├── .gitignore ├── LICENSE ├── Readme.md ├── cinemaot ├── __init__.py ├── benchmark.py ├── cinemaot.py ├── sinkhorn_knopp.py └── utils.py ├── cinemaot_tutorial.ipynb ├── pyproject.toml ├── setup.cfg └── simulation.py /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | __pycache__ 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published by 637 | the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . -------------------------------------------------------------------------------- /Readme.md: -------------------------------------------------------------------------------- 1 | # Causal INdependent Effect Module Attribution + Optimal Transport (CINEMA-OT) 2 | 3 | CINEMA-OT is a **causal** framework for perturbation effect analysis to identify **individual treatment effects** and **synergy** at the **single cell** level. 4 | 5 | **Note**: Newer versions of CINEMA-OT are maintained at [Pertpy](https://github.com/scverse/pertpy). 6 | 7 | ## Architecture 8 | 9 | image 10 | 11 | 12 | Read our preprint on bioRxiv: 13 | 14 | - Dong, Mingze, et al. "Causal identification of single-cell experimental perturbation effects with CINEMA-OT". bioRxiv (2022). 15 | [https://www.biorxiv.org/content/10.1101/2022.07.31.502173v3](https://www.biorxiv.org/content/10.1101/2022.07.31.502173v3) 16 | 17 | ## System requirements 18 | ### Hardware requirements 19 | `CINEMA-OT` requires only a standard computer with enough RAM to perform in-memory computations. 20 | ### OS requirements 21 | The `CINEMA-OT` package is supported for all OS in principle. The package has been tested on the following systems: 22 | * macOS: Monterey (12.4) 23 | * Linux: RHEL Maipo (7.9), Ubantu (18.04) 24 | ### Dependencies 25 | See `setup.cfg` for details. 26 | 27 | ## Installation 28 | CINEMA-OT requires `python` version 3.7+. Install directly from pip with: 29 | 30 | pip install cinemaot 31 | 32 | The installation should take no more than a few minutes on a normal desktop computer. 33 | 34 | 35 | ## Usage 36 | 37 | For detailed usage, follow our step-by-step tutorial here: 38 | 39 | - [Getting Started with CINEMA-OT](https://github.com/vandijklab/CINEMA-OT/blob/main/cinemaot_tutorial.ipynb) 40 | 41 | Download the data used for the tutorial here: 42 | 43 | - [Ex vivo stimulation of human peripheral blood mononuclear cells (PBMC) with interferon](https://drive.google.com/file/d/1A3rNdgfiXFWhCUOoUfJ-AiY7AAOU0Ie3/view?usp=sharing) 44 | -------------------------------------------------------------------------------- /cinemaot/__init__.py: -------------------------------------------------------------------------------- 1 | """CINEMA-OT - Causal Independent Effect Module Attribution + Optimal Transport, for single-cell level treatment effect identification""" 2 | __version__ = "0.0.3" 3 | from . import cinemaot -------------------------------------------------------------------------------- /cinemaot/benchmark.py: -------------------------------------------------------------------------------- 1 | import scib 2 | import numpy as np 3 | import pandas as pd 4 | import scanpy as sc 5 | from sklearn.neighbors import NearestNeighbors 6 | from scipy.sparse import csr_matrix 7 | 8 | # In this newer version we use the Python implementation of xicor 9 | # import rpy2.robjects as ro 10 | # import rpy2.robjects.numpy2ri 11 | # import rpy2.robjects.pandas2ri 12 | # from rpy2.robjects.packages import importr 13 | # rpy2.robjects.numpy2ri.activate() 14 | # rpy2.robjects.pandas2ri.activate() 15 | 16 | from scipy.stats.stats import pearsonr 17 | from sklearn.decomposition import FastICA 18 | from sklearn.metrics import roc_curve 19 | from sklearn.metrics import auc 20 | from sklearn.metrics import pairwise_distances 21 | from . import sinkhorn_knopp as skp 22 | 23 | from sklearn.preprocessing import OneHotEncoder 24 | from scipy.stats import ttest_1samp 25 | import harmonypy as hm 26 | 27 | def mixscape(adata,obs_label, ref_label, expr_label, nn=20, return_te = True): 28 | X_pca1 = adata.obsm['X_pca'][adata.obs[obs_label]==expr_label,:] 29 | X_pca2 = adata.obsm['X_pca'][adata.obs[obs_label]==ref_label,:] 30 | nbrs = NearestNeighbors(n_neighbors=nn, algorithm='ball_tree').fit(X_pca1) 31 | mixscape_pca = adata.obsm['X_pca'].copy() 32 | mixscapematrix = nbrs.kneighbors_graph(X_pca2).toarray() 33 | mixscape_pca[adata.obs[obs_label]==ref_label,:] = np.dot(mixscapematrix, mixscape_pca[adata.obs[obs_label]==expr_label,:])/20 34 | if return_te: 35 | te2 = adata.X[adata.obs[obs_label]==ref_label,:] - (mixscapematrix/np.sum(mixscapematrix,axis=1)[:,None]) @ (adata.X[adata.obs[obs_label]==expr_label,:]) 36 | return mixscape_pca, mixscapematrix, te2 37 | else: 38 | return mixscape_pca, mixscapematrix 39 | 40 | def harmony_mixscape(adata,obs_label, ref_label, expr_label,nn=20, return_te = True): 41 | meta_data = adata.obs 42 | data_mat=adata.obsm['X_pca'] 43 | vars_use=[obs_label] 44 | ho = hm.run_harmony(data_mat, meta_data,vars_use) 45 | hmdata = ho.Z_corr.T 46 | X_pca1 = hmdata[adata.obs[obs_label]==expr_label,:] 47 | X_pca2 = hmdata[adata.obs[obs_label]==ref_label,:] 48 | nbrs = NearestNeighbors(n_neighbors=nn, algorithm='ball_tree').fit(X_pca1) 49 | hmmatrix = nbrs.kneighbors_graph(X_pca2).toarray() 50 | if return_te: 51 | te2 = adata.X[adata.obs[obs_label]==ref_label,:] - np.matmul(hmmatrix/np.sum(hmmatrix,axis=1)[:,None],adata.X[adata.obs[obs_label]==expr_label,:]) 52 | return hmdata, hmmatrix, te2 53 | else: 54 | return hmdata, hmmatrix 55 | 56 | def OT(adata,obs_label, ref_label, expr_label,thres=0.01, return_te = True): 57 | cf1 = adata.obsm['X_pca'][adata.obs[obs_label]==expr_label,0:20] 58 | cf2 = adata.obsm['X_pca'][adata.obs[obs_label]==ref_label,0:20] 59 | r = np.zeros([cf1.shape[0],1]) 60 | c = np.zeros([cf2.shape[0],1]) 61 | r[:,0] = 1/cf1.shape[0] 62 | c[:,0] = 1/cf2.shape[0] 63 | sk = skp.SinkhornKnopp(setr=r,setc=c,epsilon=1e-2) 64 | dis = pairwise_distances(cf1,cf2) 65 | e = thres * adata.obsm['X_pca'].shape[1] 66 | af = np.exp(-dis * dis / e) 67 | ot = sk.fit(af).T 68 | OT_pca = adata.obsm['X_pca'].copy() 69 | OT_pca[adata.obs[obs_label]==ref_label,:] = np.matmul(ot/np.sum(ot,axis=1)[:,None],OT_pca[adata.obs[obs_label]==expr_label,:]) 70 | if return_te: 71 | te2 = adata.X[adata.obs[obs_label]==ref_label,:] - np.matmul(ot/np.sum(ot,axis=1)[:,None],adata.X[adata.obs[obs_label]==expr_label,:]) 72 | return OT_pca, ot, te2 73 | else: 74 | return OT_pca, ot 75 | 76 | 77 | def evaluate_cinema(matrix,ite,gt,gite): 78 | #includes four statistics: knn-AUC, treatment effect pearson correlation, treatment effect spearman correlation, ttest AUC 79 | aucdata = np.zeros(gt.shape[0]) 80 | corr_ = np.zeros(gt.shape[0]) 81 | scorr_ = np.zeros(gt.shape[0]) 82 | #genesig = np.zeros(gite.shape[1]) 83 | for i in range(gt.shape[0]): 84 | fpr, tpr, thres = roc_curve(gt[i,:],matrix[i,:]) 85 | aucdata[i] = auc(fpr,tpr) 86 | for i in range(ite.shape[0]): 87 | corr_[i], pval = pearsonr(ite[i,1000:],gite[i,1000:]) 88 | scorr_[i],pval = spearmanr(ite[i,1000:],gite[i,1000:]) 89 | corr_[i], pval = pearsonr(ite[i,:],gite[i,:]) 90 | scorr_[i],pval = spearmanr(ite[i,:],gite[i,:]) 91 | return np.median(aucdata), np.median(corr_), np.median(scorr_) 92 | 93 | def evaluate_batch(sig, adata,obs_label, label, continuity,asw=True,silhouette=True,graph_conn=True,pcr=True,nmi=True,ari=True,diff_coefs=False): 94 | #Label is a list!!! 95 | newsig = sc.AnnData(X=sig, obs = adata.obs) 96 | sc.pp.pca(newsig,n_comps=min(15,newsig.X.shape[1]-1)) 97 | #newsig.obsm['X_pca'] = newsig.X 98 | k0=15 99 | sc.pp.neighbors(newsig, n_neighbors=k0) 100 | sc.tl.diffmap(newsig, n_comps=min(15,newsig.X.shape[1]-1)) 101 | eigen = newsig.obsm['X_diffmap'] 102 | #newsig_nbrs = NearestNeighbors(n_neighbors=10, algorithm='ball_tree').fit(newsig.X) 103 | #newsig_con = newsig_nbrs.kneighbors_graph(newsig.X) 104 | #newsig.obsp['connectivities'] = newsig_con 105 | newsig_metrics = scib.metrics.metrics(adata,newsig,obs_label,label[0], 106 | isolated_labels_asw_= asw, 107 | graph_conn_= graph_conn, 108 | silhouette_ = silhouette, 109 | nmi_=nmi, 110 | ari_=ari, 111 | pcr_=pcr) 112 | if diff_coefs: 113 | for i in range(len(label)): 114 | steps = adata.obs[label[i]].values 115 | #also we test max correlation to see strong functional dependence between steps and signals, for each state_group population 116 | if continuity[i]: 117 | xi = np.zeros(eigen.shape[1]) 118 | #pval = np.zeros(eigen.shape[1]) 119 | j = 0 120 | for source_row in eigen.T: 121 | #rresults = xicor(ro.FloatVector(source_row), ro.FloatVector(steps), pvalue = True) 122 | xi_obj = Xi(source_row,steps.astype(np.float)) 123 | xi[j] = xi_obj.correlation 124 | j = j+1 125 | maxcoef = np.max(xi) 126 | #newsig_metrics.rename(index={'trajectory':'trajectory_coef'},inplace=True) 127 | #newsig_metrics.iloc[13,0] = np.max(xi) 128 | newsig_metrics.loc[label[i]] = maxcoef 129 | else: 130 | encoder = OneHotEncoder(sparse=False) 131 | onehot = encoder.fit_transform(np.array(adata.obs[label[i]].values.tolist()).reshape(-1, 1)) 132 | yi = np.zeros([onehot.shape[1],eigen.shape[1]]) 133 | k = 0 134 | #ind = onehot.T[0] * 0 135 | m = onehot.T.shape[0] 136 | for indicator in onehot.T[0:m-1]: 137 | j = 0 138 | #ind = ind + indicator 139 | for source_row in eigen.T: 140 | xi_obj = Xi(source_row,indicator*1) 141 | yi[k,j] = xi_obj.correlation 142 | j = j+1 143 | k = k+1 144 | 145 | #newsig_metrics.rename(index={'hvg_overlap':'state_coef'},inplace=True) 146 | #newsig_metrics.iloc[12,0] = np.mean(np.max(yi,axis=1)) 147 | newsig_metrics.loc[label[i]] = np.mean(np.max(yi,axis=1)) 148 | 149 | return newsig_metrics 150 | 151 | 152 | class Xi: 153 | """ 154 | x and y are the data vectors 155 | """ 156 | 157 | def __init__(self, x, y): 158 | 159 | self.x = x 160 | self.y = y 161 | 162 | @property 163 | def sample_size(self): 164 | return len(self.x) 165 | 166 | @property 167 | def x_ordered_rank(self): 168 | # PI is the rank vector for x, with ties broken at random 169 | # Not mine: source (https://stackoverflow.com/a/47430384/1628971) 170 | # random shuffling of the data - reason to use random.choice is that 171 | # pd.sample(frac=1) uses the same randomizing algorithm 172 | len_x = len(self.x) 173 | randomized_indices = np.random.choice(np.arange(len_x), len_x, replace=False) 174 | randomized = [self.x[idx] for idx in randomized_indices] 175 | # same as pandas rank method 'first' 176 | rankdata = ss.rankdata(randomized, method="ordinal") 177 | # Reindexing based on pairs of indices before and after 178 | unrandomized = [ 179 | rankdata[j] for i, j in sorted(zip(randomized_indices, range(len_x))) 180 | ] 181 | return unrandomized 182 | 183 | @property 184 | def y_rank_max(self): 185 | # f[i] is number of j s.t. y[j] <= y[i], divided by n. 186 | return ss.rankdata(self.y, method="max") / self.sample_size 187 | 188 | @property 189 | def g(self): 190 | # g[i] is number of j s.t. y[j] >= y[i], divided by n. 191 | return ss.rankdata([-i for i in self.y], method="max") / self.sample_size 192 | 193 | @property 194 | def x_ordered(self): 195 | # order of the x's, ties broken at random. 196 | return np.argsort(self.x_ordered_rank) 197 | 198 | @property 199 | def x_rank_max_ordered(self): 200 | x_ordered_result = self.x_ordered 201 | y_rank_max_result = self.y_rank_max 202 | # Rearrange f according to ord. 203 | return [y_rank_max_result[i] for i in x_ordered_result] 204 | 205 | @property 206 | def mean_absolute(self): 207 | x1 = self.x_rank_max_ordered[0 : (self.sample_size - 1)] 208 | x2 = self.x_rank_max_ordered[1 : self.sample_size] 209 | 210 | return ( 211 | np.mean( 212 | np.abs( 213 | [ 214 | x - y 215 | for x, y in zip( 216 | x1, 217 | x2, 218 | ) 219 | ] 220 | ) 221 | ) 222 | * (self.sample_size - 1) 223 | / (2 * self.sample_size) 224 | ) 225 | 226 | @property 227 | def inverse_g_mean(self): 228 | gvalue = self.g 229 | return np.mean(gvalue * (1 - gvalue)) 230 | 231 | @property 232 | def correlation(self): 233 | """xi correlation""" 234 | return 1 - self.mean_absolute / self.inverse_g_mean 235 | 236 | @classmethod 237 | def xi(cls, x, y): 238 | return cls(x, y) 239 | 240 | def pval_asymptotic(self, ties=False, nperm=1000): 241 | """ 242 | Returns p values of the correlation 243 | Args: 244 | ties: boolean 245 | If ties is true, the algorithm assumes that the data has ties 246 | and employs the more elaborated theory for calculating 247 | the P-value. Otherwise, it uses the simpler theory. There is 248 | no harm in setting tiles True, even if there are no ties. 249 | nperm: int 250 | The number of permutations for the permutation test, if needed. 251 | default 1000 252 | Returns: 253 | p value 254 | """ 255 | # If there are no ties, return xi and theoretical P-value: 256 | 257 | if ties: 258 | return 1 - ss.norm.cdf( 259 | np.sqrt(self.sample_size) * self.correlation / np.sqrt(2 / 5) 260 | ) 261 | 262 | # If there are ties, and the theoretical method 263 | # is to be used for calculation P-values: 264 | # The following steps calculate the theoretical variance 265 | # in the presence of ties: 266 | sorted_ordered_x_rank = sorted(self.x_rank_max_ordered) 267 | 268 | ind = [i + 1 for i in range(self.sample_size)] 269 | ind2 = [2 * self.sample_size - 2 * ind[i - 1] + 1 for i in ind] 270 | 271 | a = ( 272 | np.mean([i * j * j for i, j in zip(ind2, sorted_ordered_x_rank)]) 273 | / self.sample_size 274 | ) 275 | 276 | c = ( 277 | np.mean([i * j for i, j in zip(ind2, sorted_ordered_x_rank)]) 278 | / self.sample_size 279 | ) 280 | 281 | cq = np.cumsum(sorted_ordered_x_rank) 282 | 283 | m = [ 284 | (i + (self.sample_size - j) * k) / self.sample_size 285 | for i, j, k in zip(cq, ind, sorted_ordered_x_rank) 286 | ] 287 | 288 | b = np.mean([np.square(i) for i in m]) 289 | v = (a - 2 * b + np.square(c)) / np.square(self.inverse_g_mean) 290 | 291 | return 1 - ss.norm.cdf( 292 | np.sqrt(self.sample_size) * self.correlation / np.sqrt(v) 293 | ) -------------------------------------------------------------------------------- /cinemaot/cinemaot.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import scanpy as sc 4 | from anndata import AnnData 5 | from . import sinkhorn_knopp as skp 6 | #from . import utils 7 | from scipy.sparse import issparse 8 | from sklearn.neighbors import NearestNeighbors 9 | import scipy.stats as ss 10 | 11 | # In this newer version we use the Python implementation of xicor 12 | # import rpy2.robjects as ro 13 | # import rpy2.robjects.numpy2ri 14 | # import rpy2.robjects.pandas2ri 15 | # from rpy2.robjects.packages import importr 16 | # rpy2.robjects.numpy2ri.activate() 17 | # rpy2.robjects.pandas2ri.activate() 18 | 19 | 20 | # Instead of projecting the whole count matrix, we use the pca result of projected ICA components to stablize the noise 21 | # returning an anndata object 22 | # Detecting differently expressed genes: G = A + Z + AZ + e by NB regression. Significant coefficient before AZ means conditional-specific effect 23 | # Further exclusion of false positives may be removed by permutation (as in PseudotimeDE) 24 | 25 | #import ot 26 | 27 | import statsmodels.api as sm 28 | from sklearn.linear_model import LinearRegression 29 | 30 | from sklearn.decomposition import FastICA 31 | import sklearn.metrics 32 | 33 | 34 | def cinemaot_unweighted(adata,obs_label,ref_label,expr_label,dim=20,thres=0.15,smoothness=1e-4,eps=1e-3,mode='parametric',marker=None,preweight_label=None): 35 | """ 36 | Parameters 37 | ---------- 38 | adata: 'AnnData' 39 | An anndata object containing the whole gene count matrix and an observation index for treatments. It should be preprocessed before input. 40 | obs_label: 'str' 41 | A string for indicating the treatment column name in adata.obs. 42 | ref_label: 'str' 43 | A string for indicating the control group in adata.obs.values. 44 | expr_label: 'str' 45 | A string for indicating the experiment group in adata.obs.values. 46 | dim: 'int' 47 | The number of independent components. 48 | thres: 'float' 49 | The threshold for setting the Chatterjee coefficent for confounder separation. 50 | smoothness: 'float' 51 | The parameter for setting the smoothness of entropy-regularized optimal transport. Should be set as a small value above zero! 52 | eps: 'float' 53 | The parameter for stop condition of OT convergence. 54 | mode: 'str' 55 | If mode is 'parametric', return standard differential matrices. If it's non-parametric, we return expr cells' weighted quantile. 56 | Return 57 | ---------- 58 | cf: 'numpy.ndarray' 59 | Confounder components, of shape (n_cells,n_components). 60 | ot: 'numpy.ndarray' 61 | Transport map across control and experimental conditions. 62 | te2: 'numpy.ndarray' 63 | Single-cell differential expression for each cell in control condition, of shape (n_refcells, n_genes). 64 | """ 65 | if dim is None: 66 | sk = skp.SinkhornKnopp() 67 | c = 0.5 68 | data=adata.X 69 | vm = (1e-3 + data + c * data * data)/(1+c) 70 | P = sk.fit(vm) 71 | wm = np.dot(np.dot(np.sqrt(sk._D1),vm),np.sqrt(sk._D2)) 72 | u,s,vt = np.linalg.svd(wm) 73 | dim = np.min(sum(s > (np.sqrt(data.shape[0])+np.sqrt(data.shape[1]))),adata.obsm['X_pca'].shape[1]) 74 | 75 | 76 | transformer = FastICA(n_components=dim, random_state=0,whiten="arbitrary-variance") 77 | X_transformed = transformer.fit_transform(adata.obsm['X_pca'][:,:dim]) 78 | #importr("XICOR") 79 | #xicor = ro.r["xicor"] 80 | groupvec = (adata.obs[obs_label]==ref_label *1).values #control 81 | xi = np.zeros(dim) 82 | #pval = np.zeros(dim) 83 | j = 0 84 | for source_row in X_transformed.T: 85 | xi_obj = Xi(source_row,groupvec*1) 86 | #rresults = xicor(ro.FloatVector(source_row), ro.FloatVector(groupvec), pvalue = True) 87 | #xi[j] = np.array(rresults.rx2("xi"))[0] 88 | xi[j] = xi_obj.correlation 89 | #pval[j] = np.array(rresults.rx2("pval"))[0] 90 | j = j+1 91 | cf = X_transformed[:,xi (np.sqrt(data.shape[0])+np.sqrt(data.shape[1]))) 199 | 200 | sk = skp.SinkhornKnopp() 201 | adata_ = adata[adata.obs[obs_label].isin([expr_label,ref_label])].copy() 202 | if use_rep is None: 203 | X_pca1 = adata_.obsm['X_pca'][adata_.obs[obs_label]==expr_label,:] 204 | X_pca2 = adata_.obsm['X_pca'][adata_.obs[obs_label]==ref_label,:] 205 | nbrs = NearestNeighbors(n_neighbors=k, algorithm='ball_tree').fit(X_pca1) 206 | mixscape_pca = adata.obsm['X_pca'].copy() 207 | mixscapematrix = nbrs.kneighbors_graph(X_pca2).toarray() 208 | mixscape_pca[adata_.obs[obs_label]==ref_label,:] = np.dot(mixscapematrix, mixscape_pca[adata_.obs[obs_label]==expr_label,:])/k 209 | 210 | adata_.obsm['X_mpca'] = mixscape_pca 211 | sc.pp.neighbors(adata_,use_rep='X_mpca') 212 | 213 | else: 214 | sc.pp.neighbors(adata_,use_rep=use_rep) 215 | sc.tl.leiden(adata_,resolution=resolution) 216 | 217 | z = np.zeros(adata_.shape[0]) + 1 218 | 219 | j = 0 220 | 221 | for i in adata_.obs['leiden'].cat.categories: 222 | if adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)].shape[0] >= adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)].shape[0]: 223 | z[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)] = adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)].shape[0] / adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)].shape[0] 224 | if j == 0: 225 | idx = sc.pp.subsample(adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)],n_obs = adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)].shape[0],copy=True).obs.index 226 | idx = idx.append(adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)].obs.index) 227 | j = j + 1 228 | else: 229 | idx_tmp = sc.pp.subsample(adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)],n_obs = adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)].shape[0],copy=True).obs.index 230 | idx_tmp = idx_tmp.append(adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)].obs.index) 231 | idx = idx.append(idx_tmp) 232 | else: 233 | z[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)] = adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)].shape[0] / adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)].shape[0] 234 | if j == 0: 235 | idx = sc.pp.subsample(adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)],n_obs = adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)].shape[0],copy=True).obs.index 236 | idx = idx.append(adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)].obs.index) 237 | j = j + 1 238 | else: 239 | idx_tmp = sc.pp.subsample(adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==expr_label)],n_obs = adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)].shape[0],copy=True).obs.index 240 | idx_tmp = idx_tmp.append(adata_[(adata_.obs['leiden']==i) & (adata_.obs[obs_label]==ref_label)].obs.index) 241 | idx = idx.append(idx_tmp) 242 | 243 | transformer = FastICA(n_components=dim, random_state=0, whiten="arbitrary-variance") 244 | X_transformed = transformer.fit_transform(adata_[idx].obsm['X_pca'][:,:dim]) 245 | #importr("XICOR") 246 | #xicor = ro.r["xicor"] 247 | groupvec = (adata_[idx].obs[obs_label]==ref_label *1).values #control 248 | xi = np.zeros(dim) 249 | #pval = np.zeros(dim) 250 | j = 0 251 | for source_row in X_transformed.T: 252 | xi_obj = Xi(source_row,groupvec*1) 253 | #rresults = xicor(ro.FloatVector(source_row), ro.FloatVector(groupvec), pvalue = True) 254 | #xi[j] = np.array(rresults.rx2("xi"))[0] 255 | xi[j] = xi_obj.correlation 256 | #pval[j] = np.array(rresults.rx2("pval"))[0] 257 | j = j+1 258 | 259 | cf = transformer.transform(adata_.obsm['X_pca'][:,:dim])[:,xi0)>=n_cells: 368 | glm_binom = sm.GLM(adata.raw.X[:,i].toarray()[:,0], X, family=sm.families.Poisson()) 369 | try: 370 | res = glm_binom.fit(tol=1e-4) 371 | pvalue[i] = np.min(res.pvalues[cf.shape[1]+2:]) 372 | effectsize[i] = res.params[np.argmin(res.pvalues[cf.shape[1]+2:])] 373 | except: 374 | pvalue[i] = 0 375 | effectsize[i] = 0 376 | 377 | return effectsize, pvalue 378 | 379 | 380 | def attribution_scatter(adata,obs_label,control_label,expr_label,use_raw=True): 381 | cf = adata.obsm['cf'] 382 | if use_raw: 383 | Y0 = adata.raw.X.toarray()[adata.obs[obs_label]==control_label,:] 384 | Y1 = adata.raw.X.toarray()[adata.obs[obs_label]==expr_label,:] 385 | else: 386 | Y0 = adata.X.toarray()[adata.obs[obs_label]==control_label,:] 387 | Y1 = adata.X.toarray()[adata.obs[obs_label]==expr_label,:] 388 | X0 = cf[adata.obs[obs_label]==control_label,:] 389 | X1 = cf[adata.obs[obs_label]==expr_label,:] 390 | ols0 = LinearRegression() 391 | ols0.fit(X0,Y0) 392 | ols1 = LinearRegression() 393 | ols1.fit(X1,Y1) 394 | c0 = ols0.predict(X0) - np.mean(ols0.predict(X0),axis=0) 395 | c1 = ols1.predict(X1) - np.mean(ols1.predict(X1),axis=0) 396 | e0 = Y0 - ols0.predict(X0) 397 | e1 = Y1 - ols1.predict(X1) 398 | #c_effect = np.mean(np.abs(ols1.coef_)+1e-6,axis=1) / np.mean(np.abs(ols0.coef_)+1e-6,axis=1) 399 | c_effect = (np.linalg.norm(c1,axis=0)+1e-6)/(np.linalg.norm(c0,axis=0)+1e-6) 400 | s_effect = (np.linalg.norm(e1,axis=0)+1e-6)/(np.linalg.norm(e0,axis=0)+1e-6) 401 | return c_effect, s_effect 402 | 403 | 404 | class Xi: 405 | """ 406 | x and y are the data vectors 407 | """ 408 | 409 | def __init__(self, x, y): 410 | 411 | self.x = x 412 | self.y = y 413 | 414 | @property 415 | def sample_size(self): 416 | return len(self.x) 417 | 418 | @property 419 | def x_ordered_rank(self): 420 | # PI is the rank vector for x, with ties broken at random 421 | # Not mine: source (https://stackoverflow.com/a/47430384/1628971) 422 | # random shuffling of the data - reason to use random.choice is that 423 | # pd.sample(frac=1) uses the same randomizing algorithm 424 | len_x = len(self.x) 425 | randomized_indices = np.random.choice(np.arange(len_x), len_x, replace=False) 426 | randomized = [self.x[idx] for idx in randomized_indices] 427 | # same as pandas rank method 'first' 428 | rankdata = ss.rankdata(randomized, method="ordinal") 429 | # Reindexing based on pairs of indices before and after 430 | unrandomized = [ 431 | rankdata[j] for i, j in sorted(zip(randomized_indices, range(len_x))) 432 | ] 433 | return unrandomized 434 | 435 | @property 436 | def y_rank_max(self): 437 | # f[i] is number of j s.t. y[j] <= y[i], divided by n. 438 | return ss.rankdata(self.y, method="max") / self.sample_size 439 | 440 | @property 441 | def g(self): 442 | # g[i] is number of j s.t. y[j] >= y[i], divided by n. 443 | return ss.rankdata([-i for i in self.y], method="max") / self.sample_size 444 | 445 | @property 446 | def x_ordered(self): 447 | # order of the x's, ties broken at random. 448 | return np.argsort(self.x_ordered_rank) 449 | 450 | @property 451 | def x_rank_max_ordered(self): 452 | x_ordered_result = self.x_ordered 453 | y_rank_max_result = self.y_rank_max 454 | # Rearrange f according to ord. 455 | return [y_rank_max_result[i] for i in x_ordered_result] 456 | 457 | @property 458 | def mean_absolute(self): 459 | x1 = self.x_rank_max_ordered[0 : (self.sample_size - 1)] 460 | x2 = self.x_rank_max_ordered[1 : self.sample_size] 461 | 462 | return ( 463 | np.mean( 464 | np.abs( 465 | [ 466 | x - y 467 | for x, y in zip( 468 | x1, 469 | x2, 470 | ) 471 | ] 472 | ) 473 | ) 474 | * (self.sample_size - 1) 475 | / (2 * self.sample_size) 476 | ) 477 | 478 | @property 479 | def inverse_g_mean(self): 480 | gvalue = self.g 481 | return np.mean(gvalue * (1 - gvalue)) 482 | 483 | @property 484 | def correlation(self): 485 | """xi correlation""" 486 | return 1 - self.mean_absolute / self.inverse_g_mean 487 | 488 | @classmethod 489 | def xi(cls, x, y): 490 | return cls(x, y) 491 | 492 | def pval_asymptotic(self, ties=False, nperm=1000): 493 | """ 494 | Returns p values of the correlation 495 | Args: 496 | ties: boolean 497 | If ties is true, the algorithm assumes that the data has ties 498 | and employs the more elaborated theory for calculating 499 | the P-value. Otherwise, it uses the simpler theory. There is 500 | no harm in setting tiles True, even if there are no ties. 501 | nperm: int 502 | The number of permutations for the permutation test, if needed. 503 | default 1000 504 | Returns: 505 | p value 506 | """ 507 | # If there are no ties, return xi and theoretical P-value: 508 | 509 | if ties: 510 | return 1 - ss.norm.cdf( 511 | np.sqrt(self.sample_size) * self.correlation / np.sqrt(2 / 5) 512 | ) 513 | 514 | # If there are ties, and the theoretical method 515 | # is to be used for calculation P-values: 516 | # The following steps calculate the theoretical variance 517 | # in the presence of ties: 518 | sorted_ordered_x_rank = sorted(self.x_rank_max_ordered) 519 | 520 | ind = [i + 1 for i in range(self.sample_size)] 521 | ind2 = [2 * self.sample_size - 2 * ind[i - 1] + 1 for i in ind] 522 | 523 | a = ( 524 | np.mean([i * j * j for i, j in zip(ind2, sorted_ordered_x_rank)]) 525 | / self.sample_size 526 | ) 527 | 528 | c = ( 529 | np.mean([i * j for i, j in zip(ind2, sorted_ordered_x_rank)]) 530 | / self.sample_size 531 | ) 532 | 533 | cq = np.cumsum(sorted_ordered_x_rank) 534 | 535 | m = [ 536 | (i + (self.sample_size - j) * k) / self.sample_size 537 | for i, j, k in zip(cq, ind, sorted_ordered_x_rank) 538 | ] 539 | 540 | b = np.mean([np.square(i) for i in m]) 541 | v = (a - 2 * b + np.square(c)) / np.square(self.inverse_g_mean) 542 | 543 | return 1 - ss.norm.cdf( 544 | np.sqrt(self.sample_size) * self.correlation / np.sqrt(v) 545 | ) -------------------------------------------------------------------------------- /cinemaot/sinkhorn_knopp.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | import numpy as np 4 | 5 | 6 | class SinkhornKnopp: 7 | """ 8 | Sinkhorn Knopp Algorithm 9 | 10 | Takes a non-negative square matrix P, where P =/= 0 11 | and iterates through Sinkhorn Knopp's algorithm 12 | to convert P to a doubly stochastic matrix. 13 | Guaranteed convergence if P has total support. 14 | 15 | For reference see original paper: 16 | http://msp.org/pjm/1967/21-2/pjm-v21-n2-p14-s.pdf 17 | 18 | Parameters 19 | ---------- 20 | max_iter : int, default=1000 21 | The maximum number of iterations. 22 | 23 | epsilon : float, default=1e-3 24 | Metric used to compute the stopping condition, 25 | which occurs if all the row and column sums are 26 | within epsilon of 1. This should be a very small value. 27 | Epsilon must be between 0 and 1. 28 | 29 | Attributes 30 | ---------- 31 | _max_iter : int, default=1000 32 | User defined parameter. See above. 33 | 34 | _epsilon : float, default=1e-3 35 | User defined paramter. See above. 36 | 37 | _stopping_condition: string 38 | Either "max_iter", "epsilon", or None, which is a 39 | description of why the algorithm stopped iterating. 40 | 41 | _iterations : int 42 | The number of iterations elapsed during the algorithm's 43 | run-time. 44 | 45 | _D1 : 2d-array 46 | Diagonal matrix obtained after a stopping condition was met 47 | so that _D1.dot(P).dot(_D2) is close to doubly stochastic. 48 | 49 | _D2 : 2d-array 50 | Diagonal matrix obtained after a stopping condition was met 51 | so that _D1.dot(P).dot(_D2) is close to doubly stochastic. 52 | 53 | Example 54 | ------- 55 | 56 | .. code-block:: python 57 | >>> import numpy as np 58 | >>> from sinkhorn_knopp import sinkhorn_knopp as skp 59 | >>> sk = skp.SinkhornKnopp() 60 | >>> P = [[.011, .15], [1.71, .1]] 61 | >>> P_ds = sk.fit(P) 62 | >>> P_ds 63 | array([[ 0.06102561, 0.93897439], 64 | [ 0.93809928, 0.06190072]]) 65 | >>> np.sum(P_ds, axis=0) 66 | array([ 0.99912489, 1.00087511]) 67 | >>> np.sum(P_ds, axis=1) 68 | array([ 1., 1.]) 69 | 70 | """ 71 | 72 | def __init__(self, max_iter=1000, setr=0, setc=0, epsilon=1e-3): 73 | assert isinstance(max_iter, int) or isinstance(max_iter, float),\ 74 | "max_iter is not of type int or float: %r" % max_iter 75 | assert max_iter > 0,\ 76 | "max_iter must be greater than 0: %r" % max_iter 77 | self._max_iter = int(max_iter) 78 | 79 | assert isinstance(epsilon, int) or isinstance(epsilon, float),\ 80 | "epsilon is not of type float or int: %r" % epsilon 81 | assert epsilon > 0 and epsilon < 1,\ 82 | "epsilon must be between 0 and 1 exclusive: %r" % epsilon 83 | self._epsilon = epsilon 84 | self._setr = setr 85 | self._setc = setc 86 | self._stopping_condition = None 87 | self._iterations = 0 88 | self._D1 = np.ones(1) 89 | self._D2 = np.ones(1) 90 | 91 | def fit(self, P): 92 | """Fit the diagonal matrices in Sinkhorn Knopp's algorithm 93 | 94 | Parameters 95 | ---------- 96 | P : 2d array-like 97 | Must be a square non-negative 2d array-like object, that 98 | is convertible to a numpy array. The matrix must not be 99 | equal to 0 and it must have total support for the algorithm 100 | to converge. 101 | 102 | Returns 103 | ------- 104 | A double stochastic matrix. 105 | 106 | """ 107 | P = np.asarray(P) 108 | assert np.all(P >= 0) 109 | assert P.ndim == 2 110 | 111 | N = P.shape[0] 112 | if np.sum(abs(self._setr)) == 0: 113 | rsum = P.shape[1] 114 | else: 115 | rsum = self._setr 116 | if np.sum(abs(self._setc)) == 0: 117 | csum = P.shape[0] 118 | else: 119 | csum = self._setc 120 | max_threshr = rsum + self._epsilon 121 | min_threshr = rsum - self._epsilon 122 | max_threshc = csum + self._epsilon 123 | min_threshc = csum - self._epsilon 124 | # Initialize r and c, the diagonals of D1 and D2 125 | # and warn if the matrix does not have support. 126 | r = np.ones((N, 1)) 127 | pdotr = P.T.dot(r) 128 | total_support_warning_str = ( 129 | "Matrix P must have total support. " 130 | "See documentation" 131 | ) 132 | if not np.all(pdotr != 0): 133 | warnings.warn(total_support_warning_str, UserWarning) 134 | 135 | c = 1 / pdotr 136 | pdotc = P.dot(c) 137 | if not np.all(pdotc != 0): 138 | warnings.warn(total_support_warning_str, UserWarning) 139 | 140 | r = 1 / pdotc 141 | del pdotr, pdotc 142 | 143 | P_eps = np.copy(P) 144 | while np.any(np.sum(P_eps, axis=1) < min_threshr) \ 145 | or np.any(np.sum(P_eps, axis=1) > max_threshr) \ 146 | or np.any(np.sum(P_eps, axis=0) < min_threshc) \ 147 | or np.any(np.sum(P_eps, axis=0) > max_threshc): 148 | 149 | c = csum / P.T.dot(r) 150 | r = rsum / P.dot(c) 151 | 152 | self._D1 = np.diag(np.squeeze(r)) 153 | self._D2 = np.diag(np.squeeze(c)) 154 | 155 | P_eps = np.diag(self._D1)[:,None] * P * np.diag(self._D2)[None,:] 156 | 157 | 158 | self._iterations += 1 159 | 160 | if self._iterations >= self._max_iter: 161 | self._stopping_condition = "max_iter" 162 | break 163 | 164 | if not self._stopping_condition: 165 | self._stopping_condition = "epsilon" 166 | 167 | self._D1 = np.diag(np.squeeze(r)) 168 | self._D2 = np.diag(np.squeeze(c)) 169 | P_eps = np.diag(self._D1)[:,None] * P * np.diag(self._D2)[None,:] 170 | 171 | return P_eps 172 | -------------------------------------------------------------------------------- /cinemaot/utils.py: -------------------------------------------------------------------------------- 1 | import gseapy as gp 2 | import pandas as pd 3 | from scipy.stats import wilcoxon 4 | import numpy as np 5 | import scanpy as sc 6 | #import scib 7 | from sklearn.linear_model import LogisticRegression 8 | from sklearn.preprocessing import OneHotEncoder 9 | from scipy.stats import kstest 10 | import plotly.graph_objects as go 11 | import plotly.express as px 12 | 13 | # import rpy2.robjects as ro 14 | # import rpy2.robjects.numpy2ri 15 | # import rpy2.robjects.pandas2ri 16 | # from rpy2.robjects.packages import importr 17 | # rpy2.robjects.numpy2ri.activate() 18 | # rpy2.robjects.pandas2ri.activate() 19 | 20 | 21 | def dominantcluster(adata,ctobs,clobs): 22 | clustername = [] 23 | clustertime = np.zeros(adata.obs[ctobs].value_counts().values.shape[0]) 24 | for i in adata.obs[clobs].value_counts().sort_index().index.values: 25 | tmp = adata.obs[ctobs][adata.obs[clobs]==i].value_counts().sort_index() 26 | ind = np.argmax(tmp.values) 27 | clustername.append(tmp.index.values[ind] + str(int(clustertime[ind]))) 28 | clustertime[ind] = clustertime[ind] + 1 29 | return clustername 30 | 31 | def assignleiden(adata,ctobs,clobs,label): 32 | clustername = dominantcluster(adata,ctobs,clobs) 33 | ss = adata.obs[clobs].values.tolist() 34 | for i in range(len(ss)): 35 | ss[i] = clustername[int(ss[i])] 36 | adata.obs[label] = ss 37 | return 38 | 39 | def clustertest_synergy(adata1,adata2,clobs,thres,fthres,path,genesetpath,organism): 40 | # In this simplified function, we return the gene set only. The function is only designed for synergy computation. 41 | mkup = [] 42 | mkdown = [] 43 | for i in list(set(adata1.obs[clobs].values.tolist())): 44 | adata = adata1 45 | clusterindex = (adata.obs[clobs].values==i) 46 | tmpte = adata.X[clusterindex,:] 47 | clustername = i 48 | pv = np.zeros(tmpte.shape[1]) 49 | for k in range(tmpte.shape[1]): 50 | st, pv[k] = wilcoxon(tmpte[:,k],zero_method='zsplit') 51 | genenames = adata.var_names.values 52 | upindex = (((pv0)*1) * (np.abs(np.median(tmpte,axis=0))>fthres))>0 53 | downindex = (((pvfthres))>0 54 | allindex = (((pvfthres))>0 55 | upgenes1 = genenames[upindex] 56 | downgenes1 = genenames[downindex] 57 | allgenes1 = genenames[allindex] 58 | adata = adata2 59 | clusterindex = (adata.obs[clobs].values==i) 60 | tmpte = adata.X[clusterindex,:] 61 | clustername = i 62 | pv = np.zeros(tmpte.shape[1]) 63 | for k in range(tmpte.shape[1]): 64 | st, pv[k] = wilcoxon(tmpte[:,k],zero_method='zsplit') 65 | genenames = adata.var_names.values 66 | upindex = (((pv0)*1) * (np.abs(np.median(tmpte,axis=0))>fthres))>0 67 | downindex = (((pvfthres))>0 68 | allindex = (((pvfthres))>0 69 | upgenes2 = genenames[upindex] 70 | downgenes2 = genenames[downindex] 71 | allgenes2 = genenames[allindex] 72 | up1syn = list(set(upgenes1.tolist()) - set(upgenes2.tolist())) 73 | up2syn = list(set(upgenes2.tolist()) - set(upgenes1.tolist())) 74 | down1syn = list(set(downgenes1.tolist()) - set(downgenes2.tolist())) 75 | down2syn = list(set(downgenes2.tolist()) - set(downgenes1.tolist())) 76 | allgenes = list(set(up1syn) | set(up2syn) | set(down1syn) | set(down2syn)) 77 | enr_up1 = gp.enrichr(gene_list=up1syn, gene_sets=genesetpath, 78 | no_plot=True,organism=organism, 79 | outdir=path, format='png') 80 | enr_up2 = gp.enrichr(gene_list=up2syn, gene_sets=genesetpath, 81 | no_plot=True,organism=organism, 82 | outdir=path, format='png') 83 | enr_down1 = gp.enrichr(gene_list=down1syn, gene_sets=genesetpath, 84 | no_plot=True,organism=organism, 85 | outdir=path, format='png') 86 | enr_down2 = gp.enrichr(gene_list=down2syn, gene_sets=genesetpath, 87 | no_plot=True,organism=organism, 88 | outdir=path, format='png') 89 | if not enr_up1.results.empty: 90 | enr_up1.results.iloc[enr_up1.results['Adjusted P-value'].values<1e-2,:].to_csv(path+'/Up1'+clustername+'.csv') 91 | if not enr_up2.results.empty: 92 | enr_up2.results.iloc[enr_up2.results['Adjusted P-value'].values<1e-2,:].to_csv(path+'/Up2'+clustername+'.csv') 93 | if not enr_down1.results.empty: 94 | enr_down1.results.iloc[enr_down1.results['Adjusted P-value'].values<1e-2,:].to_csv(path+'/Down1'+clustername+'.csv') 95 | if not enr_down2.results.empty: 96 | enr_down2.results.iloc[enr_down2.results['Adjusted P-value'].values<1e-2,:].to_csv(path+'/Down2'+clustername+'.csv') 97 | upgenes1df = pd.DataFrame(index=up1syn) 98 | upgenes2df = pd.DataFrame(index=up2syn) 99 | downgenes1df = pd.DataFrame(index=down1syn) 100 | downgenes2df = pd.DataFrame(index=down2syn) 101 | allgenesdf = pd.DataFrame(index=allgenes) 102 | upgenes1df.to_csv(path+'/Upnames1'+clustername+'.csv') 103 | upgenes2df.to_csv(path+'/Upnames2'+clustername+'.csv') 104 | downgenes1df.to_csv(path+'/Downnames1'+clustername+'.csv') 105 | downgenes2df.to_csv(path+'/Downnames2'+clustername+'.csv') 106 | allgenesdf.to_csv(path+'/names'+clustername+'.csv') 107 | 108 | return 109 | 110 | 111 | def clustertest(adata,clobs,thres,fthres,label,path,genesetpath,organism,onlyup=False): 112 | # Changed from ttest to Wilcoxon test 113 | clusternum = int(np.max((np.asfarray(adata.obs[clobs].values)))) 114 | genenum = np.zeros([clusternum+1]) 115 | mk = [] 116 | for i in range(clusternum+1): 117 | clusterindex = (np.asfarray(adata.obs[clobs].values)==i) 118 | tmpte = adata.X[clusterindex,:] 119 | clustername = adata.obs[label][clusterindex][0] 120 | pv = np.zeros(tmpte.shape[1]) 121 | for k in range(tmpte.shape[1]): 122 | st, pv[k] = wilcoxon(tmpte[:,k],zero_method='zsplit') 123 | genenames = adata.var_names.values 124 | upindex = (((pv0)*1) * (np.abs(np.median(tmpte,axis=0))>fthres))>0 125 | downindex = (((pvfthres))>0 126 | allindex = (((pvfthres))>0 127 | upgenes = genenames[upindex] 128 | downgenes = genenames[downindex] 129 | allgenes = genenames[allindex] 130 | mk.extend(allgenes.tolist()) 131 | mk = list(set(mk)) 132 | genenum[i] = np.sum(((pvfthres))) 133 | enr_up = gp.enrichr(gene_list=upgenes.tolist(), gene_sets=genesetpath, 134 | no_plot=True,organism=organism, 135 | outdir=path, format='png') 136 | enr_down = gp.enrichr(gene_list=downgenes.tolist(), gene_sets=genesetpath, 137 | no_plot=True,organism=organism, 138 | outdir=path, format='png') 139 | enr = gp.enrichr(gene_list=allgenes.tolist(), gene_sets=genesetpath, 140 | no_plot=True,organism=organism, 141 | outdir=path, format='png') 142 | if not enr_up.results.empty: 143 | enr_up.results.iloc[enr_up.results['Adjusted P-value'].values<1e-3,:].to_csv(path+'/Up'+clustername+'.csv') 144 | if not enr_down.results.empty: 145 | enr_down.results.iloc[enr_down.results['Adjusted P-value'].values<1e-3,:].to_csv(path+'/Down'+clustername+'.csv') 146 | if not enr.results.empty: 147 | enr.results.iloc[enr.results['Adjusted P-value'].values<1e-3,:].to_csv(path+'/'+clustername+'.csv') 148 | upgenesdf = pd.DataFrame(index=upgenes) 149 | downgenesdf = pd.DataFrame(index=downgenes) 150 | allgenesdf = pd.DataFrame(index=allgenes) 151 | upgenesdf.to_csv(path+'/Upnames'+clustername+'.csv') 152 | downgenesdf.to_csv(path+'/Downnames'+clustername+'.csv') 153 | allgenesdf.to_csv(path+'/names'+clustername+'.csv') 154 | if onlyup: 155 | enr = enr_up 156 | 157 | if not enr.results.empty: 158 | if i == 0: 159 | df = enr.results.transpose().iloc[4:5,:] 160 | df.columns = enr.results['Term'][:] 161 | df.index.values[0] = clustername 162 | else: 163 | tmp = enr.results.transpose().iloc[4:5,:] 164 | tmp.columns = enr.results['Term'][:] 165 | tmp.index.values[0] = clustername 166 | df = pd.concat([df,tmp]) 167 | #df.values = -np.log10(df.values) 168 | #DF = sc.AnnData(df.transpose()) 169 | #sc.pl.clustermap(DF,cmap='viridis', col_cluster=False) 170 | return genenum, df, mk 171 | 172 | 173 | def concordance_map(confounder,response,obs_label, cl_label, condition): 174 | #deprecated 175 | cf = confounder[confounder.obs[obs_label] == condition,:] 176 | cf.obs['res_cl'] = response.obs[cl_label].values 177 | aswmatrix = np.zeros([len(list(set(cf.obs['res_cl'].values.tolist()))),len(list(set(cf.obs['res_cl'].values.tolist())))]) 178 | indnummatrix = pd.DataFrame(None,list(set(cf.obs['res_cl'].values.tolist())),list(set(cf.obs['res_cl'].values.tolist()))) 179 | k = 0 180 | #return aswmatrix 181 | for i in list(set(cf.obs['res_cl'].values.tolist())): 182 | l = 0 183 | for j in list(set(cf.obs['res_cl'].values.tolist())): 184 | if i != j: 185 | tmpcf = cf[cf.obs['res_cl'].isin([i,j]),:].copy() 186 | sc.pp.pca(tmpcf) 187 | encoder = OneHotEncoder(sparse=False) 188 | onehot = encoder.fit_transform(np.array(tmpcf.obs['res_cl'].values.tolist()).reshape(-1, 1)) 189 | label = onehot[:,0] 190 | lc = LogisticRegression(penalty='l1',solver='liblinear',C=1) 191 | lc.fit(tmpcf.X, label) 192 | prob = lc.predict_proba(tmpcf.X) 193 | prob1 = prob[label==1,0] 194 | prob2 = prob[label==0,0] 195 | st, pv = kstest(prob1,prob2) 196 | #yi = np.zeros([onehot.shape[1],eigen.shape[1]]) 197 | aswmatrix[k,l] = -np.log10(pv+1e-20) 198 | if np.sum(lc.coef_!=0)>0: 199 | indnummatrix.iloc[k,l] = str(np.argwhere(lc.coef_[0] !=0)[:,0].tolist())[1:-1] 200 | else: 201 | aswmatrix[k,l] = 0 202 | l = l + 1 203 | k = k + 1 204 | aswmatrix = pd.DataFrame(aswmatrix,list(set(cf.obs['res_cl'].values.tolist())),list(set(cf.obs['res_cl'].values.tolist()))) 205 | return aswmatrix, indnummatrix 206 | 207 | 208 | def coarse_matching(de,de_label,ref,ref_label,ot,scaling=1e6,mode='mean'): 209 | coarse_ot = pd.DataFrame(index=sorted(set(de.obs[de_label].values.tolist())),columns=sorted(set(ref.obs[ref_label].values.tolist())),dtype=float) 210 | for i in set(de.obs[de_label].values.tolist()): 211 | for j in set(ref.obs[ref_label].values.tolist()): 212 | tmp_ot = ot[de.obs[de_label]==i,:] 213 | if mode=='mean': 214 | coarse_ot[j][i] = np.mean(tmp_ot[:,ref.obs[ref_label]==j]) * scaling 215 | else: 216 | coarse_ot[j][i] = np.sum(tmp_ot[:,ref.obs[ref_label]==j]) * scaling 217 | return coarse_ot 218 | 219 | def sankey_plot(coarse_ot,thres1=0.1,thres2=0.1,title_text="Sankey Diagram",width=600,height=400): 220 | new_coarse_ot = pd.DataFrame(np.zeros([coarse_ot.shape[0]*coarse_ot.shape[1],3])) 221 | k = 0 222 | for i in range(coarse_ot.shape[0]): 223 | for j in range(coarse_ot.shape[1]): 224 | thres_ = max(thres1 * np.sum(coarse_ot.values[i,:]), thres2 * np.sum(coarse_ot.values[:,j])) 225 | if coarse_ot.values[i,j] > thres_: 226 | new_coarse_ot.iloc[k,1] = 'Response: ' + coarse_ot.index[i] 227 | new_coarse_ot.iloc[k,0] = coarse_ot.columns[j] 228 | new_coarse_ot.iloc[k,2] = coarse_ot.values[i,j] 229 | 230 | k = k + 1 231 | new_coarse_ot = new_coarse_ot.loc[new_coarse_ot.iloc[:,2]>0,:] 232 | a = set(new_coarse_ot[0].values.tolist()) 233 | b = set(new_coarse_ot[1].values.tolist()) 234 | a0 = [] 235 | for i in range(len(list(a))): 236 | a0.append(list(a)[i][:-1]) 237 | a0 = list(set(a0)) 238 | 239 | source = np.arange(new_coarse_ot.shape[0] + new_coarse_ot.shape[0]) 240 | target = np.arange(new_coarse_ot.shape[0] + new_coarse_ot.shape[0]) 241 | 242 | for i in range(new_coarse_ot.shape[0]): 243 | source[i+new_coarse_ot.shape[0]] = np.argwhere(np.array(list(a))==new_coarse_ot[0].values[i])[0][0] 244 | target[i+new_coarse_ot.shape[0]] = np.argwhere(np.array(list(b))==new_coarse_ot[1].values[i])[0][0] 245 | 246 | target = target + len(list(a)) 247 | 248 | for i in range(new_coarse_ot.shape[0]): 249 | source[i] = np.argwhere(np.array(a0)==new_coarse_ot[0].values[i][:-1])[0][0] 250 | target[i] = np.argwhere(np.array(list(a))==new_coarse_ot[0].values[i])[0][0] 251 | 252 | target = target + len(a0) 253 | source[new_coarse_ot.shape[0]:] = source[new_coarse_ot.shape[0]:] + len(a0) 254 | values = np.zeros(2*new_coarse_ot.shape[0]) 255 | for i in range(new_coarse_ot.shape[0]): 256 | values[i] = np.sum(new_coarse_ot.values[:,2][new_coarse_ot.values[:,0]==new_coarse_ot.values[i,0]]) / np.sum(new_coarse_ot.values[:,0]==new_coarse_ot.values[i,0]) 257 | 258 | values[new_coarse_ot.shape[0]:] = new_coarse_ot.values[:,2] 259 | colorlist = px.colors.qualitative.Plotly 260 | colors = np.array(a0 + list(a) + list(b)) 261 | colors[0:len(a0)] = colorlist[0:len(a0)] 262 | for i in range(len(a0),len(a0)+len(list(a))): 263 | colors[i] = colors[0:len(a0)][np.array(a0)==(list(a)[i-len(a0)][:-1])][0] 264 | for i in range(len(a0)+len(list(a)),len(a0)+len(list(a))+len(list(b))): 265 | colors[i] = colors[0:len(a0)][np.array(a0)==(list(b)[i-len(a0)-len(list(a))][10:-1])][0] 266 | 267 | fig = go.Figure(data=[go.Sankey( 268 | node = dict( 269 | pad = 15, 270 | thickness = 20, 271 | #line = dict(color = "black", width = 0.5), 272 | label = a0 + list(a) + list(b), 273 | color = colors 274 | ), 275 | link = dict( 276 | source = source, # indices correspond to labels, eg A1, A2, A1, B1, ... 277 | target = target, 278 | value = values 279 | ))]) 280 | 281 | fig.update_layout(title_text="Sankey Diagram", font_family="Arial", font_size=10,width=width, height=height) 282 | fig.show() 283 | return 284 | 285 | 286 | 287 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools>=42"] 3 | build-backend = "setuptools.build_meta" -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | name = cinemaot 3 | version = 0.0.4 4 | author = Mingze Dong 5 | author_email = mingze.dong@yale.edu 6 | description = Causal INdependent Effect Module Attribution + Optimal Transport 7 | long_description = file: README.md 8 | long_description_content_type = text/markdown 9 | url = https://github.com/vandijklab/CINEMA-OT 10 | project_urls = 11 | Bug Tracker = https://github.com/vandijklab/CINEMA-OT/issues 12 | classifiers = 13 | Programming Language :: Python :: 3 14 | Development Status :: 2 - Pre-Alpha 15 | Operating System :: OS Independent 16 | 17 | [options] 18 | package_dir = 19 | packages = find: 20 | python_requires = >=3.7 21 | install_requires = 22 | numpy 23 | pandas 24 | scanpy 25 | scikit-learn 26 | scipy 27 | statsmodels 28 | anndata 29 | 30 | [options.packages.find] 31 | where = 32 | 33 | -------------------------------------------------------------------------------- /simulation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scanpy as sc 3 | import matplotlib.pyplot as plt 4 | from scsim_master import scsim 5 | import pandas as pd 6 | 7 | import random 8 | 9 | def numbers_with_sum(n, k): 10 | """n numbers with sum k""" 11 | if n == 1: 12 | return [k] 13 | num = random.randint(0, k) 14 | return [num] + numbers_with_sum(n - 1, k - num) 15 | 16 | random.seed(0) 17 | np.random.seed(0) 18 | for i in range(15): 19 | states_num = round(i/5) + 2 20 | gp = numbers_with_sum(states_num, 10-states_num) 21 | simulator = scsim.scsim(ngenes=1000, ncells=5000, seed = i, ngroups=states_num, libloc=7.64, libscale=0.78, 22 | mean_rate=7.68,mean_shape=0.34, expoutprob=0.00286, 23 | expoutloc=6.15, expoutscale=0.49, 24 | diffexpprob=.5, diffexpdownprob=.5, diffexploc=1, diffexpscale=1, 25 | bcv_dispersion=0.448, bcv_dof=22.087, ndoublets=0,groupprob=(np.array(gp)+1)/10,proggoups=[1,2],nproggenes=500, 26 | progdeloc=1,progdescale=1,progdownprob=0.,progcellfrac = 1.) 27 | 28 | simulator.simulate() 29 | tmpobs = simulator.cellparams 30 | ## "Groups" represent the treatment variable 31 | tmpobs['Groups'] = 0 32 | tmpobs['Response_state'] = 0 33 | response_num = round(i/5) + 1 34 | attribution_matrix = np.zeros([states_num,response_num]) 35 | simulator2_counts = simulator.counts.iloc[:,0:500].copy() 36 | for j in range(states_num): 37 | ncells_j = np.sum(simulator.cellparams['group']==(j+1)) 38 | 39 | group = np.random.randint(0,2,size=ncells_j) 40 | 41 | gp2 = np.zeros(response_num+1) + 0.5 42 | gp2[1:] = (np.array(numbers_with_sum(response_num, 5)))/10 43 | 44 | simulator2 = scsim.scsim(ngenes=500, ncells=ncells_j, seed = 300, ngroups=response_num+1, libloc=7.64, libscale=0.78, 45 | mean_rate=7.68,mean_shape=0.34, expoutprob=0.00286, 46 | expoutloc=6.15, expoutscale=0.49, 47 | diffexpprob=.5, diffexpdownprob=.5, diffexploc=1, diffexpscale=1, 48 | bcv_dispersion=0.148, bcv_dof=22.087, ndoublets=0,groupprob=gp2,nproggenes=0, 49 | progdeloc=1,progdescale=1,progdownprob=0.,progcellfrac = 1.) 50 | 51 | attribution_matrix[j,:] = 2 * gp2[1:] 52 | simulator2.simulate() 53 | ## group==1 is assigned as control, set the rest as perturbed 54 | tmpobs['Groups'][simulator.cellparams['group']==(j+1)] = (simulator2.cellparams['group'].values > 1) * 1 + 1 55 | tmpobs['Response_state'][simulator.cellparams['group']==(j+1)] = simulator2.cellparams['group'].values 56 | simulator2_counts.loc[simulator.cellparams['group']==(j+1),:] = simulator2.counts.values 57 | ## in the final anndata, 'group' represents cell state / type, 'Groups' represents treated or not, 'Response_state' indicates response heterogeneity 58 | adata = sc.AnnData(pd.concat([simulator.counts, simulator2_counts], axis=1),obs=tmpobs) 59 | adata.uns['attribution'] = attribution_matrix 60 | adata.write('ScsimBenchmarkData/adata'+str(i)+'.h5ad') --------------------------------------------------------------------------------