regressing out ['total_counts', 'pct_counts_mt'] sparse input is densified and may lead to...
#foreach cell compute fraction of counts inmitogenes vs. all genes # the`.A1` is only necessary as X is sparse (to transform to a dense array after summing) adata.obs['percent_mito']=np.sum( adata[:,mito_genes].X,axis=1).A1/np.sum(adata.X,axis=1).A1 #addthe total counts pe...
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def log1p: return log1p_array(X, copy=copy, base=base) # 根据参数base,又两种标准化方法。 # --- np.log1p(X, out=X) # 平滑处理,数据更加符合高斯分布。log1p = log(x+1) 逆运算 expm1 if base is not None: np.divide(X, np.log(base), out=X) # np.log(base) 以E 为底的base...
mito_genes=adata.var_names.str.startswith('MT-')# for each cell compute fraction of counts in mito genes vs. all genes# the `.A1` is only necessary as X is sparse (to transform to a dense array after summing)adata.obs['percent_mito']=np.sum(adata[:,mito_genes].X,axis=1).A1/...
used in case it has been initialized before. The simplest and fastest method to do #so is the...
when only coordinates or meta data were changed. html_dir If this variable is set, the export will build html files from `data_dir` to `html_dir`, creating html/js/json files. Usually there is one global html output directory for all datasets. Often, `html_dir` is located under a ...
""" logg.info(' abstracted graph will have {} nodes'.format(self.n_splits+1)) indices_all = np.arange(self.X.shape[0], dtype=int) segs = [indices_all] if False: # this is safe, but not compatible with on-the-fly computation tips_all = np.array(np.unravel_index(np.argmax...
4scipy==1.12.0pandas==2.2.1scikit-learn==1.4.1.post1statsmodels==0.14.1igraph==0.11.4pynndescent==0.5.11#设置工作输出文件# results_file = "write/pbmc3k.h5ad" # the file that will store the analysis results#这是原教程#修改成我自己的results_file="../output/pbmc3k.h5ad"# the file ...