LinAlgError when running dyn.tl.dynamics()

[nprashant2001]

Discussed in https://github.com/aristoteleo/dynamo-release/discussions/423

^{Originally posted by **Ckenen** September 21, 2022} When I run dyn.tl.dynamics(adata, group="group", NTR_vel=True), it will raise LinAlgError: SVD did not converge. However, if I do not provide group parameter, not error will be raised. But I need M_s and velocity_S in my downstream analysis. How can I fixed this problem? Detail as follow: LinAlgError Traceback (most recent call last) Cell In [150], line 2 1 # dyn.tl.dynamics(adata, NTR_vel=True) ----> 2 dyn.tl.dynamics(adata, group="group", NTR_vel=True) File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/dynamo/tools/dynamics.py:521, 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) 518 warnings.simplefilter("ignore") 520 if experiment_type.lower() in ["one-shot", "one_shot"]: --> 521 est.fit(one_shot_method=one_shot_method, **est_kwargs) 522 else: 523 # experiment_type can be `kin` also and by default use 524 # conventional method to estimate k but correct for time 525 est.fit(**est_kwargs) File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/dynamo/estimation/csc/velocity.py:1368, in ss_estimation.fit(self, intercept, perc_left, perc_right, clusters, one_shot_method) 1357 if cores == 1: 1358 for i in tqdm(range(n_genes), desc="estimating gamma"): 1359 ( 1360 k[i], 1361 k_intercept[i], 1362 _, 1363 k_r2[i], 1364 _, 1365 k_logLL[i], 1366 bs[i], 1367 bf[i], -> 1368 ) = self.fit_gamma_stochastic( 1369 self.est_method, 1370 U[i], 1371 S[i], 1372 US[i], 1373 S2[i], 1374 perc_left=perc_left, 1375 perc_right=perc_right, 1376 normalize=True, 1377 ) 1378 else: 1379 pool = ThreadPool(cores) File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/dynamo/estimation/csc/velocity.py:1690, in ss_estimation.fit_gamma_stochastic(self, est_method, u, s, us, ss, perc_left, perc_right, normalize) 1688 bs, bf = None, None 1689 if est_method.lower() == "gmm": -> 1690 k = fit_stochastic_linreg(u[mask], s[mask], us[mask], ss[mask]) 1691 elif est_method.lower() == "negbin": 1692 phi = compute_dispersion(s, ss) File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/dynamo/estimation/csc/utils_velocity.py:420, in fit_stochastic_linreg(u, s, us, ss, fit_2_gammas, err_cov) 418 else: 419 cov = np.diag(E.var(1)) --> 420 cov_inv = np.linalg.pinv(cov) 422 # generalized least squares 423 xy, xx = 0, 0 File <__array_function__ internals>:5, in pinv(*args, **kwargs) File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/numpy/linalg/linalg.py:2002, in pinv(a, rcond, hermitian) 2000 return wrap(res) 2001 a = a.conjugate() -> 2002 u, s, vt = svd(a, full_matrices=False, hermitian=hermitian) 2004 # discard small singular values 2005 cutoff = rcond[..., newaxis] * amax(s, axis=-1, keepdims=True) File <__array_function__ internals>:5, in svd(*args, **kwargs) File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/numpy/linalg/linalg.py:1660, in svd(a, full_matrices, compute_uv, hermitian) 1657 gufunc = _umath_linalg.svd_n_s 1659 signature = 'D->DdD' if isComplexType(t) else 'd->ddd' -> 1660 u, s, vh = gufunc(a, signature=signature, extobj=extobj) 1661 u = u.astype(result_t, copy=False) 1662 s = s.astype(_realType(result_t), copy=False) File ~/miniconda3/envs/scanpy/lib/python3.10/site-packages/numpy/linalg/linalg.py:97, in _raise_linalgerror_svd_nonconvergence(err, flag) 96 def _raise_linalgerror_svd_nonconvergence(err, flag): ---> 97 raise LinAlgError("SVD did not converge") LinAlgError: SVD did not converge

[migthms]

Hello,

I am trying to follow the tutorial "scNT-seq human hematopoiesis dynamics" and estimate RNA Velocity with Model 1 instead of Model 2 from the adata_hsc_raw (dyn.sample_data.hematopoiesis_raw()). I defined adata_hsc_raw.uns["pp"] like the one I found in the tutorial of Zebrafish pigmentation.

And I got the exact same error as mentioned above when trying to run the following line : dyn.tl.dynamics(adata_hsc_raw, model="stochastic").

dynamo==1.4.0 numpy==1.26.4

Thank you in advance for any response!

[Sichao25]

Thanks for raising the issue. Could you check whether there are NaNs or Infs in your processed data before dyn.tl.dynamics()?