M	4.969393	4.644173	-7.136606	0.107135	10.578271	11.012444	5.799222	4.674300	7.700708
A4GALT	2.188914	-5.635605	10.049661	-2.815912	2.751041	0.346938	-1.500088	-0.499869	4.627847
A4GNT	-52.692189	-41.352716	-79.309756	-68.903881	-53.336398	-57.597987	-43.542568	-60.534034	-30.738910
AAAS	12.600847	14.252838	7.151148	11.757109	1.258617	15.252655	10.579879	7.990256	9.632633
...	...	...	...	...	...	...	...	...	...
ZXDB	-0.078946	-0.587111	3.251433	-2.533632	0.531741	-1.372360	3.515837	2.519761	-2.505127
ZXDC	-6.219017	-16.505548	-9.643654	-5.520325	-8.714572	-23.390602	-2.862318	1.014756	-6.854683
ZYX	22.489053	27.402711	35.119063	21.876607	20.948085	26.892091	22.315721	13.386875	10.346862
ZZEF1	-10.595280	-30.928752	-17.735101	-19.004267	-11.055636	-15.283503	-17.462598	-22.132080	-12.173054
ZZZ3	8.319129	12.323906	19.538155	31.135778	12.643030	12.765400	11.261598	13.941717	5.741134

	XPR032_AGS.311_96H	XPR032_A549.311_96H	XPR032_BICR6.311_96H	XPR032_ES2.311_96H	XPR032_PC3.311B_96H	XPR032_U251MG.311_96H	XPR032_A375.311_96H	XPR032_HT29.311_96H	XPR032_YAPC.311_96H
symbol
A1CF	-1.246737	-1.004192	-1.690429	-1.665790	-1.630342	-1.179333	-1.321012	-1.223911	-1.056337
A2M	0.278585	0.205393	-0.272091	0.000797	0.646290	0.502073	0.319819	0.240769	0.393296
A4GALT	0.068180	-0.146484	0.248284	-0.157615	0.139366	0.004167	-0.065231	-0.021841	0.184802
A4GNT	-1.126377	-1.144015	-1.158369	-1.293424	-1.172730	-1.301800	-1.089542	-1.185840	-0.852393
AAAS	0.240515	0.271056	0.096593	0.260816	0.036636	0.311805	0.278677	0.179783	0.199099
...	...	...	...	...	...	...	...	...	...
ZXDB	-0.003877	-0.017850	0.057785	-0.095970	0.019653	-0.036673	0.108741	0.053650	-0.078633
ZXDC	-0.307716	-0.705302	-0.272967	-0.435002	-0.509541	-0.574439	-0.265492	0.043187	-0.251703
ZYX	0.833233	0.651813	0.741641	1.234107	1.058885	0.964060	1.060643	0.631116	0.554679
ZZEF1	-0.488678	-0.787260	-0.613892	-0.653600	-0.583108	-0.635629	-0.679085	-0.670204	-0.674863
ZZZ3	0.218672	0.434406	0.416559	0.806175	0.467929	0.357918	0.403518	0.355551	0.165524

	Rank
Method
cd	159.888889
fc	233.555556
limma	330.000000

	Rank
Method
cd	562.333333
limma	562.666667
fc	595.111111

	Rank
Method
cd	276.222222
fc	475.666667
limma	517.666667