Network Analysis¶

To identify which sub-networks i.e. communities, are of interest, we'll calculate the correlation between the networks and the relevant clinical metadata variables

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy
from sklearn.decomposition import PCA
from scipy.stats import pearsonr
from statsmodels.stats.multitest import multipletests
import pickle
import seaborn as sns

import pandas as pd import numpy as np import matplotlib.pyplot as plt import scipy from sklearn.decomposition import PCA from scipy.stats import pearsonr from statsmodels.stats.multitest import multipletests import pickle import seaborn as sns

Read in required data

In [2]:

#Load in the required data
datExpr = pd.read_csv('/ReCoDE-Gene-Network-Analysis/data/data/Bcell_datExpr_pseudobulk.csv', index_col = 0)
metadata = pd.read_csv('/ReCoDE-Gene-Network-Analysis/data/data/Bcell_metadata_pseudobulk.csv', index_col = 0)

#Load in the required data datExpr = pd.read_csv('/ReCoDE-Gene-Network-Analysis/data/data/Bcell_datExpr_pseudobulk.csv', index_col = 0) metadata = pd.read_csv('/ReCoDE-Gene-Network-Analysis/data/data/Bcell_metadata_pseudobulk.csv', index_col = 0)

In [3]:

datExpr

datExpr

Out[3]:

	ISG15	LINC01342	TTLL10-AS1	TNFRSF18	CALML6	CHD5	ICMT-DT	MIR34AHG	RBP7	MTOR-AS1	...	FRMPD3	TSC22D3	KLHL13	AKAP14	RHOXF1-AS1	TMEM255A	SMIM10L2B-AS1	IL9R_ENSG00000124334	DDX3Y	EIF1AY
donor_id
CH-20-001	6.380902	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	53.239480	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	21.632603	17.641195
CH-20-002	12.606751	2.33599	0.000000	0.000000	0.00000	0.000000	1.089918	0.000000	1.158743	1.173824	...	0.000000	112.643970	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	45.432410	22.809190
CH-20-004	12.302510	0.00000	0.000000	21.512184	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	42.873410	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	15.570595	20.173725
CH-20-005	18.603716	1.16925	1.232658	4.975880	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	1.112746	190.337740	0.000000	0.0000	1.191559	0.000000	0.000000	0.000000	6.931139	1.071742
CH-21-002	13.705297	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	44.942260	0.000000	0.0000	0.000000	0.000000	0.000000	1.323198	0.000000	0.000000
CH-21-006	4.377715	0.00000	0.000000	23.782143	0.00000	0.000000	0.000000	0.000000	1.023552	0.000000	...	0.000000	12.741602	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	5.349793	12.981407
CH-21-008	18.058025	0.00000	0.000000	44.614340	0.00000	1.201673	0.000000	0.000000	0.000000	0.000000	...	0.000000	76.893720	0.000000	0.0000	0.000000	0.000000	0.000000	1.080360	1.188176	2.377049
CH-21-013	21.395964	0.00000	0.000000	30.426510	0.00000	0.000000	1.235703	0.000000	0.000000	0.000000	...	0.000000	54.458330	0.000000	0.0000	0.000000	0.000000	1.117969	1.236817	23.543072	53.250420
CH-21-014	13.436963	0.00000	0.000000	11.067089	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	32.248600	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	17.709280	21.636190
CH-21-017	22.916807	0.00000	0.000000	9.076924	0.00000	0.000000	0.000000	0.000000	2.478934	0.000000	...	0.000000	188.600070	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	49.114600	40.454937
CH-21-020	197.794700	0.00000	0.000000	122.788270	0.00000	0.000000	0.000000	0.000000	1.047435	0.000000	...	0.000000	197.616580	0.000000	0.0000	0.000000	0.000000	0.000000	0.765914	88.202680	173.938080
CH-21-021	13.898113	0.00000	0.000000	11.169237	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	20.431047	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	11.017612	21.158054
CH-21-028	7.210576	0.00000	1.066841	1.321003	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	57.428060	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	2.989963	0.000000
CH-21-029	9.007506	0.00000	0.000000	1.928462	1.21185	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	157.941900	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	3.745571	2.051687
CH-21-031	30.211197	0.00000	0.000000	40.325450	0.00000	0.000000	0.000000	2.130981	0.000000	0.000000	...	1.244156	12.550498	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	1.227465	0.906813
CH-21-033	21.972580	0.00000	0.000000	84.504500	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	152.658000	1.167664	1.2505	0.000000	1.199426	0.000000	0.000000	45.661453	142.600740
CH-21-034	54.934030	0.00000	0.000000	147.552780	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.886594	167.975900	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	1.075679
CH-21-036	17.018766	0.00000	0.000000	2.483573	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	88.924920	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	39.051266	10.004631
CH-21-037	150.473450	0.00000	0.000000	53.255013	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	38.325650	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	33.786545	58.778214
CH-21-046	9.337872	0.00000	0.000000	28.949800	0.00000	1.123670	0.000000	0.000000	0.000000	0.000000	...	0.000000	28.600826	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	9.238980	12.119887
CH-21-073	4.982193	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	40.201653	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	27.179262	2.954510
CH-21-074	3.954194	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	1.127058	0.000000	0.000000	...	0.000000	18.354240	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	6.401694	2.069999
CH-21-077	33.969110	0.00000	0.000000	3.333775	0.00000	0.000000	0.000000	0.000000	1.115637	0.000000	...	0.000000	161.007570	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	1.646068	0.000000
CH-21-079	7.030363	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	40.449474	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	18.332941	11.980942

24 rows × 1000 columns

In [4]:

metadata

metadata

Out[4]:

	nCount_RNA	nFeature_RNA	donor_id.1	MUTATION	percent.mt	scType_celltype	tissue_type	cell_type	tissue	development_stage	male	female	CH	normal	DNMT3A	TET2	NoMutation
donor_id
CH-20-001	2490.0	1403	CH-20-001	DNMT3A R882C	6.119578	Naive B cells	tissue	B cell	blood	60	1	0	1	0	1	0	0
CH-20-002	1192.0	629	CH-20-002	DNMT3A R729W (4%), DNMT3A R736C (2%)	3.803975	Naive B cells	tissue	B cell	blood	68	1	0	1	0	1	0	0
CH-20-004	1833.0	985	CH-20-004	TET2 R1516X (30%), TET2 Q659X (29%), SRSF2 P95...	5.335196	Naive B cells	tissue	B cell	blood	85	1	0	1	0	0	1	0
CH-20-005	1966.0	886	CH-20-005	TET2 V1900F (2%)	5.314136	Naive B cells	tissue	B cell	blood	58	0	1	1	0	0	1	0
CH-21-002	1912.0	938	CH-21-002	none	5.657238	Naive B cells	tissue	B cell	blood	48	0	1	0	1	0	0	1
CH-21-006	1356.0	709	CH-21-006	DNMT3A R882H (13%)	5.211849	Naive B cells	tissue	B cell	blood	67	0	1	1	0	1	0	0
CH-21-008	1117.0	575	CH-21-008	none	8.398348	Naive B cells	tissue	B cell	blood	70	0	1	0	1	0	0	1
CH-21-013	1321.0	816	CH-21-013	none	4.663212	Naive B cells	tissue	B cell	blood	73	1	0	0	1	0	0	1
CH-21-014	1064.0	623	CH-21-014	SRSF2 P95R (40%), TET2 L957Ifs*15 (51%)	4.146577	Naive B cells	tissue	B cell	blood	74	1	0	1	0	0	1	0
CH-21-017	1880.0	953	CH-21-017	DNMT3A R882H (20%), IDH2 R140Q (10%), TP53 R27...	6.519922	Naive B cells	tissue	B cell	blood	65	1	0	1	0	1	0	0
CH-21-020	5325.0	2286	CH-21-020	none	5.631046	Naive B cells	tissue	B cell	blood	61	1	0	0	1	0	0	1
CH-21-021	1671.0	943	CH-21-021	none	3.214286	Naive B cells	tissue	B cell	blood	83	1	0	0	1	0	0	1
CH-21-028	1690.0	866	CH-21-028	none	6.053894	Naive B cells	tissue	B cell	blood	89	0	1	0	1	0	0	1
CH-21-029	2180.0	1073	CH-21-029	TET2 G68X (2%)	2.570194	Naive B cells	tissue	B cell	blood	83	0	1	1	0	0	1	0
CH-21-031	1592.0	887	CH-21-031	none	6.734398	Naive B cells	tissue	B cell	blood	78	0	1	0	1	0	0	1
CH-21-033	2219.0	1138	CH-21-033	TET2 (33%)	5.670567	Naive B cells	tissue	B cell	blood	81	1	0	1	0	0	1	0
CH-21-034	2010.0	974	CH-21-034	DNMT3A Q816X (8%)	7.937365	Naive B cells	tissue	B cell	blood	39	0	1	1	0	1	0	0
CH-21-036	2686.0	1337	CH-21-036	DNMT3A splice (7%)	3.909544	Naive B cells	tissue	B cell	blood	91	1	0	1	0	1	0	0
CH-21-037	3546.0	1645	CH-21-037	TET2 (6.2%)	4.473764	Naive B cells	tissue	B cell	blood	71	1	0	1	0	0	1	0
CH-21-046	1918.0	907	CH-21-046	DNMT3A W305X (24%)	4.807084	Naive B cells	tissue	B cell	blood	80	1	0	1	0	1	0	0
CH-21-073	2148.0	1096	CH-21-073	SRSF2 (33%), TET2 Y1245Lfs*22 (27%), TET2 Q742...	5.174489	Naive B cells	tissue	B cell	blood	77	1	0	1	0	0	1	0
CH-21-074	1322.0	708	CH-21-074	TET2 C1378Y (23%)	3.328561	Naive B cells	tissue	B cell	blood	70	1	0	1	0	0	1	0
CH-21-077	1715.0	934	CH-21-077	DNMT3A R749C (9.1%)	6.539510	Naive B cells	tissue	B cell	blood	50	0	1	1	0	1	0	0
CH-21-079	1354.0	793	CH-21-079	DNMT3A M880V (5%)	6.386293	Naive B cells	tissue	B cell	blood	78	1	0	1	0	1	0	0

In [ ]:

In [5]:

gene_names = datExpr.columns
gene_names

gene_names = datExpr.columns gene_names

Out[5]:

Index(['ISG15', 'LINC01342', 'TTLL10-AS1', 'TNFRSF18', 'CALML6', 'CHD5',
       'ICMT-DT', 'MIR34AHG', 'RBP7', 'MTOR-AS1',
       ...
       'FRMPD3', 'TSC22D3', 'KLHL13', 'AKAP14', 'RHOXF1-AS1', 'TMEM255A',
       'SMIM10L2B-AS1', 'IL9R_ENSG00000124334', 'DDX3Y', 'EIF1AY'],
      dtype='object', length=1000)

In [6]:

with open('/ReCoDE-Gene-Network-Analysis/data/other/separated_communities.pkl', 'rb') as file:
        separated_communities = pickle.load(file)

with open('/ReCoDE-Gene-Network-Analysis/data/other/separated_communities.pkl', 'rb') as file: separated_communities = pickle.load(file)

In [7]:

print(separated_communities)

print(separated_communities)

[['NKX6-3', 'ZC2HC1B', 'ZSCAN10', 'HCG9', 'CC2D2B', 'DBX2', 'MOBP', 'STAP2', 'LCN2', 'ZNF462', 'TMEM72-AS1', 'LNCOC1', 'SLC28A2', 'SLC10A5', 'SRRM5', 'SLC17A7', 'OPA1-AS1', 'DEPDC7', 'LIN28A', 'SLC6A3', 'GJB6', 'TMEM255A', 'IGLC4', 'LINC00640', 'DENND6A-AS1', 'FNDC5', 'LAMA2', 'CYYR1', 'CNKSR3', 'RNF182', 'TECTA', 'KRT2', 'PRRT3-AS1', 'LINC02267', 'MUCL3', 'CFAP99', 'CCNA1', 'SLC26A8', 'GNG12', 'PHACTR3', 'LINC01133', 'C21orf62', 'ANKRD29', 'NGFR', 'NKD2', 'GJC2', 'LIM2', 'ANO1', 'LINC00997', 'STARD6', 'ARHGAP23', 'HYDIN', 'KLHL13', 'IGLV5-37', 'PLSCR2', 'TDRP', 'REG1A', 'LINC01832', 'ARHGEF39', 'CDH6', 'LINC01891', 'MGAM', 'AKAP14', 'TDRD1', 'PRLR', 'RARRES2', 'SLC2A4', 'OR2B11', 'DTNA', 'PTGES'], ['ACTN3', 'PDK4-AS1', 'C10orf105', 'ACVR2B-AS1', 'LINC02348', 'DPYD-IT1', 'UBE2Q2P16', 'PRKD3-DT', 'LINC01986', 'CALML6', 'EPHB2', 'PAPPA-AS1', 'DLGAP2', 'MROH8', 'RAB6C', 'ZNF503', 'H3C8', 'LINC02569', 'CDC42EP1', 'TFAP2A', 'ESCO2', 'WNT3A', 'SLC12A3', 'RGPD6', 'ICA1-AS1', 'FOXD3', 'LINC02615', 'FAM174A-DT'], ['IGHV3-32', 'LINC01050', 'STON2', 'CLGN', 'CCK', 'RGL3', 'COPDA1', 'PPP1R9A-AS1', 'SPDYE21', 'BSN', 'PRR15', 'IGKV3D-11', 'C12orf71', 'SMIM17', 'FAM20C', 'KCNQ4', 'PGAM2', 'DAGLA', 'NKX6-2', 'REEP1', 'LGALSL-DT', 'ZNF32-AS2', 'ZSCAN2-AS1', 'TWIST2', 'LINC01503', 'CADM4', 'DBH-AS1'], ['MAG', 'MOCS1', 'ALDH8A1', 'MTOR-AS1', 'MEX3B', 'MYO3B', 'GLP2R', 'SCT', 'TAF1L', 'IGHV3-73', 'OR2AK2', 'PTPRD-AS1', 'ST8SIA5', 'PITPNM2-AS1', 'EPHA2', 'TKTL2', 'SLC5A5', 'PCSK1', 'CUX2', 'IGHV3-69-1', 'ZNF474'], ['LINC02880', 'COL11A2', 'SCARF2', 'ADGRG3', 'ZNF491', 'CFAP141', 'FSTL1', 'TRBJ2-4', 'SMIM10L2B-AS1', 'UBL4B', 'CLEC1B', 'DPYSL4', 'LDHAL6A', 'PRUNE2', 'LINC00200', 'BRME1', 'FAM186B', 'VWA5B1', 'RARRES1', 'LINC01108'], ['SLC1A2-AS1', 'MIR130AHG', 'HEATR4', 'SLC49A3', 'OR6C6', 'SLC4A9', 'IGKV1D-13', 'ACTL7B', 'LINC01823', 'MIPOL1', 'AMPD1', 'IGKV2D-30', 'SLC16A14', 'DEPTOR-AS1', 'EDNRB-AS1', 'H3C12', 'LINC01990', 'ATP8A1-DT', 'TGFB2-AS1'], ['NLRP6', 'PRSS45P', 'NAALADL2', 'VSTM1', 'MYLK3', 'FOXI1', 'PCDHGA3', 'VWA7', 'HDHD5-AS1', 'LINC03021_ENSG00000254319', 'LINC01579', 'IGHV1-14', 'HCG22', 'LINC02055', 'UNC79', 'RNF217-AS1', 'C1orf50-AS1', 'GYS2'], ['CASC9', 'CCDC28A-AS1', 'TRPM1', 'CYP2S1', 'TMPRSS3', 'THTPA', 'GCNT3', 'FREY1', 'RHOXF1-AS1', 'NHS', 'IGHV7-4-1', 'SLITRK5', 'IGLV5-45', 'TRMT9B', 'CFAP57', 'RNF112', 'LINC00654'], ['ASIC3', 'SLC22A1', 'BHLHE22', 'LIF', 'S100A16', 'CT69', 'TRAV29DV5', 'C10orf71', 'TTLL7', 'MS4A4E', 'DNMBP-AS1', 'EDAR'], ['FAM182A', 'PACRG', 'NKX6-1', 'ACTA2-AS1', 'TAS2R39', 'CHRNG', 'LINC02660', 'CDH23'], ['TEKTIP1', 'ASGR2', 'IGKV2-26', 'LINC02057', 'GOLGA8H', 'LRRC49'], ['CYP2C8', 'PELP1-DT', 'LINC01301', 'CBY3', 'PTGR1'], ['KCNH6', 'LINC01980', 'DLG1-AS1', 'IQSEC2', 'LPAR4'], ['MYO3A', 'HSD17B2-AS1', 'DUOX2', 'LINC01115_ENSG00000237667'], ['FCN3', 'PCDHB4', 'S100A5'], ['USP43', 'IGFBP2', 'CNTNAP3'], ['NPM2', 'RHBDF1'], ['STXBP6', 'SERTAD3-AS1']]

Step 1: Module Eigengene Calculation¶

Module eigengene calculation is a concept used in the analysis of gene expression data.

Module/sub-network: A module refers to a group of genes that exhibit similar expression patterns across samples. Modules are often identified using clustering algorithms applied to gene expression data.

Eigengene: An eigengene represents the overall expression profile of a module. It is calculated as the first principal component of the gene expression profiles within the module. Essentially, the eigengene captures the main axis of variation or the common expression pattern shared by the genes within the module.

We will be using module eigengenes as representations for further downstream analysis.

In [8]:

# Initialise a DataFrame to store module eigengenes
module_eigengenes = pd.DataFrame(index=datExpr.index)

# Calculate the module eigengene for each community
for i, community in enumerate(separated_communities):
    community_genes = [gene for gene in community if gene in gene_names]
    if community_genes:
        community_expr = datExpr[community_genes]
        pca = PCA(n_components=1)
        eigengene = pca.fit_transform(community_expr)
        module_eigengenes[f'Module_{i+1}'] = eigengene[:, 0]

# Initialise a DataFrame to store module eigengenes module_eigengenes = pd.DataFrame(index=datExpr.index) # Calculate the module eigengene for each community for i, community in enumerate(separated_communities): community_genes = [gene for gene in community if gene in gene_names] if community_genes: community_expr = datExpr[community_genes] pca = PCA(n_components=1) eigengene = pca.fit_transform(community_expr) module_eigengenes[f'Module_{i+1}'] = eigengene[:, 0]

In [ ]:

In [9]:

metadata

metadata

Out[9]:

	nCount_RNA	nFeature_RNA	donor_id.1	MUTATION	percent.mt	scType_celltype	tissue_type	cell_type	tissue	development_stage	male	female	CH	normal	DNMT3A	TET2	NoMutation
donor_id
CH-20-001	2490.0	1403	CH-20-001	DNMT3A R882C	6.119578	Naive B cells	tissue	B cell	blood	60	1	0	1	0	1	0	0
CH-20-002	1192.0	629	CH-20-002	DNMT3A R729W (4%), DNMT3A R736C (2%)	3.803975	Naive B cells	tissue	B cell	blood	68	1	0	1	0	1	0	0
CH-20-004	1833.0	985	CH-20-004	TET2 R1516X (30%), TET2 Q659X (29%), SRSF2 P95...	5.335196	Naive B cells	tissue	B cell	blood	85	1	0	1	0	0	1	0
CH-20-005	1966.0	886	CH-20-005	TET2 V1900F (2%)	5.314136	Naive B cells	tissue	B cell	blood	58	0	1	1	0	0	1	0
CH-21-002	1912.0	938	CH-21-002	none	5.657238	Naive B cells	tissue	B cell	blood	48	0	1	0	1	0	0	1
CH-21-006	1356.0	709	CH-21-006	DNMT3A R882H (13%)	5.211849	Naive B cells	tissue	B cell	blood	67	0	1	1	0	1	0	0
CH-21-008	1117.0	575	CH-21-008	none	8.398348	Naive B cells	tissue	B cell	blood	70	0	1	0	1	0	0	1
CH-21-013	1321.0	816	CH-21-013	none	4.663212	Naive B cells	tissue	B cell	blood	73	1	0	0	1	0	0	1
CH-21-014	1064.0	623	CH-21-014	SRSF2 P95R (40%), TET2 L957Ifs*15 (51%)	4.146577	Naive B cells	tissue	B cell	blood	74	1	0	1	0	0	1	0
CH-21-017	1880.0	953	CH-21-017	DNMT3A R882H (20%), IDH2 R140Q (10%), TP53 R27...	6.519922	Naive B cells	tissue	B cell	blood	65	1	0	1	0	1	0	0
CH-21-020	5325.0	2286	CH-21-020	none	5.631046	Naive B cells	tissue	B cell	blood	61	1	0	0	1	0	0	1
CH-21-021	1671.0	943	CH-21-021	none	3.214286	Naive B cells	tissue	B cell	blood	83	1	0	0	1	0	0	1
CH-21-028	1690.0	866	CH-21-028	none	6.053894	Naive B cells	tissue	B cell	blood	89	0	1	0	1	0	0	1
CH-21-029	2180.0	1073	CH-21-029	TET2 G68X (2%)	2.570194	Naive B cells	tissue	B cell	blood	83	0	1	1	0	0	1	0
CH-21-031	1592.0	887	CH-21-031	none	6.734398	Naive B cells	tissue	B cell	blood	78	0	1	0	1	0	0	1
CH-21-033	2219.0	1138	CH-21-033	TET2 (33%)	5.670567	Naive B cells	tissue	B cell	blood	81	1	0	1	0	0	1	0
CH-21-034	2010.0	974	CH-21-034	DNMT3A Q816X (8%)	7.937365	Naive B cells	tissue	B cell	blood	39	0	1	1	0	1	0	0
CH-21-036	2686.0	1337	CH-21-036	DNMT3A splice (7%)	3.909544	Naive B cells	tissue	B cell	blood	91	1	0	1	0	1	0	0
CH-21-037	3546.0	1645	CH-21-037	TET2 (6.2%)	4.473764	Naive B cells	tissue	B cell	blood	71	1	0	1	0	0	1	0
CH-21-046	1918.0	907	CH-21-046	DNMT3A W305X (24%)	4.807084	Naive B cells	tissue	B cell	blood	80	1	0	1	0	1	0	0
CH-21-073	2148.0	1096	CH-21-073	SRSF2 (33%), TET2 Y1245Lfs*22 (27%), TET2 Q742...	5.174489	Naive B cells	tissue	B cell	blood	77	1	0	1	0	0	1	0
CH-21-074	1322.0	708	CH-21-074	TET2 C1378Y (23%)	3.328561	Naive B cells	tissue	B cell	blood	70	1	0	1	0	0	1	0
CH-21-077	1715.0	934	CH-21-077	DNMT3A R749C (9.1%)	6.539510	Naive B cells	tissue	B cell	blood	50	0	1	1	0	1	0	0
CH-21-079	1354.0	793	CH-21-079	DNMT3A M880V (5%)	6.386293	Naive B cells	tissue	B cell	blood	78	1	0	1	0	1	0	0

Lets tidy up the metadata dataframe further to only the columns needed for this analysis.

In [10]:

metadata2 = metadata.drop(columns=['donor_id.1','scType_celltype', 'tissue_type','cell_type', 'tissue', 'MUTATION'])

metadata2 = metadata.drop(columns=['donor_id.1','scType_celltype', 'tissue_type','cell_type', 'tissue', 'MUTATION'])

In [11]:

metadata2

metadata2

Out[11]:

	nCount_RNA	nFeature_RNA	percent.mt	development_stage	male	female	CH	normal	DNMT3A	TET2	NoMutation
donor_id
CH-20-001	2490.0	1403	6.119578	60	1	0	1	0	1	0	0
CH-20-002	1192.0	629	3.803975	68	1	0	1	0	1	0	0
CH-20-004	1833.0	985	5.335196	85	1	0	1	0	0	1	0
CH-20-005	1966.0	886	5.314136	58	0	1	1	0	0	1	0
CH-21-002	1912.0	938	5.657238	48	0	1	0	1	0	0	1
CH-21-006	1356.0	709	5.211849	67	0	1	1	0	1	0	0
CH-21-008	1117.0	575	8.398348	70	0	1	0	1	0	0	1
CH-21-013	1321.0	816	4.663212	73	1	0	0	1	0	0	1
CH-21-014	1064.0	623	4.146577	74	1	0	1	0	0	1	0
CH-21-017	1880.0	953	6.519922	65	1	0	1	0	1	0	0
CH-21-020	5325.0	2286	5.631046	61	1	0	0	1	0	0	1
CH-21-021	1671.0	943	3.214286	83	1	0	0	1	0	0	1
CH-21-028	1690.0	866	6.053894	89	0	1	0	1	0	0	1
CH-21-029	2180.0	1073	2.570194	83	0	1	1	0	0	1	0
CH-21-031	1592.0	887	6.734398	78	0	1	0	1	0	0	1
CH-21-033	2219.0	1138	5.670567	81	1	0	1	0	0	1	0
CH-21-034	2010.0	974	7.937365	39	0	1	1	0	1	0	0
CH-21-036	2686.0	1337	3.909544	91	1	0	1	0	1	0	0
CH-21-037	3546.0	1645	4.473764	71	1	0	1	0	0	1	0
CH-21-046	1918.0	907	4.807084	80	1	0	1	0	1	0	0
CH-21-073	2148.0	1096	5.174489	77	1	0	1	0	0	1	0
CH-21-074	1322.0	708	3.328561	70	1	0	1	0	0	1	0
CH-21-077	1715.0	934	6.539510	50	0	1	1	0	1	0	0
CH-21-079	1354.0	793	6.386293	78	1	0	1	0	1	0	0

In [12]:

# Merge module eigengenes with metadata
merged_data = pd.concat([metadata2, module_eigengenes], axis=1)

# Merge module eigengenes with metadata merged_data = pd.concat([metadata2, module_eigengenes], axis=1)

In [13]:

merged_data

merged_data

Out[13]:

	nCount_RNA	nFeature_RNA	percent.mt	development_stage	male	female	CH	normal	DNMT3A	TET2	...	Module_9	Module_10	Module_11	Module_12	Module_13	Module_14	Module_15	Module_16	Module_17	Module_18
donor_id
CH-20-001	2490.0	1403	6.119578	60	1	0	1	0	1	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-20-002	1192.0	629	3.803975	68	1	0	1	0	1	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-20-004	1833.0	985	5.335196	85	1	0	1	0	0	1	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-20-005	1966.0	886	5.314136	58	0	1	1	0	0	1	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-002	1912.0	938	5.657238	48	0	1	0	1	0	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-006	1356.0	709	5.211849	67	0	1	1	0	1	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-008	1117.0	575	8.398348	70	0	1	0	1	0	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-013	1321.0	816	4.663212	73	1	0	0	1	0	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-014	1064.0	623	4.146577	74	1	0	1	0	0	1	...	-0.188323	-0.138498	2.744634	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-017	1880.0	953	6.519922	65	1	0	1	0	1	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-020	5325.0	2286	5.631046	61	1	0	0	1	0	0	...	4.331426	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-021	1671.0	943	3.214286	83	1	0	0	1	0	0	...	-0.188323	-0.138498	-0.119332	2.475387	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-028	1690.0	866	6.053894	89	0	1	0	1	0	0	...	-0.188323	-0.138498	-0.119332	-0.107626	2.486453	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-029	2180.0	1073	2.570194	83	0	1	1	0	0	1	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-031	1592.0	887	6.734398	78	0	1	0	1	0	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	1.535032	-0.070642
CH-21-033	2219.0	1138	5.670567	81	1	0	1	0	0	1	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-034	2010.0	974	7.937365	39	0	1	1	0	1	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-036	2686.0	1337	3.909544	91	1	0	1	0	1	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	2.167574	1.941447	-0.085366	-0.066741	-0.070642
CH-21-037	3546.0	1645	4.473764	71	1	0	1	0	0	1	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	1.963424	-0.066741	-0.070642
CH-21-046	1918.0	907	4.807084	80	1	0	1	0	1	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-073	2148.0	1096	5.174489	77	1	0	1	0	0	1	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-074	1322.0	708	3.328561	70	1	0	1	0	0	1	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	1.624761
CH-21-077	1715.0	934	6.539510	50	0	1	1	0	1	0	...	-0.188323	3.185443	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642
CH-21-079	1354.0	793	6.386293	78	1	0	1	0	1	0	...	-0.188323	-0.138498	-0.119332	-0.107626	-0.108107	-0.094242	-0.084411	-0.085366	-0.066741	-0.070642

24 rows × 29 columns

In [14]:

# Calculate Pearson correlation between module eigengenes and metadata
correlation_matrix2 = merged_data.corr(method='pearson')

# Calculate Pearson correlation between module eigengenes and metadata correlation_matrix2 = merged_data.corr(method='pearson')

In [15]:

correlation_matrix2

correlation_matrix2

Out[15]:

	nCount_RNA	nFeature_RNA	percent.mt	development_stage	male	female	CH	normal	DNMT3A	TET2	...	Module_9	Module_10	Module_11	Module_12	Module_13	Module_14	Module_15	Module_16	Module_17	Module_18
nCount_RNA	1.000000	0.984470	-0.036198	-0.104184	0.221317	-0.221317	-0.079895	0.079895	-0.118003	0.044151	...	0.787767	-0.062271	-0.215561	-0.072632	-0.068158	0.166368	0.166368	0.368870	-0.091234	-0.154810
nFeature_RNA	0.984470	1.000000	-0.046930	-0.064074	0.284235	-0.284235	-0.070985	0.070985	-0.094982	0.029101	...	0.737328	-0.040462	-0.219377	-0.035284	-0.079582	0.191379	0.191379	0.368568	-0.067501	-0.170477
percent.mt	-0.036198	-0.046930	1.000000	-0.450374	-0.403977	0.403977	-0.205453	0.205453	0.208230	-0.411947	...	0.046851	0.182254	-0.174404	-0.313359	0.109875	-0.209733	-0.209733	-0.125638	0.211302	-0.296327
development_stage	-0.104184	-0.064074	-0.450374	1.000000	0.368332	-0.368332	-0.045963	0.045963	-0.261424	0.224159	...	-0.158508	-0.336576	0.051937	0.197629	0.294757	0.327133	0.327133	0.003373	0.116689	-0.012816
male	0.221317	0.284235	-0.403977	0.368332	1.000000	-1.000000	0.260360	-0.260360	0.066667	0.182574	...	0.161515	-0.269191	0.161515	0.161515	-0.269191	0.161515	0.161515	0.161515	-0.269191	0.161515
female	-0.221317	-0.284235	0.403977	-0.368332	-1.000000	1.000000	-0.260360	0.260360	-0.066667	-0.182574	...	-0.161515	0.269191	-0.161515	-0.161515	0.269191	-0.161515	-0.161515	-0.161515	0.269191	-0.161515
CH	-0.079895	-0.070985	-0.205453	-0.045963	0.260360	-0.260360	1.000000	-1.000000	0.497050	0.453743	...	-0.324946	0.133801	0.133801	-0.324946	-0.324946	0.133801	0.133801	0.133801	-0.324946	0.133801
normal	0.079895	0.070985	0.205453	0.045963	-0.260360	0.260360	-1.000000	1.000000	-0.497050	-0.453743	...	0.324946	-0.133801	-0.133801	0.324946	0.324946	-0.133801	-0.133801	-0.133801	0.324946	-0.133801
DNMT3A	-0.118003	-0.094982	0.208230	-0.261424	0.066667	-0.066667	0.497050	-0.497050	1.000000	-0.547723	...	-0.161515	0.269191	-0.161515	-0.161515	-0.161515	0.269191	0.269191	-0.161515	-0.161515	-0.161515
TET2	0.044151	0.029101	-0.411947	0.224159	0.182574	-0.182574	0.453743	-0.453743	-0.547723	1.000000	...	-0.147442	-0.147442	0.294884	-0.147442	-0.147442	-0.147442	-0.147442	0.294884	-0.147442	0.294884
NoMutation	0.079895	0.070985	0.205453	0.045963	-0.260360	0.260360	-1.000000	1.000000	-0.497050	-0.453743	...	0.324946	-0.133801	-0.133801	0.324946	0.324946	-0.133801	-0.133801	-0.133801	0.324946	-0.133801
Module_1	0.056404	0.076897	0.052741	0.165253	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_2	0.047221	0.039503	-0.409359	0.197629	-0.269191	0.269191	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_3	-0.203081	-0.246991	0.459308	-0.012816	-0.269191	0.269191	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_4	-0.185421	-0.215925	-0.225468	-0.045192	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_5	-0.155046	-0.108346	-0.097401	0.035749	0.161515	-0.161515	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_6	0.007192	-0.017450	0.390600	-0.514644	-0.269191	0.269191	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_7	-0.023419	-0.029531	0.179335	-0.093756	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_8	-0.003169	-0.068076	-0.000384	-0.207072	-0.269191	0.269191	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_9	0.787767	0.737328	0.046851	-0.158508	0.161515	-0.161515	-0.324946	0.324946	-0.161515	-0.147442	...	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_10	-0.062271	-0.040462	0.182254	-0.336576	-0.269191	0.269191	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_11	-0.215561	-0.219377	-0.174404	0.051937	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_12	-0.072632	-0.035284	-0.313359	0.197629	0.161515	-0.161515	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_13	-0.068158	-0.079582	0.109875	0.294757	-0.269191	0.269191	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_14	0.166368	0.191379	-0.209733	0.327133	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	1.000000	-0.043478	-0.043478	-0.043478
Module_15	0.166368	0.191379	-0.209733	0.327133	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	1.000000	-0.043478	-0.043478	-0.043478
Module_16	0.368870	0.368568	-0.125638	0.003373	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	-0.043478	-0.043478
Module_17	-0.091234	-0.067501	0.211302	0.116689	-0.269191	0.269191	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	-0.043478
Module_18	-0.154810	-0.170477	-0.296327	-0.012816	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000

29 rows × 29 columns

In [16]:

#The correlation matrix needs to be reformatted into the correct format:
correlation_matrix3 = correlation_matrix2.drop(['nCount_RNA', 'nFeature_RNA', 'percent.mt', 'development_stage', 
                                               'male', 'female', 'CH', 'normal', 'DNMT3A', 'TET2', 'NoMutation'])

#The correlation matrix needs to be reformatted into the correct format: correlation_matrix3 = correlation_matrix2.drop(['nCount_RNA', 'nFeature_RNA', 'percent.mt', 'development_stage', 'male', 'female', 'CH', 'normal', 'DNMT3A', 'TET2', 'NoMutation'])

In [17]:

correlation_matrix3

correlation_matrix3

Out[17]:

	nCount_RNA	nFeature_RNA	percent.mt	development_stage	male	female	CH	normal	DNMT3A	TET2	...	Module_9	Module_10	Module_11	Module_12	Module_13	Module_14	Module_15	Module_16	Module_17	Module_18
Module_1	0.056404	0.076897	0.052741	0.165253	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_2	0.047221	0.039503	-0.409359	0.197629	-0.269191	0.269191	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_3	-0.203081	-0.246991	0.459308	-0.012816	-0.269191	0.269191	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_4	-0.185421	-0.215925	-0.225468	-0.045192	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_5	-0.155046	-0.108346	-0.097401	0.035749	0.161515	-0.161515	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_6	0.007192	-0.017450	0.390600	-0.514644	-0.269191	0.269191	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_7	-0.023419	-0.029531	0.179335	-0.093756	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_8	-0.003169	-0.068076	-0.000384	-0.207072	-0.269191	0.269191	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_9	0.787767	0.737328	0.046851	-0.158508	0.161515	-0.161515	-0.324946	0.324946	-0.161515	-0.147442	...	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_10	-0.062271	-0.040462	0.182254	-0.336576	-0.269191	0.269191	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_11	-0.215561	-0.219377	-0.174404	0.051937	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_12	-0.072632	-0.035284	-0.313359	0.197629	0.161515	-0.161515	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_13	-0.068158	-0.079582	0.109875	0.294757	-0.269191	0.269191	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478
Module_14	0.166368	0.191379	-0.209733	0.327133	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	1.000000	-0.043478	-0.043478	-0.043478
Module_15	0.166368	0.191379	-0.209733	0.327133	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	1.000000	-0.043478	-0.043478	-0.043478
Module_16	0.368870	0.368568	-0.125638	0.003373	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	-0.043478	-0.043478
Module_17	-0.091234	-0.067501	0.211302	0.116689	-0.269191	0.269191	-0.324946	0.324946	-0.161515	-0.147442	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000	-0.043478
Module_18	-0.154810	-0.170477	-0.296327	-0.012816	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	...	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	-0.043478	1.000000

18 rows × 29 columns

In [18]:

correlation_matrix3 = correlation_matrix3.drop(correlation_matrix3.columns[11:], axis=1)

correlation_matrix3 = correlation_matrix3.drop(correlation_matrix3.columns[11:], axis=1)

In [19]:

correlation_matrix3

correlation_matrix3

Out[19]:

	nCount_RNA	nFeature_RNA	percent.mt	development_stage	male	female	CH	normal	DNMT3A	TET2	NoMutation
Module_1	0.056404	0.076897	0.052741	0.165253	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	-0.133801
Module_2	0.047221	0.039503	-0.409359	0.197629	-0.269191	0.269191	0.133801	-0.133801	-0.161515	0.294884	-0.133801
Module_3	-0.203081	-0.246991	0.459308	-0.012816	-0.269191	0.269191	-0.324946	0.324946	-0.161515	-0.147442	0.324946
Module_4	-0.185421	-0.215925	-0.225468	-0.045192	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	-0.133801
Module_5	-0.155046	-0.108346	-0.097401	0.035749	0.161515	-0.161515	-0.324946	0.324946	-0.161515	-0.147442	0.324946
Module_6	0.007192	-0.017450	0.390600	-0.514644	-0.269191	0.269191	0.133801	-0.133801	0.269191	-0.147442	-0.133801
Module_7	-0.023419	-0.029531	0.179335	-0.093756	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	-0.133801
Module_8	-0.003169	-0.068076	-0.000384	-0.207072	-0.269191	0.269191	0.133801	-0.133801	-0.161515	0.294884	-0.133801
Module_9	0.787767	0.737328	0.046851	-0.158508	0.161515	-0.161515	-0.324946	0.324946	-0.161515	-0.147442	0.324946
Module_10	-0.062271	-0.040462	0.182254	-0.336576	-0.269191	0.269191	0.133801	-0.133801	0.269191	-0.147442	-0.133801
Module_11	-0.215561	-0.219377	-0.174404	0.051937	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	-0.133801
Module_12	-0.072632	-0.035284	-0.313359	0.197629	0.161515	-0.161515	-0.324946	0.324946	-0.161515	-0.147442	0.324946
Module_13	-0.068158	-0.079582	0.109875	0.294757	-0.269191	0.269191	-0.324946	0.324946	-0.161515	-0.147442	0.324946
Module_14	0.166368	0.191379	-0.209733	0.327133	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	-0.133801
Module_15	0.166368	0.191379	-0.209733	0.327133	0.161515	-0.161515	0.133801	-0.133801	0.269191	-0.147442	-0.133801
Module_16	0.368870	0.368568	-0.125638	0.003373	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	-0.133801
Module_17	-0.091234	-0.067501	0.211302	0.116689	-0.269191	0.269191	-0.324946	0.324946	-0.161515	-0.147442	0.324946
Module_18	-0.154810	-0.170477	-0.296327	-0.012816	0.161515	-0.161515	0.133801	-0.133801	-0.161515	0.294884	-0.133801

We now need to calculate the p-values for the correlations on the merged data.

In [20]:

# Initialise an empty DataFrame to store p-values
module_p_values = pd.DataFrame(index=module_eigengenes.columns, columns=metadata2.columns)

# Calculate p-values for correlations between module eigengenes and metadata
for module in module_eigengenes.columns:
    for metadata_column in metadata2.columns:
        # Calculate correlation coefficient and p-value
        correlation_coefficient, p_value = pearsonr(module_eigengenes[module], metadata2[metadata_column])
        # Store p-value in the DataFrame
        module_p_values.loc[module, metadata_column] = p_value

print(module_p_values)

# Initialise an empty DataFrame to store p-values module_p_values = pd.DataFrame(index=module_eigengenes.columns, columns=metadata2.columns) # Calculate p-values for correlations between module eigengenes and metadata for module in module_eigengenes.columns: for metadata_column in metadata2.columns: # Calculate correlation coefficient and p-value correlation_coefficient, p_value = pearsonr(module_eigengenes[module], metadata2[metadata_column]) # Store p-value in the DataFrame module_p_values.loc[module, metadata_column] = p_value print(module_p_values)

          nCount_RNA nFeature_RNA percent.mt development_stage      male  \
Module_1    0.793491      0.72099   0.806646          0.440304  0.450857   
Module_2    0.826566      0.85459   0.046988          0.354618  0.203374   
Module_3    0.341225     0.244609   0.023954          0.952607  0.203374   
Module_4    0.385701     0.310888   0.289457          0.833917  0.450857   
Module_5    0.469428     0.614305   0.650711          0.868287  0.450857   
Module_6    0.973395     0.935497   0.059141          0.010078  0.203374   
Module_7    0.913504     0.891046   0.401758          0.663019  0.450857   
Module_8    0.988275     0.751953   0.998581          0.331617  0.203374   
Module_9    0.000005     0.000039   0.827907          0.459441  0.450857   
Module_10   0.772535     0.851099    0.39401          0.107796  0.203374   
Module_11   0.311724     0.303027   0.415034          0.809543  0.450857   
Module_12   0.735911     0.869983   0.135952          0.354618  0.450857   
Module_13   0.751662     0.711648   0.609287          0.162049  0.203374   
Module_14   0.437182     0.370342     0.3253          0.118677  0.450857   
Module_15   0.437182     0.370342     0.3253          0.118677  0.450857   
Module_16     0.0761     0.076359    0.55857          0.987522  0.450857   
Module_17   0.671581     0.753986   0.321611          0.587131  0.203374   
Module_18   0.470112     0.425779    0.15972          0.952607  0.450857   

             female        CH    normal    DNMT3A     TET2 NoMutation  
Module_1   0.450857   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_2   0.203374   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_3   0.203374  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_4   0.450857   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_5   0.450857  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_6   0.203374   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_7   0.450857   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_8   0.203374   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_9   0.450857  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_10  0.203374   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_11  0.450857   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_12  0.450857  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_13  0.203374  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_14  0.450857   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_15  0.450857   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_16  0.450857   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_17  0.203374  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_18  0.450857   0.53308   0.53308  0.450857  0.16186    0.53308  

We now need to calculate the adjusted p-values.

In [21]:

# Calculate adjusted p-values using FDR correction
adjusted_p_values = pd.DataFrame(index=module_p_values.index, columns=module_p_values.columns)
for column in module_p_values.columns:
    p_values = module_p_values[column].astype(float)
    # Perform FDR correction
    _, adj_p_values, _, _ = multipletests(p_values, method='fdr_bh')
    adjusted_p_values[column] = adj_p_values

print(adjusted_p_values)

# Calculate adjusted p-values using FDR correction adjusted_p_values = pd.DataFrame(index=module_p_values.index, columns=module_p_values.columns) for column in module_p_values.columns: p_values = module_p_values[column].astype(float) # Perform FDR correction _, adj_p_values, _, _ = multipletests(p_values, method='fdr_bh') adjusted_p_values[column] = adj_p_values print(adjusted_p_values)

           nCount_RNA  nFeature_RNA  percent.mt  development_stage      male  \
Module_1     0.988275      0.935497    0.876607           0.826994  0.450857   
Module_2     0.988275      0.935497    0.354845           0.797891  0.450857   
Module_3     0.940223      0.935497    0.354845           0.987522  0.450857   
Module_4     0.940223      0.935497    0.622551           0.987522  0.450857   
Module_5     0.940223      0.935497    0.780854           0.987522  0.450857   
Module_6     0.988275      0.935497    0.354845           0.181400  0.450857   
Module_7     0.988275      0.935497    0.622551           0.987522  0.450857   
Module_8     0.988275      0.935497    0.998581           0.797891  0.450857   
Module_9     0.000088      0.000710    0.876607           0.826994  0.450857   
Module_10    0.988275      0.935497    0.622551           0.534046  0.450857   
Module_11    0.940223      0.935497    0.622551           0.987522  0.450857   
Module_12    0.988275      0.935497    0.574992           0.797891  0.450857   
Module_13    0.988275      0.935497    0.780854           0.583378  0.450857   
Module_14    0.940223      0.935497    0.622551           0.534046  0.450857   
Module_15    0.940223      0.935497    0.622551           0.534046  0.450857   
Module_16    0.684901      0.687232    0.773405           0.987522  0.450857   
Module_17    0.988275      0.935497    0.622551           0.960760  0.450857   
Module_18    0.940223      0.935497    0.574992           0.987522  0.450857   

             female        CH    normal    DNMT3A      TET2  NoMutation  
Module_1   0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_2   0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_3   0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_4   0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_5   0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_6   0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_7   0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_8   0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_9   0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_10  0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_11  0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_12  0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_13  0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_14  0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_15  0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_16  0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_17  0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_18  0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  

Step 2: Correlation Heatmap¶

We want a method to highlight interesting modules.¶

For this purpose we will be creating a heatmap of the correlations and associated adjusted p-values between the modules and the metadata.¶

In [22]:

# Function to annotate heatmap with correlation values and p-values
def annotate_heatmap_with_p_values(ax, correlation_matrix, p_values, threshold=0.05):
    stars = np.empty(p_values.shape, dtype='<U2')
    stars[p_values > threshold] = ''
    stars[p_values <= threshold] = '*'
    for i in range(correlation_matrix.shape[0]):
        for j in range(correlation_matrix.shape[1]):
            # Format annotation string with correlation value
            annotation_corr = f"{correlation_matrix.iloc[i, j]:.2f}"
            # Format p-value in brackets
            annotation_p_value = f"({p_values.iloc[i, j]:.2f})"
            # Add star for significant p-values
            if p_values.iloc[i, j] <= threshold:
                annotation_p_value += '*'
            # Add annotations
            ax.text(j+0.5, i+0.4, annotation_corr, ha='center', va='center', color='black')
            ax.text(j+0.5, i+0.6, annotation_p_value, ha='center', va='center', color='black')

# Function to annotate heatmap with correlation values and p-values def annotate_heatmap_with_p_values(ax, correlation_matrix, p_values, threshold=0.05): stars = np.empty(p_values.shape, dtype=' threshold] = '' stars[p_values <= threshold] = '*' for i in range(correlation_matrix.shape[0]): for j in range(correlation_matrix.shape[1]): # Format annotation string with correlation value annotation_corr = f"{correlation_matrix.iloc[i, j]:.2f}" # Format p-value in brackets annotation_p_value = f"({p_values.iloc[i, j]:.2f})" # Add star for significant p-values if p_values.iloc[i, j] <= threshold: annotation_p_value += '*' # Add annotations ax.text(j+0.5, i+0.4, annotation_corr, ha='center', va='center', color='black') ax.text(j+0.5, i+0.6, annotation_p_value, ha='center', va='center', color='black')

In [25]:

# Visualise the correlation matrix as a heatmap with correlation values and p-values
plt.figure(figsize=(15, 15))
heatmap = sns.heatmap(correlation_matrix3, annot=False, cmap='coolwarm', vmin=-1, vmax=1)
plt.title('Correlation between Module Eigengenes and Metadata')

# Annotate heatmap with correlation values and p-values
annotate_heatmap_with_p_values(heatmap, correlation_matrix3, adjusted_p_values)

plt.show()

# Visualise the correlation matrix as a heatmap with correlation values and p-values plt.figure(figsize=(15, 15)) heatmap = sns.heatmap(correlation_matrix3, annot=False, cmap='coolwarm', vmin=-1, vmax=1) plt.title('Correlation between Module Eigengenes and Metadata') # Annotate heatmap with correlation values and p-values annotate_heatmap_with_p_values(heatmap, correlation_matrix3, adjusted_p_values) plt.show()

In [ ]:

External Reading:

• PCA: https://www.sartorius.com/en/knowledge/science-snippets/what-is-principal-component-analysis-pca-and-how-it-is-used-507186
• Heatmaps: https://www.atlassian.com/data/charts/heatmap-complete-guide#:~:text=What%20is%20a%20heatmap%3F,in%20the%20corresponding%20cell%20range.
• False Discovery Rate: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1716-1

Exercise Questions:

1. What is a correlation and why is it used in gene co-expression analysis?
2. What is the purpose of calculating module eigengenes in the context of gene expression data analysis?
3. Explain the rationale behind using PCA to calculate module eigengenes. How does PCA help in capturing the main variation in the gene expression data within a module?
4. Why are adjusted p-values used instead of p-values?
5. In the context of merging module eigengenes with clinical metadata, why might it be important to drop certain columns like 'donor_id.1', 'scType_celltype', 'tissue_type', 'cell_type', 'tissue', and 'MUTATION'?
6. What is fdr_bh and explain it fully?
7. Based on the heatmap, what can you summarise?
8. It can be important to check for missing values within the data. How would you do this? Also, if there are missing values, how would you handle them in this context?
9. Perform FDR adjustment on the p-values and compare the number of significant correlations before and after correction.
10. Plot the expression of eigengenes for a selected module against a specific metadata variable and provide an interpretation of the plot.

Answers:

1. In the context of gene co-expression analysis, correlation refers to the statistical measure of the strength and direction of association between the expression levels of two genes across a set of samples or conditions. It quantifies the degree to which the expression patterns of two genes are similar or dissimilar across different biological contexts.
2. Module eigengenes represent the overall expression profile of gene modules, summarising the main patterns of gene expression within a group of co-expressed genes. This simplifies complex gene expression data, making it easier to study associations between gene expression patterns and clinical variables.
3. PCA identifies the principal components that capture the maximum variance in the data. By taking the first principal component as the module eigengene, it provides a single vector that represents the predominant expression pattern of the genes within the module, thereby summarising the main axis of variation.
4. Adjusted p-values are used instead of raw p-values in statistical hypothesis testing to correct for multiple comparisons. When conducting multiple statistical tests simultaneously (such as testing multiple genes in gene expression analysis or multiple variables in genomics studies), the probability of obtaining false positive results increases. This increased probability arises due to the cumulative effect of conducting multiple tests, leading to an inflated Type I error rate (false positives).
5. These columns might be dropped because they are categorical variables or identifiers that do not directly relate to the continuous expression data or are not relevant for biological interpretation. Including only relevant numerical metadata ensures meaningful correlation analysis.
6. FDR_BH stands for False Discovery Rate (FDR) control using the Benjamini-Hochberg (BH) procedure. It is a statistical method used to control the proportion of false positives among all significant results when conducting multiple hypothesis tests simultaneously.
7. There are 18 modules in total. Number of counts and number of featurs appear to significantly impact module 9. Although the rest of the modules do not have significant correlations, there are still high positive correlations in terms of single cell data, with individual mutations as well as development stage. This can give an indication for which sets of genes may be impacted by those mutations or disease.

Answer 8:

In [26]:

print(datExpr.isnull().sum().sum())
print(metadata.isnull().sum().sum())

print(datExpr.isnull().sum().sum()) print(metadata.isnull().sum().sum())

0
0

In this case there are no missing values, but if there were then it's important to calculate first how many missing values there are e.g. per gene within the expression matrix or per donor within the metadata. If there was a high proportion of missing values, then those respective rows would have to be dropped. Alternatively, missing values can also be imputed using mean/median imputation or more sophisticated techniques like K-Nearest Neighbors imputation.

Answer 9:

In [27]:

# Initialise a DataFrame to store p-values
module_p_values = pd.DataFrame(index=module_eigengenes.columns, columns=metadata2.columns)

# Calculate p-values for correlations between module eigengenes and metadata
for module in module_eigengenes.columns:
    for metadata_column in metadata2.columns:
        # Calculate correlation coefficient and p-value
        _, p_value = pearsonr(module_eigengenes[module], metadata2[metadata_column])
        # Store p-value in the DataFrame
        module_p_values.loc[module, metadata_column] = p_value

print("P-values before FDR adjustment:")
print(module_p_values)

# Initialise a DataFrame to store p-values module_p_values = pd.DataFrame(index=module_eigengenes.columns, columns=metadata2.columns) # Calculate p-values for correlations between module eigengenes and metadata for module in module_eigengenes.columns: for metadata_column in metadata2.columns: # Calculate correlation coefficient and p-value _, p_value = pearsonr(module_eigengenes[module], metadata2[metadata_column]) # Store p-value in the DataFrame module_p_values.loc[module, metadata_column] = p_value print("P-values before FDR adjustment:") print(module_p_values)

P-values before FDR adjustment:
          nCount_RNA nFeature_RNA percent.mt development_stage      male  \
Module_1    0.793491      0.72099   0.806646          0.440304  0.450857   
Module_2    0.826566      0.85459   0.046988          0.354618  0.203374   
Module_3    0.341225     0.244609   0.023954          0.952607  0.203374   
Module_4    0.385701     0.310888   0.289457          0.833917  0.450857   
Module_5    0.469428     0.614305   0.650711          0.868287  0.450857   
Module_6    0.973395     0.935497   0.059141          0.010078  0.203374   
Module_7    0.913504     0.891046   0.401758          0.663019  0.450857   
Module_8    0.988275     0.751953   0.998581          0.331617  0.203374   
Module_9    0.000005     0.000039   0.827907          0.459441  0.450857   
Module_10   0.772535     0.851099    0.39401          0.107796  0.203374   
Module_11   0.311724     0.303027   0.415034          0.809543  0.450857   
Module_12   0.735911     0.869983   0.135952          0.354618  0.450857   
Module_13   0.751662     0.711648   0.609287          0.162049  0.203374   
Module_14   0.437182     0.370342     0.3253          0.118677  0.450857   
Module_15   0.437182     0.370342     0.3253          0.118677  0.450857   
Module_16     0.0761     0.076359    0.55857          0.987522  0.450857   
Module_17   0.671581     0.753986   0.321611          0.587131  0.203374   
Module_18   0.470112     0.425779    0.15972          0.952607  0.450857   

             female        CH    normal    DNMT3A     TET2 NoMutation  
Module_1   0.450857   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_2   0.203374   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_3   0.203374  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_4   0.450857   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_5   0.450857  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_6   0.203374   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_7   0.450857   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_8   0.203374   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_9   0.450857  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_10  0.203374   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_11  0.450857   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_12  0.450857  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_13  0.203374  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_14  0.450857   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_15  0.450857   0.53308   0.53308  0.203374  0.49175    0.53308  
Module_16  0.450857   0.53308   0.53308  0.450857  0.16186    0.53308  
Module_17  0.203374  0.121306  0.121306  0.450857  0.49175   0.121306  
Module_18  0.450857   0.53308   0.53308  0.450857  0.16186    0.53308  

In [28]:

# Initialize a DataFrame to store adjusted p-values
adjusted_p_values = pd.DataFrame(index=module_p_values.index, columns=module_p_values.columns)

# Apply FDR correction
for column in module_p_values.columns:
    p_values = module_p_values[column].astype(float)
    _, adj_p_values, _, _ = multipletests(p_values, method='fdr_bh')
    adjusted_p_values[column] = adj_p_values

print("Adjusted p-values after FDR correction:")
print(adjusted_p_values)

# Initialize a DataFrame to store adjusted p-values adjusted_p_values = pd.DataFrame(index=module_p_values.index, columns=module_p_values.columns) # Apply FDR correction for column in module_p_values.columns: p_values = module_p_values[column].astype(float) _, adj_p_values, _, _ = multipletests(p_values, method='fdr_bh') adjusted_p_values[column] = adj_p_values print("Adjusted p-values after FDR correction:") print(adjusted_p_values)

Adjusted p-values after FDR correction:
           nCount_RNA  nFeature_RNA  percent.mt  development_stage      male  \
Module_1     0.988275      0.935497    0.876607           0.826994  0.450857   
Module_2     0.988275      0.935497    0.354845           0.797891  0.450857   
Module_3     0.940223      0.935497    0.354845           0.987522  0.450857   
Module_4     0.940223      0.935497    0.622551           0.987522  0.450857   
Module_5     0.940223      0.935497    0.780854           0.987522  0.450857   
Module_6     0.988275      0.935497    0.354845           0.181400  0.450857   
Module_7     0.988275      0.935497    0.622551           0.987522  0.450857   
Module_8     0.988275      0.935497    0.998581           0.797891  0.450857   
Module_9     0.000088      0.000710    0.876607           0.826994  0.450857   
Module_10    0.988275      0.935497    0.622551           0.534046  0.450857   
Module_11    0.940223      0.935497    0.622551           0.987522  0.450857   
Module_12    0.988275      0.935497    0.574992           0.797891  0.450857   
Module_13    0.988275      0.935497    0.780854           0.583378  0.450857   
Module_14    0.940223      0.935497    0.622551           0.534046  0.450857   
Module_15    0.940223      0.935497    0.622551           0.534046  0.450857   
Module_16    0.684901      0.687232    0.773405           0.987522  0.450857   
Module_17    0.988275      0.935497    0.622551           0.960760  0.450857   
Module_18    0.940223      0.935497    0.574992           0.987522  0.450857   

             female        CH    normal    DNMT3A      TET2  NoMutation  
Module_1   0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_2   0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_3   0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_4   0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_5   0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_6   0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_7   0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_8   0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_9   0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_10  0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_11  0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_12  0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_13  0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_14  0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_15  0.450857  0.533080  0.533080  0.450857  0.491750    0.533080  
Module_16  0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  
Module_17  0.450857  0.363919  0.363919  0.450857  0.491750    0.363919  
Module_18  0.450857  0.533080  0.533080  0.450857  0.485579    0.533080  

In [29]:

# Threshold for significance
significance_threshold = 0.05

# Count significant correlations before adjustment
significant_before = (module_p_values < significance_threshold).sum().sum()
print(f"Number of significant correlations before FDR adjustment: {significant_before}")

# Count significant correlations after adjustment
significant_after = (adjusted_p_values < significance_threshold).sum().sum()
print(f"Number of significant correlations after FDR adjustment: {significant_after}")

# Threshold for significance significance_threshold = 0.05 # Count significant correlations before adjustment significant_before = (module_p_values < significance_threshold).sum().sum() print(f"Number of significant correlations before FDR adjustment: {significant_before}") # Count significant correlations after adjustment significant_after = (adjusted_p_values < significance_threshold).sum().sum() print(f"Number of significant correlations after FDR adjustment: {significant_after}")

Number of significant correlations before FDR adjustment: 5
Number of significant correlations after FDR adjustment: 2

Answer 10:

In [30]:

plt.scatter(metadata['development_stage'], module_eigengenes['Module_1'])
plt.xlabel('Selected Metadata Variable')
plt.ylabel('Module 1 Eigengene')
plt.title('Eigengene Expression vs Metadata Variable')
plt.show()

plt.scatter(metadata['development_stage'], module_eigengenes['Module_1']) plt.xlabel('Selected Metadata Variable') plt.ylabel('Module 1 Eigengene') plt.title('Eigengene Expression vs Metadata Variable') plt.show()

As can be seen from the scatter plot, development stage appears to have a weak relationship with Module 1 as the dots are spread out. There is also an outlier within the data. There also appears to be no apparent trend in the data. This may be due to the few numbers of samples as well as due to the sparse nature of single-cell data.