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storm.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
import numpy as np
import scipy.sparse as sp
from scipy.sparse import csr_matrix
from dynamo.estimation.tsc.twostep import (
lin_reg_gamma_synthesis,
fit_slope_stochastic
)
from storm_param_infer import (
MLE_Cell_Specific_Poisson_SS,
MLE_Cell_Specific_Poisson,
MLE_Cell_Specific_Zero_Inflated_Poisson,
MLE_Independent_Cell_Specific_Poisson,
Cell_Specific_Alpha_Beta,
MLE_ICSP_Without_SS,
Select_SCV_Genes
)
def storm_kin_data(adata, use_genes=None, tkey='time', assumption='steady_state', method='CSP_Baseline'):
subset_adata = adata[:, use_genes].copy()
gene_indices = [list(adata.var_names).index(item) if item in list(adata.var_names) else -1 for item in list(use_genes)]
time = subset_adata.obs[tkey]
# Initialization based on the steady-state assumption
if method is not 'CSP_Splicing':
layers_smoothed = ["M_t", "M_n"]
Total_smoothed, New_smoothed = (
subset_adata.layers[layers_smoothed[0]].T,
subset_adata.layers[layers_smoothed[1]].T,
)
(gamma_init, _, _, _, _,) = lin_reg_gamma_synthesis(Total_smoothed, New_smoothed, time, perc_right=5)
# Read raw counts
layers_raw = ["total", "new"]
Total_raw, New_raw = (
subset_adata.layers[layers_raw[0]].T,
subset_adata.layers[layers_raw[1]].T,
)
# Read smoothed values based CSP type distribution for cell-specific parameter inference
layers_smoothed_CSP = ["M_CSP_t", "M_CSP_n"]
Total_smoothed_CSP, New_smoothed_CSP = (
subset_adata.layers[layers_smoothed_CSP[0]].T,
subset_adata.layers[layers_smoothed_CSP[1]].T,
)
# Parameters inference based on maximum likelihood estimation
cell_total = subset_adata.obs['initial_cell_size'].astype("float").values
else:
layers_smoothed = ["M_u", "M_s", "M_t", "M_n"]
U_smoothed, S_smoothed, Total_smoothed, New_smoothed = (
subset_adata.layers[layers_smoothed[0]].T,
subset_adata.layers[layers_smoothed[1]].T,
subset_adata.layers[layers_smoothed[2]].T,
subset_adata.layers[layers_smoothed[3]].T,
)
US_smoothed, S2_smoothed = (
subset_adata.layers["M_us"].T,
subset_adata.layers["M_ss"].T,
)
(gamma_k, _, _, _,) = fit_slope_stochastic(S_smoothed, U_smoothed, US_smoothed, S2_smoothed, perc_left=None,
perc_right=5)
(gamma_init, _, _, _, _) = lin_reg_gamma_synthesis(Total_smoothed, New_smoothed, time, perc_right=5)
beta_init = gamma_init / gamma_k # gamma_k = gamma / beta
# Read raw counts
layers_raw = ["ul", "sl"]
UL_raw, SL_raw = (
subset_adata.layers[layers_raw[0]].T,
subset_adata.layers[layers_raw[1]].T,
)
# Read smoothed values based CSP type distribution for cell-specific parameter inference
UL_smoothed_CSP, SL_smoothed_CSP = (
subset_adata.layers['M_CSP_ul'].T,
subset_adata.layers['M_CSP_sl'].T,
)
# Parameters inference based on maximum likelihood estimation
cell_total = subset_adata.obs['initial_cell_size'].astype("float").values
# Parameter inference and RNA velocity
if assumption == 'steady_state':
method = 'CSP_Baseline'
gamma, select_genes, gamma_r2, alpha = MLE_Cell_Specific_Poisson_SS(Total_raw, New_raw, time, gamma_init,
cell_total, Total_smoothed, New_smoothed)
k = 1 - np.exp(-gamma[:, None] * time[None, :])
cell_wise_alpha = csr_matrix(gamma[:, None]).multiply(New_smoothed_CSP).multiply(1 / k) # cell-specific alpha
velocity_T = cell_wise_alpha - csr_matrix(gamma[:, None]).multiply(Total_smoothed)
adata.var['gamma'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma'][gene_indices] = gamma
adata.var['gamma_r2'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma_r2'][gene_indices] = gamma_r2
adata.var['alpha'] = np.zeros(adata.n_vars) * np.nan
adata.var['alpha'][gene_indices] = alpha
adata.var['select_genes'] = np.zeros(adata.n_vars, dtype=bool)
adata.var['select_genes'][gene_indices] = select_genes
adata.layers["velocity_T"] = sp.csr_matrix((adata.shape), dtype=np.float64)
velocity_T = velocity_T.T.tocsr() if sp.issparse(velocity_T) else sp.csr_matrix(velocity_T, dtype=np.float64).T
adata.layers["velocity_T"][:, gene_indices] = velocity_T
adata.layers["cell_wise_alpha"] = sp.csr_matrix((adata.shape), dtype=np.float64)
cell_wise_alpha = cell_wise_alpha.T.tocsr() if sp.issparse(cell_wise_alpha) else sp.csr_matrix(cell_wise_alpha, dtype=np.float64).T
adata.layers["cell_wise_alpha"][:, gene_indices] = cell_wise_alpha
else:
if method == 'CSP_Baseline':
gamma, select_genes, gamma_r2, alpha = MLE_Cell_Specific_Poisson(New_raw, time, gamma_init, cell_total,
Total_smoothed)
k = 1 - np.exp(-gamma[:, None] * time[None, :])
cell_wise_alpha = csr_matrix(gamma[:, None]).multiply(New_smoothed_CSP).multiply(1 / k) # cell-specific alpha
velocity_T = cell_wise_alpha - csr_matrix(gamma[:, None]).multiply(Total_smoothed)
adata.var['gamma'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma'][gene_indices] = gamma
adata.var['gamma_r2'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma_r2'][gene_indices] = gamma_r2
adata.var['alpha'] = np.zeros(adata.n_vars) * np.nan
adata.var['alpha'][gene_indices] = alpha
adata.var['select_genes'] = np.zeros(adata.n_vars, dtype=bool)
adata.var['select_genes'][gene_indices] = select_genes
adata.layers["velocity_T"] = sp.csr_matrix((adata.shape), dtype=np.float64)
velocity_T = velocity_T.T.tocsr() if sp.issparse(velocity_T) else sp.csr_matrix(velocity_T, dtype=np.float64).T
adata.layers["velocity_T"][:, gene_indices] = velocity_T
adata.layers["cell_wise_alpha"] = sp.csr_matrix((adata.shape), dtype=np.float64)
cell_wise_alpha = cell_wise_alpha.T.tocsr() if sp.issparse(cell_wise_alpha) else sp.csr_matrix(
cell_wise_alpha, dtype=np.float64).T
adata.layers["cell_wise_alpha"][:, gene_indices] = cell_wise_alpha
elif method == 'CSP_Splicing':
gamma_s, select_genes, beta, gamma_t, gamma_r2, alpha = MLE_Independent_Cell_Specific_Poisson(UL_raw,
SL_raw, time,
gamma_init,
beta_init,
cell_total,
Total_smoothed,
S_smoothed)
# Cell specific parameters (fixed gamma_s)
cell_wise_alpha, cell_wise_beta = Cell_Specific_Alpha_Beta(UL_smoothed_CSP, SL_smoothed_CSP, time, gamma_s,
beta)
velocity_T = cell_wise_alpha - csr_matrix(gamma_s[:, None]).multiply(S_smoothed)
velocity_S = cell_wise_beta.multiply(U_smoothed) - csr_matrix(gamma_s[:, None]).multiply(S_smoothed)
# # Cell specific parameters(fixed gamma_t)
# k = 1 - np.exp(-gamma_t[:, None] * time[None, :])
# cell_wise_alpha = csr_matrix(gamma_t[:, None]).multiply(UL_smoothed_CSP+SL_smoothed_CSP).multiply(1 / k)
# velocity_T = cell_wise_alpha - csr_matrix(gamma_t[:, None]).multiply(Total_smoothed)
adata.var['gamma'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma'][gene_indices] = gamma_t
adata.var['gamma_s'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma_s'][gene_indices] = gamma_s
adata.var['gamma_r2'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma_r2'][gene_indices] = gamma_r2
adata.var['alpha'] = np.zeros(adata.n_vars) * np.nan
adata.var['alpha'][gene_indices] = alpha
adata.var['beta'] = np.zeros(adata.n_vars) * np.nan
adata.var['beta'][gene_indices] = beta
adata.var['select_genes'] = np.zeros(adata.n_vars, dtype=bool)
adata.var['select_genes'][gene_indices] = select_genes
adata.layers["velocity_T"] = sp.csr_matrix((adata.shape), dtype=np.float64)
velocity_T = velocity_T.T.tocsr() if sp.issparse(velocity_T) else sp.csr_matrix(velocity_T, dtype=np.float64).T
adata.layers["velocity_T"][:, gene_indices] = velocity_T
adata.layers["velocity_S"] = sp.csr_matrix((adata.shape), dtype=np.float64)
velocity_S = velocity_S.T.tocsr() if sp.issparse(velocity_S) else sp.csr_matrix(velocity_S, dtype=np.float64).T
adata.layers["velocity_S"][:, gene_indices] = velocity_S
adata.layers["cell_wise_alpha"] = sp.csr_matrix((adata.shape), dtype=np.float64)
cell_wise_alpha = cell_wise_alpha.T.tocsr() if sp.issparse(cell_wise_alpha) else sp.csr_matrix(
cell_wise_alpha, dtype=np.float64).T
adata.layers["cell_wise_alpha"][:, gene_indices] = cell_wise_alpha
adata.layers["cell_wise_beta"] = sp.csr_matrix((adata.shape), dtype=np.float64)
cell_wise_beta = cell_wise_beta.T.tocsr() if sp.issparse(cell_wise_beta) else sp.csr_matrix(
cell_wise_beta, dtype=np.float64).T
adata.layers["cell_wise_alpha"][:, gene_indices] = cell_wise_beta
elif method == 'CSP_Switching':
gamma, prob_off, select_genes, gamma_r2, alpha = MLE_Cell_Specific_Zero_Inflated_Poisson(New_raw, time,
gamma_init,
cell_total)
alpha = alpha * (1 - prob_off)
k = 1 - np.exp(-gamma[:, None] * time[None, :])
cell_wise_alpha = csr_matrix(gamma[:, None]).multiply(New_smoothed_CSP).multiply(1 / k) # cell-specific alpha*(1-p_off)
velocity_T = cell_wise_alpha - csr_matrix(gamma[:, None]).multiply(Total_smoothed)
adata.var['gamma'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma'][gene_indices] = gamma
adata.var['gamma_r2'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma_r2'][gene_indices] = gamma_r2
adata.var['alpha'] = np.zeros(adata.n_vars) * np.nan
adata.var['alpha'][gene_indices] = alpha
adata.var['prob_off'] = np.zeros(adata.n_vars) * np.nan
adata.var['prob_off'][gene_indices] = prob_off
adata.var['select_genes'] = np.zeros(adata.n_vars, dtype=bool)
adata.var['select_genes'][gene_indices] = select_genes
adata.layers["velocity_T"] = sp.csr_matrix((adata.shape), dtype=np.float64)
velocity_T = velocity_T.T.tocsr() if sp.issparse(velocity_T) else sp.csr_matrix(velocity_T, dtype=np.float64).T
adata.layers["velocity_T"][:, gene_indices] = velocity_T
adata.layers["cell_wise_alpha"] = sp.csr_matrix((adata.shape), dtype=np.float64)
cell_wise_alpha = cell_wise_alpha.T.tocsr() if sp.issparse(cell_wise_alpha) else sp.csr_matrix(cell_wise_alpha, dtype=np.float64).T
adata.layers["cell_wise_alpha"][:, gene_indices] = cell_wise_alpha
def storm_one_shot_data(adata, use_genes=None, tkey='time', assumption='steady_state', method='CSP_Baseline'):
subset_adata = adata[:, use_genes].copy()
gene_indices = [list(adata.var_names).index(item) if item in list(adata.var_names) else -1 for item in
list(use_genes)]
time = subset_adata.obs[tkey]
# Initialization based on the steady-state assumption
if method is not 'CSP_Splicing':
layers_smoothed = ["M_t", "M_n"]
Total_smoothed, New_smoothed = (
subset_adata.layers[layers_smoothed[0]].T,
subset_adata.layers[layers_smoothed[1]].T,
)
(gamma_init, _, _, _, _,) = lin_reg_gamma_synthesis(Total_smoothed, New_smoothed, time, perc_right=5)
# Read raw counts
layers_raw = ["total", "new"]
Total_raw, New_raw = (
subset_adata.layers[layers_raw[0]].T,
subset_adata.layers[layers_raw[1]].T,
)
# Read smoothed values based CSP type distribution for cell-specific parameter inference
layers_smoothed_CSP = ["M_CSP_t", "M_CSP_n"]
Total_smoothed_CSP, New_smoothed_CSP = (
subset_adata.layers[layers_smoothed_CSP[0]].T,
subset_adata.layers[layers_smoothed_CSP[1]].T,
)
# Parameters inference based on maximum likelihood estimation
cell_total = subset_adata.obs['initial_cell_size'].astype("float").values
else:
# import scvelo as scv
# subset_adata.layers['Ms'] = subset_adata.layers['M_s']
# subset_adata.layers['Mu'] = subset_adata.layers['M_u']
# scv.tl.recover_dynamics(subset_adata, var_names='all')
scv_gamma = subset_adata.var.fit_gamma.values
scv_beta = subset_adata.var.fit_beta.values
scv_alpha = subset_adata.var.fit_alpha.values
scv_t_switch = subset_adata.var.fit_t_.values
scv_time = subset_adata.layers['fit_t'].T
layers_smoothed = ["M_u", "M_s", "M_t", "M_n"]
U_smoothed, S_smoothed, Total_smoothed, New_smoothed = (
subset_adata.layers[layers_smoothed[0]].T,
subset_adata.layers[layers_smoothed[1]].T,
subset_adata.layers[layers_smoothed[2]].T,
subset_adata.layers[layers_smoothed[3]].T,
)
(gamma_init, _, _, _, _) = lin_reg_gamma_synthesis(Total_smoothed, New_smoothed, time, perc_right=5)
# Read raw counts
layers_raw = ["ul", "sl"]
UL_raw, SL_raw = (
subset_adata.layers[layers_raw[0]].T,
subset_adata.layers[layers_raw[1]].T,
)
# Read smoothed values based CSP type distribution for cell-specific parameter inference
UL_smoothed_CSP, SL_smoothed_CSP = (
subset_adata.layers['M_CSP_ul'].T,
subset_adata.layers['M_CSP_sl'].T,
)
cell_total = subset_adata.obs['initial_cell_size'].astype("float").values
# Parameter inference and RNA velocity
if assumption == 'steady_state':
method = 'CSP_Baseline'
gamma, select_genes, gamma_r2, alpha = MLE_Cell_Specific_Poisson_SS(Total_raw, New_raw, time, gamma_init,
cell_total, Total_smoothed, New_smoothed)
k = 1 - np.exp(-gamma[:, None] * time[None, :])
cell_wise_alpha = csr_matrix(gamma[:, None]).multiply(New_smoothed_CSP).multiply(1 / k) # cell-specific alpha
velocity_T = cell_wise_alpha - csr_matrix(gamma[:, None]).multiply(Total_smoothed)
adata.var['gamma'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma'][gene_indices] = gamma
adata.var['gamma_r2'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma_r2'][gene_indices] = gamma_r2
adata.var['alpha'] = np.zeros(adata.n_vars) * np.nan
adata.var['alpha'][gene_indices] = alpha
adata.var['select_genes'] = np.zeros(adata.n_vars, dtype=bool)
adata.var['select_genes'][gene_indices] = select_genes
adata.layers["velocity_T"] = sp.csr_matrix((adata.shape), dtype=np.float64)
velocity_T = velocity_T.T.tocsr() if sp.issparse(velocity_T) else sp.csr_matrix(velocity_T, dtype=np.float64).T
adata.layers["velocity_T"][:, gene_indices] = velocity_T
adata.layers["cell_wise_alpha"] = sp.csr_matrix((adata.shape), dtype=np.float64)
cell_wise_alpha = cell_wise_alpha.T.tocsr() if sp.issparse(cell_wise_alpha) else sp.csr_matrix(cell_wise_alpha, dtype=np.float64).T
adata.layers["cell_wise_alpha"][:, gene_indices] = cell_wise_alpha
else:
method = 'CSP_Splicing'
alpha, beta, gamma_s, select_genes, gamma_t = MLE_ICSP_Without_SS(UL_raw, SL_raw, time, cell_total, scv_gamma,
scv_beta, U_smoothed, S_smoothed, gamma_init,
scv_t_switch, scv_time)
Select_SCV_Genes(subset_adata)
# # Cell specific parameters (fixed gamma_s)
# alpha, beta = CSP4ML.cell_specific_alpha_beta(UL_smoothed_CSP, SL_smoothed_CSP, time,
# gamma_s, beta)
# Cell specific parameters(fixed gamma_t)
k = 1 - np.exp(-gamma_t[:, None] * time[None, :])
cell_wise_alpha = csr_matrix(gamma_t[:, None]).multiply(UL_smoothed_CSP + SL_smoothed_CSP).multiply(1 / k)
velocity_T = cell_wise_alpha - csr_matrix(gamma_s[:, None]).multiply(S_smoothed)
velocity_S = csr_matrix(beta[:, None]).multiply(U_smoothed) - csr_matrix(gamma_s[:, None]).multiply(S_smoothed)
adata.var['gamma'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma'][gene_indices] = gamma_t
adata.var['gamma_s'] = np.zeros(adata.n_vars) * np.nan
adata.var['gamma_s'][gene_indices] = gamma_s
adata.var['select_genes'] = np.zeros(adata.n_vars, dtype=bool)
adata.var['select_genes'][gene_indices] = select_genes
adata.var['alpha'] = np.zeros(adata.n_vars) * np.nan
adata.var['alpha'][gene_indices] = alpha
adata.var['beta'] = np.zeros(adata.n_vars) * np.nan
adata.var['beta'][gene_indices] = beta
adata.var['no_linear_r2'] = np.zeros(adata.n_vars) * np.nan
adata.var['no_linear_r2'][gene_indices] = subset_adata.var['no_linear_r2'].copy()
adata.layers["velocity_T"] = sp.csr_matrix((adata.shape), dtype=np.float64)
velocity_T = velocity_T.T.tocsr() if sp.issparse(velocity_T) else sp.csr_matrix(velocity_T, dtype=np.float64).T
adata.layers["velocity_T"][:, gene_indices] = velocity_T
adata.layers["velocity_S"] = sp.csr_matrix((adata.shape), dtype=np.float64)
velocity_S = velocity_S.T.tocsr() if sp.issparse(velocity_S) else sp.csr_matrix(velocity_S, dtype=np.float64).T
adata.layers["velocity_S"][:, gene_indices] = velocity_S
adata.layers["cell_wise_alpha"] = sp.csr_matrix((adata.shape), dtype=np.float64)
cell_wise_alpha = cell_wise_alpha.T.tocsr() if sp.issparse(cell_wise_alpha) else sp.csr_matrix(
cell_wise_alpha, dtype=np.float64).T
adata.layers["cell_wise_alpha"][:, gene_indices] = cell_wise_alpha