Tutorial

In this tutorial we demonstrate the use of the CoRegTor tool to find transcription co regulators for a gene from gene expression data.

Objective

The aim of this tutorial is to find potential co-regulators of the gene GFAP by analyzing tissue gene expression data for Frontal cortex in adult brain.

Step 1 : Install and import the CoRegTor package and other dependencies

Using pip, pip install coregtor .

Or poetry install coregtor to add the package as a dependency in your project

# Install coregtor if not already installed, then import it
try:
    import coregtor
    import coregtor.utils as ut
except ImportError:
    %pip install coregtor
    import coregtor

# Additional imports
from pathlib import Path
import pandas as pd

Step 2 : Get data and load it

Let’s gather all the data we require:

• Gene Expression data ge_brain.gct. This file contains tissue gene expression data for the Frontal Cortex (BA9) in an adult brain. The data is downloaded from the GTEx portal

• List of transcription factors human_tf.txt : This file was downloaded from aertslab.org

We load this data in dataframes. Now we are ready to use CoRegTor!

base_path = Path("docs/temp") # UPDATE THIS
data_file_path = Path(base_path/"brain_ge.gct") # UPDATE THIS 
tf_file_path = Path(base_path/"human_tf.txt") # UPDATE THIS
target_gene_name = "GFAP" # the gene we are interested in 

# load data 

ge_data = ut.exp.read_GE_data(file_path=data_file_path) # this is just a utility method you dont have to use this if you already have you gene expression data ready in a dataframe
tf_data = pd.read_csv(tf_file_path, names=["gene_name"], header=None)

ge_data

gene_name	DDX11L1	WASH7P	MIR6859-1	MIR1302-2HG	FAM138A	OR4G4P	OR4G11P	OR4F5	ENSG00000238009	CICP27	...	MT-ND4	MT-TH	MT-TS2	MT-TL2	MT-ND5	MT-ND6	MT-TE	MT-CYB	MT-TT	MT-TP
sample_name
GTEX-1117F-0011-R10b-SM-GI4VE	0.000000	3.57928	0.0	0.093825	0.000000	0.000000	0.028731	0.046554	0.039501	0.058675	...	49762.2	1.177570	2.754330	0.000000	7311.39	4788.56	6.47666	28676.5	3.077750	1.19489
GTEX-111FC-0011-R10a-SM-GIN8G	0.000000	2.32926	0.0	0.025333	0.000000	0.052233	0.031030	0.016759	0.000000	0.031684	...	44692.0	0.953824	0.000000	1.544930	6831.00	5164.36	6.67677	26950.9	1.661970	3.54879
GTEX-117XS-0011-R10b-SM-GIN8Z	0.000000	4.79425	0.0	0.000000	0.046843	0.067977	0.020191	0.043622	0.013880	0.032987	...	39249.9	0.827551	0.967814	1.206360	5603.53	3585.51	6.20663	20794.9	0.432584	2.93902
GTEX-1192W-0011-R10b-SM-GHWOF	0.000000	3.83774	0.0	0.032159	0.045693	0.000000	0.039392	0.053189	0.013539	0.000000	...	50750.5	1.614480	2.832190	1.176750	9433.33	7697.90	12.51220	23405.4	1.265900	3.68601
GTEX-1192X-0011-R10a-SM-DO941	0.040388	1.47233	0.0	0.040318	0.000000	0.000000	0.049385	0.040010	0.050922	0.000000	...	31566.9	2.024070	0.591784	0.983528	4424.64	3568.41	4.55416	14051.5	0.529019	1.54038
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
GTEX-ZVZQ-0011-R10b-SM-51MRT	0.017553	1.91964	0.0	0.070089	0.000000	0.180647	0.064389	0.092738	0.044262	0.043831	...	44939.6	3.078850	1.543150	2.137230	7019.94	6874.29	16.71380	24296.2	1.379490	2.23152
GTEX-ZXG5-0011-R10a-SM-57WDD	0.000000	1.07536	0.0	0.036646	0.000000	0.000000	0.044887	0.084853	0.000000	0.000000	...	62226.7	2.759570	1.613650	4.469730	11407.90	11061.80	15.17770	38732.2	1.442500	1.86677
GTEX-ZYFD-0011-R10a-SM-GPI91	0.000000	2.71020	0.0	0.000000	0.000000	0.000000	0.037432	0.000000	0.000000	0.030577	...	43740.3	0.000000	2.691290	0.745473	6574.39	5241.85	9.20498	24934.3	0.000000	1.55672
GTEX-ZYY3-0011-R10a-SM-GNTAZ	0.015919	3.29538	0.0	0.000000	0.000000	0.065533	0.058395	0.031540	0.066902	0.079502	...	40835.8	1.196680	0.933006	3.101260	6228.45	5626.94	10.37120	20992.5	5.004310	2.83332
GTEX-ZZPT-0011-R10b-SM-GPI8B	0.000000	2.85899	0.0	0.049821	0.000000	0.000000	0.030513	0.000000	0.041949	0.024925	...	40128.3	1.250570	0.000000	1.215350	8300.34	8785.10	28.13790	30362.8	2.614830	3.80689

269 rows × 59033 columns

tf_data

	gene_name
0	ZNF354C
1	KLF12
2	ZNF143
3	ZIC2
4	ZNF274
...	...
1887	ZNF826P
1888	ZNF827
1889	ZNF831
1890	ZRSR2
1891	ZSWIM1

1892 rows × 1 columns

Step 3 : Create Ensemble model

The first step in the process is to generate a random forest ensemble model using the gene expression data that predicts the expression value of the gene “GFAP” based on all other genes.

Since we are interested in identifying potential transcription co regulators, we filter the data to include only transcription factors. We use the create_model_input function for filtering and preparing the input for training. It takes a dataframe t_factors which should have a column gene_name listing the transcription factors to consider. The function outputs a tuple containing 2 data frames: X with the feature genes and Y with the target gene. These can then be passed to the generate_model function.

# first generate the training input for the model
X,Y = coregtor.create_model_input(ge_data,target_gene_name,tf_data)

# use the training data to create a model
model = coregtor.create_model(X,Y,"rf",{"n_jobs":8})

model

RandomForestRegressor(n_jobs=8)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Step 4 : Generating tree paths

The genes on the root node are important. These also serve as potential regulators in other tree based GRN inference methods.

Forest based ensemble methods contains multiple decision tress. We want to analyze the structure of the trees in the model.

For each tree, there exists multiple root to leaf paths. We first enumerate all the paths in all the trees in the model.

all_paths = coregtor.tree_paths(model,X,Y)

all_paths

	tree	source	target	path_length	node1	node2	node3	node4	node5	node6	...	node9	node10	node11	node12	node13	node14	node15	node16	node17	node18
0	0	HMBOX1	GFAP	11	YBX1	SP110	GATA2	DBX2	VENTX	DBX2	...	ZNF284	KLF3	None	None	None	None	None	None	None	None
1	0	HMBOX1	GFAP	13	YBX1	SP110	GATA2	DBX2	VENTX	DBX2	...	RBPJ	EGR3	KLF6	ZBTB41	None	None	None	None	None	None
2	0	HMBOX1	GFAP	11	YBX1	SP110	GATA2	DBX2	VENTX	DBX2	...	VENTX	ADARB1	None	None	None	None	None	None	None	None
3	0	HMBOX1	GFAP	9	YBX1	SP110	ZNF653	VAX2	SOX2	HOXA4	...	None	None	None	None	None	None	None	None	None	None
4	0	HMBOX1	GFAP	9	YBX1	SP110	ZNF653	VAX2	NKX2-8	ZNF595	...	None	None	None	None	None	None	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10972	99	ZNF114	GFAP	6	HDAC1	POU2F1	ZNF345	NFKB2	ZNF16	None	...	None	None	None	None	None	None	None	None	None	None
10973	99	ZNF114	GFAP	9	HDAC1	DLX1	LBX2	DMRT3	HAND1	PAX5	...	None	None	None	None	None	None	None	None	None	None
10974	99	ZNF114	GFAP	7	HDAC1	DLX1	LBX2	TET1	ZFP57	SOHLH2	...	None	None	None	None	None	None	None	None	None	None
10975	99	ZNF114	GFAP	6	HDAC1	POU2F1	ZNF345	NFKB2	MSGN1	None	...	None	None	None	None	None	None	None	None	None	None
10976	99	ZNF114	GFAP	11	HDAC1	DLX1	RBM42	CENPB	LTF	PAX9	...	MEF2B	EGR1	None	None	None	None	None	None	None	None

10977 rows × 22 columns

Step 5 : Generating a set of common sub paths (or the context) for all root nodes

In the table of paths above, we observe many unique genes appear as root nodes. Since we train the decision trees to predict the same target gene, the leaf nodes for all paths are the same.

We can thus consider these paths as potential regulatory links, where genes at the root regulates the target. All the genes at the root become potential regulators or the target. However, note that there are multiple intermediate nodes between the root and the target. Comparing these

We consider the root nodes as potential regulators of the target gene and to find if they are co regulators, we compare how similar the intermediate nodes are in between 2 unique root nodes.

pathset = coregtor.create_context(all_paths)

pathset.keys()

dict_keys(['HMBOX1', 'MEIS1', 'NMRAL1', 'ZNF18', 'NFYB', 'HCLS1', 'IRF9', 'DPRX', 'ANXA1', 'YBX1', 'ZNF485', 'ALX3', 'TBX19', 'TAGLN2', 'ZNF768', 'PHF21A', 'SP110', 'ATOH8', 'FOXB2', 'BRCA1', 'ID1', 'ZSCAN16', 'MXI1', 'EEF1D', 'CXXC1', 'ZNF846', 'ZNF624', 'AIRE', 'FOXO4', 'ZNF577', 'NKX6-2', 'FEZ1', 'ZNF837', 'ZNF114', 'HNRNPUL1', 'BCL6', 'ZBTB17', 'ZNF652', 'ZNF692', 'HDAC1'])

Step 6 : Comparing context of all root nodes with each other

similarity

# transforming the context into a more comparable representation 
gf_histogram = coregtor.transform_context(pathset,method="gene_frequency")

gf_histogram

	SP110	GATA2	DBX2	VENTX	ETS1	ZFP64	ZNF284	OLIG2	ZBTB20	RBPJ	...	ZFP30	AGMAT	ATF2	KLF14	ZBTB41	ZNF773	SETDB1	DBX1	PHTF1	ZNF789
HMBOX1	647	55	79	58	7	1	1	26	26	5	...	0	0	0	0	0	0	0	0	0	0
MEIS1	0	0	0	0	0	0	0	0	5	2	...	0	0	0	0	0	0	0	0	0	0
NMRAL1	69	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
ZNF18	18	0	0	0	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	0	0
NFYB	0	0	0	0	0	1	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
HCLS1	0	0	0	2	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
IRF9	0	0	3	3	0	0	1	0	8	1	...	0	0	0	0	0	0	0	0	0	0
DPRX	135	0	0	54	3	2	0	4	0	0	...	0	0	0	0	0	0	0	0	0	0
ANXA1	0	0	0	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
YBX1	141	27	22	24	3	0	2	0	17	0	...	0	0	0	0	0	0	0	0	0	0
ZNF485	0	7	9	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
ALX3	1	40	0	0	0	0	1	0	2	10	...	0	0	0	0	0	0	0	0	0	0
TBX19	138	0	0	0	3	0	0	0	5	0	...	0	0	0	0	0	0	0	0	0	0
TAGLN2	0	0	7	0	0	0	0	0	1	0	...	0	0	0	0	0	0	0	0	0	0
ZNF768	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
PHF21A	1	0	0	27	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
SP110	3	0	0	0	0	0	0	8	4	0	...	0	0	0	0	0	0	0	0	0	0
ATOH8	0	0	0	26	0	0	0	0	45	0	...	0	0	0	0	0	0	0	0	0	0
FOXB2	75	0	6	3	0	0	0	3	0	0	...	0	0	0	0	0	0	0	0	0	0
BRCA1	14	0	5	0	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	0	0
ID1	72	0	0	0	0	0	0	31	0	0	...	0	0	0	0	0	0	0	0	0	0
ZSCAN16	120	0	0	0	0	0	2	0	0	0	...	0	0	0	0	0	0	0	0	0	0
MXI1	0	0	0	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
EEF1D	0	0	0	0	0	1	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
CXXC1	0	0	0	45	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
ZNF846	133	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
ZNF624	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
AIRE	55	0	0	0	18	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
FOXO4	65	1	9	0	0	0	2	0	0	0	...	0	0	0	0	0	0	0	0	0	0
ZNF577	72	0	21	0	0	0	0	0	9	0	...	0	0	0	0	0	0	0	0	0	0
NKX6-2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
FEZ1	72	0	0	0	0	0	0	0	22	0	...	0	0	0	0	0	0	0	0	0	0
ZNF837	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
ZNF114	56	0	0	0	0	0	0	0	3	0	...	0	0	0	0	0	0	0	0	0	0
HNRNPUL1	0	0	0	18	0	0	0	0	5	0	...	3	1	0	0	0	0	0	0	0	0
BCL6	1	34	0	0	2	0	0	2	1	0	...	1	0	9	2	1	2	2	0	0	0
ZBTB17	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	2	1	0
ZNF652	80	0	0	0	0	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	1
ZNF692	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
HDAC1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

40 rows × 1820 columns

sim_matrix = coregtor.compare_context(gf_histogram,"cosine")

sim_matrix.to_csv("sample_sim.csv",index=False)

Step 7 : Interactive generation of co-regulating gene clusters

# Dendrogram 
ut.plot.dendrogram(sim_matrix)

(<Figure size 1500x900 with 1 Axes>,
 <Axes: title={'center': 'Hierarchical Clustering Dendrogram (average linkage)'}, xlabel='Gene', ylabel='Distance'>,
 array([[ 0.        , 25.        ,  0.30382494,  2.        ],
        [21.        , 40.        ,  0.33554309,  3.        ],
        [17.        , 36.        ,  0.40435524,  2.        ],
        [28.        , 41.        ,  0.42812026,  4.        ],
        [ 9.        , 12.        ,  0.43370955,  2.        ],
        [11.        , 42.        ,  0.47806197,  3.        ],
        [ 1.        , 10.        ,  0.47909859,  2.        ],
        [37.        , 43.        ,  0.4797432 ,  5.        ],
        [ 7.        , 44.        ,  0.50478485,  3.        ],
        [15.        , 45.        ,  0.51685424,  4.        ],
        [ 2.        , 47.        ,  0.52217979,  6.        ],
        [ 3.        ,  4.        ,  0.54517594,  2.        ],
        [13.        , 38.        ,  0.54869611,  2.        ],
        [31.        , 50.        ,  0.57270583,  7.        ],
        [ 8.        , 51.        ,  0.597578  ,  3.        ],
        [20.        , 53.        ,  0.60119813,  8.        ],
        [29.        , 48.        ,  0.60694411,  4.        ],
        [22.        , 49.        ,  0.60943375,  5.        ],
        [18.        , 55.        ,  0.6294707 ,  9.        ],
        [46.        , 54.        ,  0.66667679,  5.        ],
        [30.        , 57.        ,  0.66767775,  6.        ],
        [26.        , 32.        ,  0.67994462,  2.        ],
        [24.        , 60.        ,  0.68286585,  7.        ],
        [56.        , 58.        ,  0.686368  , 13.        ],
        [16.        , 52.        ,  0.72010222,  3.        ],
        [33.        , 63.        ,  0.72538881, 14.        ],
        [27.        , 65.        ,  0.73288874, 15.        ],
        [ 5.        , 59.        ,  0.73338946,  6.        ],
        [35.        , 67.        ,  0.75371378,  7.        ],
        [62.        , 68.        ,  0.77129978, 14.        ],
        [14.        , 64.        ,  0.77281854,  4.        ],
        [19.        , 61.        ,  0.77514362,  3.        ],
        [ 6.        , 71.        ,  0.80443734,  4.        ],
        [66.        , 69.        ,  0.86792962, 29.        ],
        [39.        , 72.        ,  0.87816609,  5.        ],
        [70.        , 73.        ,  0.89315843, 33.        ],
        [34.        , 75.        ,  0.91820863, 34.        ],
        [74.        , 76.        ,  0.92527178, 39.        ],
        [23.        , 77.        ,  0.97238938, 40.        ]]))

# Cophonetic distance 
ut.plot.cophenetic(sim_matrix,methods=["average"])

(<Figure size 1200x500 with 1 Axes>,
 [<Axes: title={'center': 'Average\nCCC = 0.771'}, xlabel='Cophenetic distance', ylabel='Original distance'>],
 {'average': np.float64(0.7708075282302238)},
 np.float64(0.7304958606121552))

# generate results

results = coregtor.identify_coregulators(sim_matrix,target_gene=target_gene_name,distance_threshold=0.70)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[17], line 3
      1 # generate results
----> 3 results = coregtor.identify_coregulators(sim_matrix,target_gene=target_gene_name,distance_threshold=0.70)

File ~/projects/coregtor/src/coregtor/clusters.py:429, in identify_coregulators(comparison_matrix, target_gene, clustering_method, auto_threshold_method, **kwargs)
    422   return {
    423       "model": None,  # SciPy-based (more efficient)
    424       "clusters_df": clusters_df,
    425       "best_cluster": best_cluster
    426   }
    428 # Manual clustering path
--> 429 result = CLUSTERING_METHODS[clustering_method]["method"](
    430     comparison_matrix, target_gene, **kwargs
    431 )
    432 result["best_cluster"] = None
    433 return result

File ~/projects/coregtor/src/coregtor/clusters.py:235, in _identify_coregulators_hierarchical(comparison_matrix, target_gene, n_clusters, distance_threshold, linkage, min_module_size, **kwargs)
    232   clustering_params["distance_threshold"] = distance_threshold
    234 model = AgglomerativeClustering(**clustering_params)
--> 235 labels = model.fit_predict(dist_matrix)
    237 # Build cluster assignments
    238 gene_clusters = pd.DataFrame({
    239     'gene': comparison_matrix.index.astype(str),
    240     'cluster_id': labels
    241 })

File ~/Library/Caches/pypoetry/virtualenvs/coregtor-uBpjneWK-py3.12/lib/python3.12/site-packages/sklearn/cluster/_agglomerative.py:1116, in AgglomerativeClustering.fit_predict(self, X, y)
   1095 def fit_predict(self, X, y=None):
   1096     """Fit and return the result of each sample's clustering assignment.
   1097 
   1098     In addition to fitting, this method also return the result of the
   (...)   1114         Cluster labels.
   1115     """
-> 1116     return super().fit_predict(X, y)

File ~/Library/Caches/pypoetry/virtualenvs/coregtor-uBpjneWK-py3.12/lib/python3.12/site-packages/sklearn/base.py:695, in ClusterMixin.fit_predict(self, X, y, **kwargs)
    672 """
    673 Perform clustering on `X` and returns cluster labels.
    674 
   (...)    691     Cluster labels.
    692 """
    693 # non-optimized default implementation; override when a better
    694 # method is possible for a given clustering algorithm
--> 695 self.fit(X, **kwargs)
    696 return self.labels_

File ~/Library/Caches/pypoetry/virtualenvs/coregtor-uBpjneWK-py3.12/lib/python3.12/site-packages/sklearn/base.py:1365, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1358     estimator._validate_params()
   1360 with config_context(
   1361     skip_parameter_validation=(
   1362         prefer_skip_nested_validation or global_skip_validation
   1363     )
   1364 ):
-> 1365     return fit_method(estimator, *args, **kwargs)

File ~/Library/Caches/pypoetry/virtualenvs/coregtor-uBpjneWK-py3.12/lib/python3.12/site-packages/sklearn/cluster/_agglomerative.py:991, in AgglomerativeClustering.fit(self, X, y)
    973 """Fit the hierarchical clustering from features, or distance matrix.
    974 
    975 Parameters
   (...)    988     Returns the fitted instance.
    989 """
    990 X = validate_data(self, X, ensure_min_samples=2)
--> 991 return self._fit(X)

File ~/Library/Caches/pypoetry/virtualenvs/coregtor-uBpjneWK-py3.12/lib/python3.12/site-packages/sklearn/cluster/_agglomerative.py:1010, in AgglomerativeClustering._fit(self, X)
   1007 memory = check_memory(self.memory)
   1009 if not ((self.n_clusters is None) ^ (self.distance_threshold is None)):
-> 1010     raise ValueError(
   1011         "Exactly one of n_clusters and "
   1012         "distance_threshold has to be set, and the other "
   1013         "needs to be None."
   1014     )
   1016 if self.distance_threshold is not None and not self.compute_full_tree:
   1017     raise ValueError(
   1018         "compute_full_tree must be True if distance_threshold is set."
   1019     )

ValueError: Exactly one of n_clusters and distance_threshold has to be set, and the other needs to be None.

results

((AgglomerativeClustering(distance_threshold=0.7, linkage='average',
                          metric='precomputed', n_clusters=None),
    target_gene                                       gene_cluster  n_genes  \
  0        GFAP  BCL6,DPRX,FOXB2,HMBOX1,IRF9,NR5A2,PHF21A,PRRX1...       16   
  1        GFAP  ALX3,CXXC1,HCLS1,HDAC1,NEUROG2,NKX6-2,NMRAL1,P...       12   
  2        GFAP                              PPP2R3B,ZNF536,ZNF775        3   
  3        GFAP                                         BBX,ZNF814        2   
  4        GFAP                                      UBXN1,ZKSCAN1        2   
  
     cluster_id  
  0           0  
  1           3  
  2           2  
  3           1  
  4           4  ),
 None)

clusters

(AgglomerativeClustering(distance_threshold=0.7, linkage='average',
                         metric='precomputed', n_clusters=None),
   target_gene                                       gene_cluster  n_genes  \
 0        GFAP  BCL6,DPRX,FOXB2,HMBOX1,IRF9,NR5A2,PHF21A,PRRX1...       16   
 1        GFAP  ALX3,CXXC1,HCLS1,HDAC1,NEUROG2,NKX6-2,NMRAL1,P...       12   
 2        GFAP                              PPP2R3B,ZNF536,ZNF775        3   
 3        GFAP                                         BBX,ZNF814        2   
 4        GFAP                                      UBXN1,ZKSCAN1        2   
 
    cluster_id  
 0           0  
 1           3  
 2           2  
 3           1  
 4           4  )

import importlib
importlib.reload(coregtor)

<module 'coregtor' from '/Users/svj/projects/coregtor/src/coregtor/__init__.py'>

results1 = results1[results1["n_genes"]<10]
results1

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[35], line 1
----> 1 results1 = results1[results1["n_genes"]<10]
      2 results1

TypeError: tuple indices must be integers or slices, not str

results2,_ = coregtor.identify_coregulators(sim_matrix,target_gene=target_gene_name,distance_threshold=0.75)
results2

	target_gene	gene_cluster	n_genes	cluster_id
0	GFAP	ALX3,CREB3L2,DPRX,FOXB2,HCLS1,HMBOX1,ID1,IKZF2...	19	0
1	GFAP	CLK1,IRF9,NKX6-2,PIR,RAD21,TBX19,ZNF768,ZNF837	8	3
2	GFAP	POU2AF1,SMC3,ZRSR2	3	2
3	GFAP	MAFK,ZNF577	2	1
4	GFAP	BCL6,MXI1	2	4
5	GFAP	FEZ1,HTATIP2	2	5

results2 = results2[results2["n_genes"]<10]
results2

	target_gene	gene_cluster	n_genes	cluster_id
1	GFAP	CLK1,IRF9,NKX6-2,PIR,RAD21,TBX19,ZNF768,ZNF837	8	3
2	GFAP	POU2AF1,SMC3,ZRSR2	3	2
3	GFAP	MAFK,ZNF577	2	1
4	GFAP	BCL6,MXI1	2	4
5	GFAP	FEZ1,HTATIP2	2	5

Validation of results