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ドキュメント https://cellbender.readthedocs.io/en/latest/index.html
github https://github.com/broadinstitute/CellBender
pipy https://pypi.org/project/cellbender/
git clone https://github.com/yyuu/pyenv.git /apps/pyenv
export PYENV_ROOT=/apps/pyenv
export PATH=$PYENV_ROOT/bin:$PATH
pyenv install miniforge3-26.3.2-1
source /apps/pyenv/versions/miniforge3-26.3.2-1/etc/profile.d/conda.sh
conda update conda
(既に環境があるなら)
source /apps/pyenv/versions/miniforge3-26.3.2-1/etc/profile.d/conda.sh[root@r9 ~]# cat /etc/redhat-release
Rocky Linux release 9.7 (Blue Onyx)
[root@r9 ~]# cat /proc/driver/nvidia/version
NVRM version: NVIDIA UNIX Open Kernel Module for x86_64 595.58.03 Release Build (dvs-builder@U22-I3-AM25-28-3) Tue Mar 17 19:55:10 UTC 2026
GCC version: gcc version 11.5.0 20240719 (Red Hat 11.5.0-11) (GCC)
[root@r9 ~]# nvidia-smi -L
GPU 0: NVIDIA RTX PRO 2000 Blackwell (UUID: GPU-40660e37-0d35-d4f4-294a-eee3fe83049e)
[root@r9 ~]#[root@r9 ~]# conda create -n cellbender python=3.12
[root@r9 ~]# conda activate cellbender
(cellbender) [root@r9 ~]#
(cellbender) [root@r9 ~]# conda install -c anaconda pytables
(cellbender) [root@r9 ~]# pip install torch
(cellbender) [root@r9 ~]# conda list
# packages in environment at /apps/pyenv/versions/miniforge3-26.3.2-1/envs/cellbender:
#
# Name Version Build Channel
_openmp_mutex 4.5 20_gnu conda-forge
aws-c-auth 0.10.1 h47b2149_0 anaconda
aws-c-cal 0.9.13 h1b28b03_0 anaconda
aws-c-common 0.12.6 h47b2149_0 anaconda
aws-c-compression 0.3.2 h47b2149_0 anaconda
aws-c-http 0.10.13 h47b2149_0 anaconda
aws-c-io 0.26.3 h1b29dbc_0 anaconda
aws-c-s3 0.12.0 h1b28b03_1 anaconda
aws-c-sdkutils 0.2.4 h47b2149_2 anaconda
aws-checksums 0.2.10 h47b2149_0 anaconda
blas 1.0 openblas anaconda
blosc 1.21.6 he440d0b_1 conda-forge
bzip2 1.0.8 hda65f42_9 conda-forge
c-ares 1.34.6 hd44998d_0 anaconda
c-blosc2 2.22.0 hc31b594_1 conda-forge
ca-certificates 2026.5.20 hbd8a1cb_0 conda-forge
cuda-bindings 13.3.1 pypi_0 pypi
cuda-pathfinder 1.5.5 pypi_0 pypi
cuda-toolkit 13.0.2 pypi_0 pypi
filelock 3.29.1 pypi_0 pypi
fsspec 2026.4.0 pypi_0 pypi
gettext 0.25.1 h92eb808_0 anaconda
gettext-tools 0.25.1 h6a67909_0 anaconda
hdf5 2.0.0 h91801a2_3 anaconda
icu 78.3 h84d19a5_0 anaconda
jansson 2.15.0 hbcba0ee_0 anaconda
jinja2 3.1.6 pypi_0 pypi
ld_impl_linux-64 2.45.1 default_hbd61a6d_102 conda-forge
libasprintf 0.25.1 hf2ab22a_0 anaconda
libasprintf-devel 0.25.1 hf2ab22a_0 anaconda
libbrotlicommon 1.2.0 h32cd6e7_0 anaconda
libbrotlidec 1.2.0 ha2c5f68_0 anaconda
libbrotlienc 1.2.0 h2e96acb_0 anaconda
libcurl 8.20.0 hd8fa685_1 anaconda
libev 4.33 h7f8727e_1 anaconda
libexpat 2.8.1 hecca717_0 conda-forge
libffi 3.5.2 h3435931_0 conda-forge
libgcc 15.2.0 he0feb66_19 conda-forge
libgcc-ng 15.2.0 h69a702a_19 conda-forge
libgettextpo 0.25.1 hf2ab22a_0 anaconda
libgettextpo-devel 0.25.1 hf2ab22a_0 anaconda
libgfortran 15.2.0 h166f726_7 anaconda
libgfortran5 15.2.0 hc633d37_7 anaconda
libgomp 15.2.0 he0feb66_19 conda-forge
libiconv 1.18 h75a1612_0 anaconda
libidn2 2.3.8 hf80d704_0 anaconda
libkrb5 1.22.1 h869c75e_1 anaconda
liblzma 5.8.3 hb03c661_0 conda-forge
liblzma-devel 5.8.3 hb03c661_0 conda-forge
libnghttp2 1.69.0 hc59f8b6_0 anaconda
libnsl 2.0.1 hb9d3cd8_1 conda-forge
libopenblas 0.3.31 hf7dbefb_2 anaconda
libsqlite 3.53.2 h0c1763c_0 conda-forge
libssh2 1.11.1 hcf80075_0 conda-forge
libstdcxx 15.2.0 h934c35e_19 conda-forge
libstdcxx-ng 15.2.0 hdf11a46_19 conda-forge
libunistring 1.4.2 h34b0ebb_0 anaconda
libuuid 2.42.1 h5347b49_0 conda-forge
libxcrypt 4.4.36 hd590300_1 conda-forge
libxml2 2.14.6 hf2a51f9_1 anaconda
libzlib 1.3.2 h25fd6f3_2 conda-forge
lmdb 0.9.31 hb25bd0a_0 anaconda
lz4-c 1.10.0 h5888daf_1 conda-forge
lzo 2.10 h7b6447c_2 anaconda
markupsafe 3.0.3 pypi_0 pypi
mpmath 1.3.0 pypi_0 pypi
ncurses 6.6 hdb14827_0 conda-forge
networkx 3.6.1 pypi_0 pypi
numexpr 2.14.1 py312h5c6250f_1 anaconda
numpy 2.4.6 py312h35deafb_0 anaconda
numpy-base 2.4.6 py312h4bc27c9_0 anaconda
nvidia-cublas 13.1.1.3 pypi_0 pypi
nvidia-cuda-cupti 13.0.85 pypi_0 pypi
nvidia-cuda-nvrtc 13.0.88 pypi_0 pypi
nvidia-cuda-runtime 13.0.96 pypi_0 pypi
nvidia-cudnn-cu13 9.20.0.48 pypi_0 pypi
nvidia-cufft 12.0.0.61 pypi_0 pypi
nvidia-cufile 1.15.1.6 pypi_0 pypi
nvidia-curand 10.4.0.35 pypi_0 pypi
nvidia-cusolver 12.0.4.66 pypi_0 pypi
nvidia-cusparse 12.6.3.3 pypi_0 pypi
nvidia-cusparselt-cu13 0.8.1 pypi_0 pypi
nvidia-nccl-cu13 2.29.7 pypi_0 pypi
nvidia-nvjitlink 13.0.88 pypi_0 pypi
nvidia-nvshmem-cu13 3.4.5 pypi_0 pypi
nvidia-nvtx 13.0.85 pypi_0 pypi
openssl 3.6.2 h35e630c_0 conda-forge
packaging 26.2 pyhc364b38_0 conda-forge
pip 26.1.2 pyh8b19718_0 conda-forge
py-cpuinfo 9.0.0 py312h06a4308_0 anaconda
pytables 3.11.1 py312h4db2897_0 anaconda
python 3.12.13 hd63d673_0_cpython conda-forge
readline 8.3 h853b02a_0 conda-forge
s2n 1.6.2 h02aa81b_0 anaconda
setuptools 81.0.0 pypi_0 pypi
snappy 1.2.1 h6a678d5_0 anaconda
sympy 1.14.0 pypi_0 pypi
tk 8.6.13 noxft_h366c992_103 conda-forge
torch 2.12.0 pypi_0 pypi
triton 3.7.0 pypi_0 pypi
typing-extensions 4.15.0 py312h06a4308_0 anaconda
typing_extensions 4.15.0 py312h06a4308_0 anaconda
tzdata 2025c hc9c84f9_1 conda-forge
wheel 0.47.0 pyhd8ed1ab_0 conda-forge
xz 5.8.3 ha02ee65_0 conda-forge
xz-gpl-tools 5.8.3 ha02ee65_0 conda-forge
xz-tools 5.8.3 hb03c661_0 conda-forge
zlib 1.3.2 h25fd6f3_2 conda-forge
zlib-ng 2.3.3 hcff5ade_0 anaconda
zstd 1.5.7 hb78ec9c_6 conda-forge
(cellbender) [root@r9 ~]#(cellbender) [root@r9 ~]# python
Python 3.12.13 | packaged by conda-forge | (main, Mar 5 2026, 16:50:00) [GCC 14.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.cuda.is_available())
True
>>> print(torch.cuda.get_device_name())
NVIDIA RTX PRO 2000 Blackwell
>>> quit();
(cellbender) [root@r9 ~]#(cellbender) [root@r9 ~]# cd /apps
(cellbender) [root@r9 apps]# git clone https://github.com/broadinstitute/CellBender.git
(cellbender) [root@r9 apps]# pip install -e CellBender
(cellbender) [root@r9 apps]# conda deactivate
[root@r9 ~]#「/apps/modulefiles/CellBender」
#%Module
set root /apps/pyenv/versions/miniforge3-26.3.2-1/envs/cellbender
prepend-path PATH $root/bin
prepend-path LD_LIBRARY_PATH $root/lib[saber@r9 ~]$ module load CellBender
[saber@r9 ~]$ mkdir test
[saber@r9 ~]$ cd test/
[saber@r9 test]$ git clone https://github.com/broadinstitute/CellBender
[saber@r9 test]$ cd CellBender/
[saber@r9 CellBender]$ cd examples/remove_background/
[saber@r9 remove_background]$ python generate_tiny_10x_dataset.py
Downloading heart10k (CellRanger 3.0.0, v3 Chemistry)...
--2026-06-08 20:15:30-- https://cf.10xgenomics.com/samples/cell-exp/3.0.0/heart_10k_v3/heart_10k_v3_raw_feature_bc_matrix.h5
Resolving cf.10xgenomics.com (cf.10xgenomics.com)... 104.18.1.173, 104.18.0.173, 2606:4700::6812:1ad, ...
Connecting to cf.10xgenomics.com (cf.10xgenomics.com)|104.18.1.173|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 167177525 (159M) [binary/octet-stream]
Saving to: ‘heart10k_raw_feature_bc_matrix.h5’
heart10k_raw_feature_bc_matrix.h5 100%[==========================================================================>] 159.43M 32.0MB/s in 5.7s
2026-06-08 20:15:37 (28.0 MB/s) - ‘heart10k_raw_feature_bc_matrix.h5’ saved [167177525/167177525]
Loading the dataset...
/apps/pyenv/versions/miniforge3-26.3.2-1/envs/cellbender/lib/python3.12/site-packages/legacy_api_wrap/__init__.py:88: FutureWarning: The dtype argument is deprecated and will be removed in late 2024.
return fn(*args_all, **kw)
Raw dataset size (6794880, 31053)
Trimming heart10k (CellRanger 3.0.0, v3 Chemistry)...
Number of genes in the trimmed dataset: 100
Number of barcodes in the trimmed dataset: 37760
Expected number of cells in the trimmed dataset: 500
AnnData object with n_obs × n_vars = 37760 × 100
var: 'gene_id', 'genome', 'feature_type'
uns: 'cellranger_version'
Saving the trimmed dataset as tiny_raw_feature_bc_matrix.h5ad ...
Done!
[saber@r9 remove_background]$
[saber@r9 remove_background]$ cellbender remove-background \
--input tiny_raw_feature_bc_matrix.h5ad \
--output tiny_output.h5 \
--expected-cells 500 \
--total-droplets-included 2000 \
--cuda
:
:
[saber@r9 remove_background]$ ls -lrt
total 205908
-rw-r--r-- 1 saber saber 167177525 Nov 15 2018 heart10k_raw_feature_bc_matrix.h5
-rwxr-xr-x 1 saber saber 3058 Jun 8 20:07 generate_tiny_10x_dataset.py
-rw-r--r-- 1 saber saber 14660728 Jun 8 20:15 tiny_raw_feature_bc_matrix.h5ad
-rw-r--r-- 1 saber saber 2134377 Jun 8 20:17 tiny_output_posterior.h5
-rw-r--r-- 1 saber saber 24664467 Jun 8 20:17 ckpt.tar.gz
-rw-r--r-- 1 saber saber 118891 Jun 8 20:17 tiny_output.pdf
-rw-r--r-- 1 saber saber 12369 Jun 8 20:17 tiny_output_cell_barcodes.csv
-rw-r--r-- 1 saber saber 940820 Jun 8 20:17 tiny_output.h5
-rw-r--r-- 1 saber saber 351137 Jun 8 20:17 tiny_output_filtered.h5
-rw-r--r-- 1 saber saber 573 Jun 8 20:17 tiny_output_metrics.csv
-rw-r--r-- 1 saber saber 740831 Jun 8 20:17 tiny_output_report.html
-rw-r--r-- 1 saber saber 18246 Jun 8 20:17 tiny_output.log
[saber@r9 remove_background]$
