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ef814bf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | import scanpy as sc
import pandas as pd
import numpy as np
from scipy.stats import ttest_ind
from statsmodels.stats.multitest import multipletests
def filter_rna_cells_genes(adata, min_genes=100, min_cells=10):
"""
Filter cells and genes in RNA data.
Parameters:
- adata (AnnData): Annotated data object containing the RNA data.
- min_genes (int): The minimum number of genes to keep a cell. Default is 100.
- min_cells (int): The minimum number of cells to keep a gene. Default is 10.
Returns:
- adata_filtered (AnnData): Annotated data object containing the filtered RNA data.
"""
sc.pp.filter_cells(adata, min_genes=min_genes)
sc.pp.filter_genes(adata, min_cells=min_cells)
return adata
def get_degs(adata, method='t-test', p_val=0.05,
batch_remove=True, batch_key='batch_no', label_key='label',
reference='dead-end', target='reprogramming'):
"""
Get differentially expressed genes (DEGs) from the RNA data.
"""
sc.pp.normalize_total(adata, target_sum=1e4, exclude_highly_expressed=False)
sc.pp.log1p(adata)
if batch_remove:
sc.pp.combat(adata, key=batch_key)
sc.tl.rank_genes_groups(adata, groupby=label_key, method=method, n_genes=adata.shape[1], use_raw=False, reference=reference)
de_results = adata.uns['rank_genes_groups']
gene_list = list(pd.DataFrame(de_results['names'])[target])
# Compute mean and std for each gene in both groups.
# These are Series indexed by gene names (from adata.var_names).
group_a_mean_expression = adata[adata.obs[label_key] == reference].to_df().mean()
group_a_std_expression = adata[adata.obs[label_key] == reference].to_df().std()
group_b_mean_expression = adata[adata.obs[label_key] == target].to_df().mean()
group_b_std_expression = adata[adata.obs[label_key] == target].to_df().std()
# Reorder (or reindex) the computed series so that they match the order in gene_list.
group_a_mean_expression = group_a_mean_expression.reindex(gene_list)
group_a_std_expression = group_a_std_expression.reindex(gene_list)
group_b_mean_expression = group_b_mean_expression.reindex(gene_list)
group_b_std_expression = group_b_std_expression.reindex(gene_list)
# Create the DEG DataFrame.
df = pd.DataFrame({
'gene': gene_list,
'mean_exp_de': group_a_mean_expression.values, # 'dead-end' (reference)
'mean_exp_re': group_b_mean_expression.values, # 'reprogramming' (target)
'std_exp_de': group_a_std_expression.values,
'std_exp_re': group_b_std_expression.values,
'pval': de_results['pvals'][target],
'pval_adj': de_results['pvals_adj'][target],
'log_fc': de_results['logfoldchanges'][target],
})
df['group'] = df.apply(lambda row: reference if row['log_fc'] < 0 else target, axis=1)
df.sort_values(by='pval_adj', inplace=True)
df.reset_index(drop=True, inplace=True)
df['pval_adj_log'] = -np.log10(df['pval_adj'])
df = df[(df.pval_adj < p_val) & ((df.log_fc < -1) | ((df.log_fc > 1) & (df.log_fc < 7)))]
return df
def get_flux_degs(adata_Flux_labelled, labels):
dead_end = adata_Flux_labelled[labels.values == "dead-end"]
reprogramming = adata_Flux_labelled[labels.values == "reprogramming"]
features = []
log_fold_changes = []
p_values = []
mean_des = []
mean_res = []
std_des = []
std_res = []
for feature in adata_Flux_labelled.columns:
mean_de = dead_end[feature].mean()
mean_re = reprogramming[feature].mean()
std_de = dead_end[feature].std()
std_re = reprogramming[feature].std()
log_fold_change = np.log2(mean_re + 1e-10) - np.log2(mean_de + 1e-10)
t_stat, p_value = ttest_ind(dead_end[feature], reprogramming[feature], nan_policy="omit")
mean_des.append(mean_de)
mean_res.append(mean_re)
std_des.append(std_de)
std_res.append(std_re)
features.append(feature)
log_fold_changes.append(log_fold_change)
p_values.append(p_value)
adjusted_p_values = multipletests(p_values, method="fdr_bh")[1]
df_flux_degs = pd.DataFrame({
"feature": features,
"mean_de": mean_des,
"mean_re": mean_res,
"mean_diff": np.array(mean_res) - np.array(mean_des),
"std_de": std_des,
"std_re": std_res,
"log_fc": log_fold_changes,
"pval": p_values,
"pval_adj": adjusted_p_values,
'pval_adj_log' : -np.log10(adjusted_p_values)
})
df_flux_degs['group'] = df_flux_degs.apply(lambda row: 'dead-end' if row['mean_de'] > row['mean_re'] else 'reprogramming', axis=1)
df_flux_degs = df_flux_degs.sort_values(by="pval_adj").reset_index(drop=True)
return df_flux_degs
def get_atac_degs(adata, method='t-test', label_key='label',
reference='dead-end', target='reprogramming'):
"""
Get differentially expressed genes (DEGs) from the ATAC data.
"""
sc.tl.rank_genes_groups(adata, groupby=label_key, method=method,
n_genes=adata.shape[1], use_raw=False, reference=reference)
group_a_mean_expression = adata[adata.obs[label_key] == reference].to_df().mean()
group_a_std_expression = adata[adata.obs[label_key] == reference].to_df().std()
group_b_mean_expression = adata[adata.obs[label_key] == target].to_df().mean()
group_b_std_expression = adata[adata.obs[label_key] == target].to_df().std()
de_results = adata.uns['rank_genes_groups']
features = list(pd.DataFrame(de_results['names'])[target])
# Reindex the mean and std Series to this feature list
mean_de = group_a_mean_expression.reindex(features)
mean_re = group_b_mean_expression.reindex(features)
std_de = group_a_std_expression.reindex(features)
std_re = group_b_std_expression.reindex(features)
min_val = min(mean_de.min(), mean_re.min())
# Determine a shift value so that the smallest value becomes a small positive number.
shift = 0
if min_val <= 0:
shift = abs(min_val) + 1e-10
df = pd.DataFrame({
'feature': list(pd.DataFrame(de_results['names'])[target]),
'pval': de_results['pvals'][target],
'pval_adj': de_results['pvals_adj'][target],
'log_fc': np.log2(mean_re + shift) - np.log2(mean_de + shift),
'mean_de': mean_de,
'mean_re': mean_re,
'mean_diff': mean_re - mean_de,
'std_de': std_de,
'std_re': std_re,
})
df['group'] = df.apply(lambda row: 'dead-end' if row['mean_de'] > row['mean_re'] else 'reprogramming', axis=1)
df.sort_values(by='pval_adj', inplace=True)
df.reset_index(drop=True, inplace=True)
df['pval_adj_log'] = -np.log10(df['pval_adj'])
return df
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