aristoteleo / dynamo-tutorials Goto Github PK

[<ipython-input-9-5d53bc49f5de>](https://ndagie4afs-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230321-060141-RC01_518395136#) in dynamo_workflow(adata)
      2     dyn.pp.recipe_monocle(adata)
      3 
----> 4     dyn.tl.dynamics(adata)
      5 
      6     dyn.tl.reduceDimension(adata)

[~/.local/lib/python3.9/site-packages/dynamo/tools/dynamics.py](https://ndagie4afs-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230321-060141-RC01_518395136#) in dynamics(adata, filter_gene_mode, use_smoothed, assumption_mRNA, assumption_protein, model, est_method, NTR_vel, group, protein_names, concat_data, log_unnormalized, one_shot_method, fraction_for_deg, re_smooth, sanity_check, del_2nd_moments, cores, tkey, **est_kwargs)
    731             data_type = "smoothed" if use_smoothed else "sfs"
    732 
--> 733             (params, half_life, cost, logLL, param_ranges, cur_X_data, cur_X_fit_data,) = kinetic_model(
    734                 subset_adata,
    735                 tkey,

[~/.local/lib/python3.9/site-packages/dynamo/tools/dynamics.py](https://ndagie4afs-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230321-060141-RC01_518395136#) in kinetic_model(subset_adata, tkey, model, est_method, experiment_type, has_splicing, splicing_labeling, has_switch, param_rngs, data_type, return_ntr, **est_kwargs)
   1540                     cur_X_raw = np.hstack((cur_X_raw[0, 0].A, cur_X_raw[1, 0].A))
   1541 
-> 1542             _, cost[i_gene] = estm.auto_fit(np.unique(time), cur_X_data)
   1543             (
   1544                 model_1,

[~/.local/lib/python3.9/site-packages/dynamo/estimation/tsc/estimation_kinetic.py](https://ndagie4afs-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230321-060141-RC01_518395136#) in auto_fit(self, time, x_data, alpha_min, beta_min, gamma_min, kin_weight, use_p0, **kwargs)
    709 
    710         if use_p0:
--> 711             popt, cost = self.fit_lsq(time, x_data_norm, p0=p0, **kwargs)
    712         else:
    713             popt, cost = self.fit_lsq(time, x_data_norm, **kwargs)

[~/.local/lib/python3.9/site-packages/dynamo/estimation/tsc/estimation_kinetic.py](https://ndagie4afs-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230321-060141-RC01_518395136#) in fit_lsq(self, t, x_data, p0, n_p0, bounds, sample_method, method, normalize)
    209         X = []
    210         for i in range(n_p0):
--> 211             ret = least_squares(
    212                 lambda p: self.f_lsq(p, t, x_data_norm, method, normalize),
    213                 p0[i],

[/usr/local/lib/python3.9/dist-packages/scipy/optimize/_lsq/least_squares.py](https://ndagie4afs-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230321-060141-RC01_518395136#) in least_squares(fun, x0, jac, bounds, method, ftol, xtol, gtol, x_scale, loss, f_scale, diff_step, tr_solver, tr_options, jac_sparsity, max_nfev, verbose, args, kwargs)
    835 
    836     if not np.all(np.isfinite(f0)):
--> 837         raise ValueError("Residuals are not finite in the initial point.")
    838 
    839     n = x0.size

ValueError: Residuals are not finite in the initial point.