Reproducibility of mixscape

[jesswhitts]

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Hello,

When running the lda visualisation plot as part of the mixscape workflow, I am not getting consistent results between runs, the plot looks slightly different (although very similar).
Is a random seed used at any point, and is there a way to set this so I can get reproducible results?

Code used:

ms = pt.tl.Mixscape()
ms.perturbation_signature(mdata["rna"], pert_key="perturbation", control="NT")

adata_pert = mdata["rna"].copy()
adata_pert.X = adata_pert.layers["X_pert"]

sc.pp.pca(adata_pert)
sc.pp.neighbors(adata_pert, metric="cosine")
sc.tl.umap(adata_pert)

ms.mixscape(adata=mdata["rna"], control="NT", labels="guide_allocation", layer="X_pert",
logfc_threshold = 0.15, iter_num=100, min_de_genes=5)
ms.lda(adata=mdata["rna"], control="NT", labels="guide_allocation", layer="X_pert")
ms.plot_lda(adata=mdata["rna"], control="NT")

Version information

---

adjustText 1.2.0
anndata 0.10.8
matplotlib 3.9.2
mudata 0.3.1
muon 0.1.6
numpy 1.26.4
pandas 2.2.2
pertpy 0.9.4
scanpy 1.10.3
session_info 1.0.0

PIL 10.4.0
absl NA
appnope 0.1.4
asttokens NA
attr 24.2.0
blitzgsea NA
certifi 2024.08.30
charset_normalizer 3.3.2
chex 0.1.86
comm 0.2.2
contextlib2 NA
cycler 0.12.1
cython_runtime NA
dateutil 2.9.0
debugpy 1.8.5
decorator 5.1.1
decoupler 1.8.0
docrep 0.3.2
equinox 0.11.7
etils 1.9.4
executing 2.1.0
filelock 3.16.1
flax 0.9.0
fsspec 2024.9.0
h5py 3.11.0
idna 3.10
igraph 0.11.6
ipykernel 6.29.5
jax 0.4.33
jaxlib 0.4.33
jaxopt NA
jaxtyping 0.2.34
jedi 0.19.1
joblib 1.4.2
kiwisolver 1.4.7
lamin_utils 0.13.4
legacy_api_wrap NA
leidenalg 0.10.2
lightning 2.4.0
lightning_fabric 2.4.0
lightning_utilities 0.11.7
lineax 0.0.5
llvmlite 0.43.0
matplotlib_inline 0.1.7
ml_collections NA
ml_dtypes 0.5.0
mpl_toolkits NA
mpmath 1.3.0
msgpack 1.1.0
multipledispatch 0.6.0
natsort 8.4.0
numba 0.60.0
numpyro 0.15.3
opt_einsum v3.3.0
optax 0.2.3
ott 0.4.8
packaging 24.1
parso 0.8.4
patsy 0.5.6
pickleshare 0.7.5
platformdirs 4.3.6
ply 3.11
prompt_toolkit 3.0.47
psutil 6.0.0
pubchempy 1.0.4
pure_eval 0.2.3
pyarrow 17.0.0
pydev_ipython NA
pydevconsole NA
pydevd 2.9.5
pydevd_file_utils NA
pydevd_plugins NA
pydevd_tracing NA
pygments 2.18.0
pynndescent 0.5.13
pyomo 6.8.0
pyparsing 3.1.4
pyro 1.9.1
pytorch_lightning 2.4.0
pytz 2024.2
requests 2.32.3
rich NA
scipy 1.14.1
scvi 1.1.6.post2
seaborn 0.13.2
six 1.16.0
sklearn 1.5.2
skmisc 0.5.1
sparsecca 0.3.1
stack_data 0.6.2
statsmodels 0.14.3
sympy 1.13.3
texttable 1.7.0
threadpoolctl 3.5.0
toolz 0.12.1
torch 2.4.1
torchgen NA
torchmetrics 1.4.2
tornado 6.4.1
tqdm 4.66.5
traitlets 5.14.3
typeguard NA
typing_extensions NA
umap 0.5.6
urllib3 2.2.3
wcwidth 0.2.13
yaml 6.0.2
zmq 26.2.0

IPython 8.27.0
jupyter_client 8.6.3
jupyter_core 5.7.2

Python 3.12.6 | packaged by conda-forge | (main, Sep 11 2024, 04:55:15) [Clang 17.0.6 ]
macOS-13.0.1-arm64-arm-64bit

[Zethson]

Dear @jesswhitts

I suspect it might be the LDA. I'll have a look soon.

[jesswhitts]

Hi @Zethson - any update on this?

[Zethson]

Sorry, I might have time for this on Friday. If it's urgent for you, you can check all function calls individually whether you always get the same results or not. Then you can tell me where the differences come from and I can try to get rid of them.

I'm very busy at the moment.