Unable to run Scrublet in v1.10

Please make sure these conditions are met

• I have checked that this issue has not already been reported.
• I have confirmed this bug exists on the latest version of scanpy.
• (optional) I have confirmed this bug exists on the main branch of scanpy.

What happened?

Dear scanpy developers,

I was exploring the new features in the latest version of Scanpy, but encountered a prolonged pause when running the sc.pp.scrublet(adata).

Initially I thought the problem was due to the large size (~100k cells) of the dataset I was exploring (I let it run for almost a whole week and nothing changed). However, even if I switched to my own dataset (unpublished, around 5k celIs), it paused at the same step.

Running Scrublet
filtered out 1419 genes that are detected in less than 3 cells
normalizing counts per cell
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
Embedding transcriptomes using PCA...

I was running this analysis on my Intel-core iMac. Surprisingly, when I ran the same line of code (under a similar virtual environment) on my M2-chip laptop, it finished in a flash of time.

filtered out 1419 genes that are detected in less than 3 cells
normalizing counts per cell
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
Embedding transcriptomes using PCA...
    using data matrix X directly
Automatically set threshold at doublet score = 0.42
Detected doublet rate = 0.3%
Estimated detectable doublet fraction = 5.2%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 6.6%
    Scrublet finished (0:00:14)

I'm still not sure what actually caused the problem, but it seems that some dependency inconsistency occurred when performing PCA within the pipeline. Perhaps some package required for the sc.pp.scrublet() pipeline needs to be updated to a newer version?

Here are the details of the packages in the virtual environment when I ran the code on my desktop (failed case):

channels:
  - pytorch
  - plotly
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - anndata=0.10.7
  - anyio=4.4.0
  - appnope=0.1.4
  - argcomplete=3.3.0
  - argh=0.31.2
  - argon2-cffi=23.1.0
  - argon2-cffi-bindings=21.2.0
  - arpack=3.8.0
  - array-api-compat=1.7.1
  - arrow=1.3.0
  - asttokens=2.4.1
  - async-lru=2.0.4
  - attrs=23.2.0
  - babel=2.14.0
  - beautifulsoup4=4.12.3
  - biopython=1.83
  - blas=2.120
  - blas-devel=3.9.0
  - bleach=6.1.0
  - blosc=1.21.5
  - brotli=1.1.0
  - brotli-bin=1.1.0
  - brotli-python=1.1.0
  - bzip2=1.0.8
  - c-ares=1.28.1
  - c-blosc2=2.14.4
  - ca-certificates=2024.6.2
  - cached-property=1.5.2
  - cached_property=1.5.2
  - certifi=2024.6.2
  - cffi=1.16.0
  - charset-normalizer=3.3.2
  - colorama=0.4.6
  - colorcet=3.1.0
  - colorful=0.5.6
  - comm=0.2.2
  - contourpy=1.2.1
  - cycler=0.12.1
  - debugpy=1.8.1
  - decorator=5.1.1
  - defusedxml=0.7.1
  - dill=0.3.8
  - dnspython=2.6.1
  - entrypoints=0.4
  - et_xmlfile=1.1.0
  - exceptiongroup=1.2.0
  - executing=2.0.1
  - filelock=3.14.0
  - fonttools=4.53.0
  - fqdn=1.5.1
  - freetype=2.12.1
  - get-annotations=0.1.2
  - gffpandas=1.2.2
  - gffutils=0.13
  - glpk=5.0
  - gmp=6.3.0
  - gmpy2=2.1.5
  - h11=0.14.0
  - h2=4.1.0
  - h5py=3.11.0
  - hdf5=1.14.3
  - hpack=4.0.0
  - httpcore=1.0.5
  - httpx=0.27.0
  - hyperframe=6.0.1
  - icu=73.2
  - idna=3.7
  - igraph=0.10.12
  - importlib-metadata=7.1.0
  - importlib_metadata=7.1.0
  - importlib_resources=6.4.0
  - ipykernel=6.29.4
  - ipython=8.25.0
  - isoduration=20.11.0
  - jedi=0.19.1
  - jinja2=3.1.4
  - joblib=1.4.2
  - json5=0.9.25
  - jsonpointer=2.4
  - jsonschema=4.22.0
  - jsonschema-specifications=2023.12.1
  - jsonschema-with-format-nongpl=4.22.0
  - jupyter-lsp=2.2.5
  - jupyter_client=8.6.2
  - jupyter_core=5.7.2
  - jupyter_events=0.10.0
  - jupyter_server=2.14.1
  - jupyter_server_terminals=0.5.3
  - jupyterlab=4.2.2
  - jupyterlab_pygments=0.3.0
  - jupyterlab_server=2.27.2
  - kaleido-core=0.2.1
  - kiwisolver=1.4.5
  - krb5=1.21.2
  - lcms2=2.16
  - legacy-api-wrap=1.4
  - leidenalg=0.10.2
  - lerc=4.0.0
  - libabseil=20240116.2
  - libaec=1.1.3
  - libblas=3.9.0
  - libbrotlicommon=1.1.0
  - libbrotlidec=1.1.0
  - libbrotlienc=1.1.0
  - libcblas=3.9.0
  - libcurl=8.8.0
  - libcxx=17.0.6
  - libdeflate=1.20
  - libedit=3.1.20191231
  - libev=4.33
  - libexpat=2.6.2
  - libffi=3.4.2
  - libgfortran=5.0.0
  - libgfortran5=13.2.0
  - libhwloc=2.10.0
  - libiconv=1.17
  - libjpeg-turbo=3.0.0
  - liblapack=3.9.0
  - liblapacke=3.9.0
  - libleidenalg=0.11.1
  - libllvm14=14.0.6
  - libnghttp2=1.58.0
  - libopenblas=0.3.27
  - libpng=1.6.43
  - libprotobuf=4.25.3
  - libsodium=1.0.18
  - libsqlite=3.46.0
  - libssh2=1.11.0
  - libtiff=4.6.0
  - libwebp-base=1.4.0
  - libxcb=1.15
  - libxml2=2.12.7
  - libzlib=1.3.1
  - llvm-openmp=18.1.7
  - llvmlite=0.42.0
  - louvain=0.8.2
  - lz4-c=1.9.4
  - markupsafe=2.1.5
  - mathjax=2.7.7
  - matplotlib=3.8.4
  - matplotlib-base=3.8.4
  - matplotlib-inline=0.1.7
  - mistune=3.0.2
  - mkl=2023.2.0
  - mkl-devel=2023.2.0
  - mkl-include=2023.2.0
  - mpc=1.3.1
  - mpfr=4.2.1
  - mpmath=1.3.0
  - mudata=0.2.3
  - multiprocess=0.70.16
  - munkres=1.1.4
  - muon=0.1.6
  - natsort=8.4.0
  - nbclient=0.10.0
  - nbconvert-core=7.16.4
  - nbformat=5.10.4
  - ncurses=6.5
  - nest-asyncio=1.6.0
  - networkx=3.3
  - notebook=7.2.1
  - notebook-shim=0.2.4
  - numba=0.59.1
  - numexpr=2.10.0
  - numpy=1.26.4
  - openjpeg=2.5.2
  - openpyxl=3.1.2
  - openssl=3.3.1
  - overrides=7.7.0
  - packaging=24.0
  - pandas=2.2.2
  - pandocfilters=1.5.0
  - parso=0.8.4
  - patsy=0.5.6
  - pexpect=4.9.0
  - pickleshare=0.7.5
  - pillow=10.3.0
  - pip=24.0
  - pkgutil-resolve-name=1.3.10
  - platformdirs=4.2.2
  - plotly=5.22.0
  - plotly-orca=1.3.1
  - pooch=1.8.2
  - prettyprinter=0.18.0
  - prometheus_client=0.20.0
  - prompt-toolkit=3.0.47
  - prompt_toolkit=3.0.47
  - protobuf=4.25.3
  - psutil=5.9.8
  - pthread-stubs=0.4
  - ptyprocess=0.7.0
  - pure_eval=0.2.2
  - py-cpuinfo=9.0.0
  - pycparser=2.22
  - pyfaidx=0.8.1.1
  - pygments=2.18.0
  - pymde=0.1.18
  - pymongo=4.7.3
  - pynndescent=0.5.12
  - pyobjc-core=10.2
  - pyobjc-framework-cocoa=10.2
  - pyparsing=3.1.2
  - pysocks=1.7.1
  - pytables=3.9.2
  - python=3.11.4
  - python-dateutil=2.9.0
  - python-fastjsonschema=2.19.1
  - python-igraph=0.11.5
  - python-json-logger=2.0.7
  - python-kaleido=0.2.1
  - python-tzdata=2024.1
  - python_abi=3.11
  - pytorch=2.2.2
  - pytz=2024.1
  - pyvcf3=1.0.3
  - pyyaml=6.0.1
  - pyzmq=26.0.3
  - radian=0.6.12
  - rchitect=0.4.6
  - readline=8.2
  - referencing=0.35.1
  - requests=2.32.3
  - rfc3339-validator=0.1.4
  - rfc3986-validator=0.1.1
  - rpds-py=0.18.1
  - scanpy=1.10.1
  - scikit-learn=1.5.0
  - scipy=1.13.1
  - seaborn=0.13.2
  - seaborn-base=0.13.2
  - send2trash=1.8.3
  - session-info=1.0.0
  - setuptools=70.0.0
  - simplejson=3.19.2
  - six=1.16.0
  - snappy=1.2.0
  - sniffio=1.3.1
  - soupsieve=2.5
  - stack_data=0.6.2
  - statsmodels=0.14.2
  - stdlib-list=0.10.0
  - sympy=1.12
  - tbb=2021.12.0
  - tenacity=8.3.0
  - terminado=0.18.1
  - texttable=1.7.0
  - threadpoolctl=3.5.0
  - tinycss2=1.3.0
  - tk=8.6.13
  - tomli=2.0.1
  - torchvision=0.17.2
  - tornado=6.4.1
  - tqdm=4.66.4
  - traitlets=5.14.3
  - types-python-dateutil=2.9.0.20240316
  - typing-extensions=4.12.2
  - typing_extensions=4.12.2
  - typing_utils=0.1.0
  - tzdata=2024a
  - umap-learn=0.5.5
  - uri-template=1.3.0
  - urllib3=2.2.1
  - wcwidth=0.2.13
  - webcolors=24.6.0
  - webencodings=0.5.1
  - websocket-client=1.8.0
  - wheel=0.43.0
  - xlrd=1.2.0
  - xorg-libxau=1.0.11
  - xorg-libxdmcp=1.1.3
  - xz=5.2.6
  - yaml=0.2.5
  - zeromq=4.3.5
  - zipp=3.19.2
  - zlib-ng=2.0.7
  - zstd=1.5.6
  - pip:
      - absl-py==2.1.0
      - astunparse==1.6.3
      - bcbio-gff==0.7.1
      - flatbuffers==24.3.25
      - gast==0.5.4
      - google-pasta==0.2.0
      - grpcio==1.64.1
      - keras==3.3.3
      - libclang==18.1.1
      - markdown==3.6
      - markdown-it-py==3.0.0
      - mdurl==0.1.2
      - ml-dtypes==0.3.2
      - namex==0.0.8
      - opt-einsum==3.3.0
      - optree==0.11.0
      - rich==13.7.1
      - tensorboard==2.16.2
      - tensorboard-data-server==0.7.2
      - tensorflow==2.16.1
      - tensorflow-io-gcs-filesystem==0.37.0
      - termcolor==2.4.0
      - werkzeug==3.0.3
      - wrapt==1.16.0

The virtual environment on my laptop (successful case):

channels:
  - pytorch
  - bioconda
  - conda-forge
dependencies:
  - adjusttext=1.0.4
  - anndata=0.10.5.post1
  - anyio=3.7.1
  - aom=3.5.0
  - appnope=0.1.3
  - argcomplete=3.3.0
  - argh=0.31.2
  - argon2-cffi=23.1.0
  - argon2-cffi-bindings=21.2.0
  - arpack=3.8.0
  - array-api-compat=1.4.1
  - arrow=1.2.3
  - asttokens=2.2.1
  - async-lru=2.0.4
  - attrs=23.1.0
  - babel=2.12.1
  - backcall=0.2.0
  - backports=1.0
  - backports.functools_lru_cache=1.6.5
  - beautifulsoup4=4.12.2
  - bleach=6.0.0
  - blosc=1.21.4
  - brotli=1.0.9
  - brotli-bin=1.0.9
  - brotli-python=1.0.9
  - bzip2=1.0.8
  - c-ares=1.19.1
  - c-blosc2=2.10.2
  - ca-certificates=2024.6.2
  - cached-property=1.5.2
  - cached_property=1.5.2
  - cairo=1.18.0
  - certifi=2024.6.2
  - cffi=1.15.1
  - charset-normalizer=3.2.0
  - colorama=0.4.6
  - colorcet=3.0.1
  - colorful=0.5.4
  - comm=0.1.4
  - contourpy=1.1.0
  - cryptography=41.0.4
  - cycler=0.11.0
  - dav1d=1.2.1
  - debugpy=1.6.8
  - decorator=5.1.1
  - defusedxml=0.7.1
  - dill=0.3.7
  - dnspython=2.4.2
  - entrypoints=0.4
  - et_xmlfile=1.1.0
  - exceptiongroup=1.1.3
  - executing=1.2.0
  - expat=2.5.0
  - ffmpeg=6.0.0
  - filelock=3.12.2
  - font-ttf-dejavu-sans-mono=2.37
  - font-ttf-inconsolata=3.000
  - font-ttf-source-code-pro=2.038
  - font-ttf-ubuntu=0.83
  - fontconfig=2.14.2
  - fonts-conda-ecosystem=1
  - fonts-conda-forge=1
  - fonttools=4.42.1
  - fqdn=1.5.1
  - freetype=2.12.1
  - fribidi=1.0.10
  - get-annotations=0.1.2
  - gettext=0.21.1
  - gffutils=0.13
  - glpk=5.0
  - gmp=6.3.0
  - gmpy2=2.1.2
  - gnutls=3.7.8
  - graphite2=1.3.13
  - h11=0.14.0
  - h2=4.1.0
  - h5py=3.9.0
  - harfbuzz=7.3.0
  - hdf5=1.14.1
  - hpack=4.0.0
  - httpcore=0.18.0
  - hyperframe=6.0.1
  - icu=73.2
  - idna=3.4
  - igraph=0.10.8
  - importlib-metadata=6.8.0
  - importlib_metadata=6.8.0
  - importlib_resources=6.0.1
  - ipykernel=6.25.1
  - ipython=8.14.0
  - isoduration=20.11.0
  - jedi=0.19.0
  - jinja2=3.1.2
  - joblib=1.3.2
  - jpeg=9e
  - json5=0.9.14
  - jsonpointer=2.0
  - jsonschema=4.19.0
  - jsonschema-specifications=2023.7.1
  - jsonschema-with-format-nongpl=4.19.0
  - jupyter-lsp=2.2.0
  - jupyter_client=8.3.0
  - jupyter_core=5.3.1
  - jupyter_events=0.7.0
  - jupyter_server=2.7.1
  - jupyter_server_terminals=0.4.4
  - jupyterlab=4.0.5
  - jupyterlab_pygments=0.2.2
  - jupyterlab_server=2.24.0
  - kaleido-core=0.2.1
  - kiwisolver=1.4.4
  - krb5=1.21.2
  - lame=3.100
  - lcms2=2.15
  - legacy-api-wrap=1.4
  - leidenalg=0.10.2
  - lerc=4.0.0
  - libabseil=20240116.2
  - libaec=1.0.6
  - libass=0.17.1
  - libblas=3.9.0
  - libbrotlicommon=1.0.9
  - libbrotlidec=1.0.9
  - libbrotlienc=1.0.9
  - libcblas=3.9.0
  - libcurl=8.2.1
  - libcxx=16.0.6
  - libdeflate=1.17
  - libedit=3.1.20191231
  - libev=4.33
  - libexpat=2.5.0
  - libffi=3.4.2
  - libgfortran=5.0.0
  - libgfortran5=12.3.0
  - libglib=2.80.0
  - libhwloc=2.9.3
  - libiconv=1.17
  - libidn2=2.3.4
  - libintl=0.22.5
  - libjpeg-turbo=2.1.4
  - liblapack=3.9.0
  - libleidenalg=0.11.1
  - libllvm14=14.0.6
  - libnghttp2=1.52.0
  - libopenblas=0.3.23
  - libopus=1.3.1
  - libpng=1.6.39
  - libprotobuf=4.25.3
  - libsodium=1.0.18
  - libsqlite=3.42.0
  - libssh2=1.11.0
  - libtasn1=4.19.0
  - libtiff=4.5.0
  - libunistring=0.9.10
  - libuv=1.48.0
  - libvpx=1.13.0
  - libwebp-base=1.3.1
  - libxcb=1.13
  - libxml2=2.11.6
  - libzlib=1.2.13
  - llvm-openmp=16.0.6
  - llvmlite=0.40.1
  - lz4-c=1.9.4
  - markupsafe=2.1.3
  - mathjax=2.7.7
  - matplotlib=3.7.2
  - matplotlib-base=3.7.2
  - matplotlib-inline=0.1.6
  - mistune=3.0.1
  - mpc=1.3.1
  - mpfr=4.2.0
  - mpmath=1.3.0
  - mudata=0.2.3
  - multiprocess=0.70.15
  - munkres=1.1.4
  - muon=0.1.6
  - natsort=8.4.0
  - nbclient=0.8.0
  - nbconvert-core=7.7.4
  - nbformat=5.9.2
  - ncurses=6.4
  - nest-asyncio=1.5.6
  - nettle=3.8.1
  - networkx=3.1
  - nodejs=20.9.0
  - notebook=7.0.2
  - notebook-shim=0.2.3
  - numba=0.57.1
  - numexpr=2.8.4
  - openh264=2.3.1
  - openjpeg=2.5.0
  - openpyxl=3.1.2
  - openssl=3.3.1
  - overrides=7.4.0
  - p11-kit=0.24.1
  - packaging=23.1
  - pandas=2.0.3
  - pandocfilters=1.5.0
  - param=2.0.2
  - parso=0.8.3
  - patsy=0.5.3
  - pcre2=10.43
  - pexpect=4.8.0
  - pickleshare=0.7.5
  - pillow=9.4.0
  - pip=23.2.1
  - pixman=0.43.4
  - pkgutil-resolve-name=1.3.10
  - platformdirs=3.10.0
  - plotly=5.16.1
  - plotly-orca=3.4.2
  - pooch=1.7.0
  - prettyprinter=0.18.0
  - prometheus_client=0.17.1
  - prompt-toolkit=3.0.39
  - prompt_toolkit=3.0.39
  - psutil=5.9.5
  - pthread-stubs=0.4
  - ptyprocess=0.7.0
  - pure_eval=0.2.2
  - py-cpuinfo=9.0.0
  - pycparser=2.21
  - pyct=0.5.0
  - pyfaidx=0.8.1.1
  - pygments=2.16.1
  - pymde=0.1.18
  - pymongo=4.5.0
  - pynndescent=0.5.11
  - pyobjc-core=9.2
  - pyobjc-framework-cocoa=9.2
  - pyparsing=3.0.9
  - pysocks=1.7.1
  - pytables=3.8.0
  - python=3.11.4
  - python-dateutil=2.8.2
  - python-fastjsonschema=2.18.0
  - python-igraph=0.11.3
  - python-json-logger=2.0.7
  - python-kaleido=0.2.1
  - python-tzdata=2023.3
  - python_abi=3.11
  - pytorch=2.0.1
  - pytz=2023.3
  - pyvcf3=1.0.3
  - pyyaml=6.0.1
  - pyzmq=25.1.1
  - radian=0.6.7
  - rchitect=0.4.1
  - readline=8.2
  - referencing=0.30.2
  - requests=2.31.0
  - rfc3339-validator=0.1.4
  - rfc3986-validator=0.1.1
  - rpds-py=0.9.2
  - scanpy=1.10.1
  - scikit-learn=1.3.0
  - scipy=1.11.2
  - seaborn=0.13.2
  - seaborn-base=0.13.2
  - send2trash=1.8.2
  - session-info=1.0.0
  - setuptools=68.1.2
  - simplejson=3.19.2
  - six=1.16.0
  - snappy=1.1.10
  - sniffio=1.3.0
  - soupsieve=2.3.2.post1
  - stack_data=0.6.2
  - statsmodels=0.14.0
  - stdlib-list=0.10.0
  - svt-av1=1.6.0
  - sympy=1.12
  - tbb=2021.11.0
  - tenacity=8.2.3
  - terminado=0.17.1
  - texttable=1.7.0
  - threadpoolctl=3.2.0
  - tinycss2=1.2.1
  - tk=8.6.12
  - tomli=2.0.1
  - torchvision=0.15.2
  - tornado=6.3.3
  - traitlets=5.9.0
  - typing_extensions=4.8.0
  - typing_utils=0.1.0
  - tzdata=2023c
  - umap-learn=0.5.5
  - uri-template=1.3.0
  - wcwidth=0.2.6
  - webcolors=1.13
  - webencodings=0.5.1
  - websocket-client=1.6.2
  - wheel=0.41.2
  - x264=1!164.3095
  - x265=3.5
  - xlrd=1.2.0
  - xorg-libxau=1.0.11
  - xorg-libxdmcp=1.1.3
  - xz=5.2.6
  - yaml=0.2.5
  - zeromq=4.3.4
  - zipp=3.16.2
  - zlib=1.2.13
  - zlib-ng=2.0.7
  - zstd=1.5.2
  - pip:
      - absl-py==1.4.0
      - astunparse==1.6.3
      - bcbio-gff==0.7.0
      - biopython==1.81
      - cachetools==5.3.1
      - click==8.1.7
      - flatbuffers==23.5.26
      - gast==0.4.0
      - geoparse==2.0.3
      - gffpandas==1.2.0
      - google-auth==2.22.0
      - google-auth-oauthlib==1.0.0
      - google-pasta==0.2.0
      - grpcio==1.57.0
      - imageio==2.34.1
      - keras==2.13.1
      - lazy-loader==0.4
      - libclang==16.0.6
      - louvain==0.8.2
      - markdown==3.4.4
      - numpy==1.24.3
      - oauthlib==3.2.2
      - opt-einsum==3.3.0
      - protobuf==4.24.1
      - pyasn1==0.5.0
      - pyasn1-modules==0.3.0
      - requests-oauthlib==1.3.1
      - rsa==4.9
      - scikit-image==0.24.0
      - tensorboard==2.13.0
      - tensorboard-data-server==0.7.1
      - tensorflow==2.13.0
      - tensorflow-estimator==2.13.0
      - tensorflow-macos==2.13.0
      - termcolor==2.3.0
      - tifffile==2024.6.18
      - tqdm==4.66.1
      - typing-extensions==4.5.0
      - urllib3==1.26.16
      - werkzeug==2.3.7
      - wrapt==1.15.0

Minimal code sample

sc.pp.scrublet(adata)

Error output

No response

Versions

# Successful case
-----
anndata     0.10.5.post1
scanpy      1.10.1
-----
PIL                 9.4.0
astunparse          1.6.3
cffi                1.15.1
colorama            0.4.6
cycler              0.10.0
cython_runtime      NA
dateutil            2.8.2
defusedxml          0.7.1
dill                0.3.7
gmpy2               2.1.2
google              NA
h5py                3.9.0
igraph              0.11.3
joblib              1.3.2
kiwisolver          1.4.4
legacy_api_wrap     NA
leidenalg           0.10.2
llvmlite            0.40.1
louvain             0.8.2
matplotlib          3.7.2
mpl_toolkits        NA
mpmath              1.3.0
natsort             8.4.0
numba               0.57.1
numexpr             2.8.4
numpy               1.24.4
opt_einsum          v3.3.0
packaging           23.1
pandas              2.0.3
pkg_resources       NA
plotly              5.16.1
psutil              5.9.5
pyparsing           3.0.9
pytz                2023.3
scipy               1.11.2
session_info        1.0.0
six                 1.16.0
sklearn             1.3.0
sympy               1.12
texttable           1.7.0
threadpoolctl       3.2.0
torch               2.0.1
tqdm                4.66.2
typing_extensions   NA
wcwidth             0.2.6
yaml                6.0.1
-----
Python 3.11.4 | packaged by conda-forge | (main, Jun 10 2023, 18:08:41) [Clang 15.0.7 ]
macOS-14.3-arm64-arm-64bit
-----
Session information updated at 2024-06-22 00:24



# Failed case
-----
anndata     0.10.7
scanpy      1.10.1
-----
PIL                 10.3.0
astunparse          1.6.3
cffi                1.16.0
colorama            0.4.6
cycler              0.12.1
cython_runtime      NA
dateutil            2.9.0
defusedxml          0.7.1
dill                0.3.8
google              NA
h5py                3.11.0
igraph              0.11.5
joblib              1.4.2
kiwisolver          1.4.5
legacy_api_wrap     NA
leidenalg           0.10.2
llvmlite            0.42.0
louvain             0.8.2
matplotlib          3.8.4
mpl_toolkits        NA
natsort             8.4.0
numba               0.59.1
numexpr             2.10.0
numpy               1.26.4
optree              0.11.0
packaging           24.0
pandas              2.2.2
pkg_resources       NA
plotly              5.22.0
psutil              5.9.8
pyparsing           3.1.2
pytz                2024.1
scipy               1.13.1
session_info        1.0.0
six                 1.16.0
sklearn             1.5.0
texttable           1.7.0
threadpoolctl       3.5.0
torch               2.2.2
torchgen            NA
tqdm                4.66.4
typing_extensions   NA
wcwidth             0.2.13
yaml                6.0.1
-----
Python 3.11.4 | packaged by conda-forge | (main, Jun 10 2023, 18:10:28) [Clang 15.0.7 ]
macOS-14.4.1-x86_64-i386-64bit
-----
Session information updated at 2024-06-22 00:26

No source file or test is named. Start by reproducing sc.pp.scrublet(adata) with the reported Scanpy 1.10.1 environments and compare the dependency and platform differences at the PCA step. Done means identifying the dependency or regression responsible and documenting a verified resolution.