aertslab
pySCENIC cannot reproduce AUCell + tSNE projection from a tutorial
Discussed in https://github.com/aertslab/SCENIC/discussions/386
Originally posted by ninhleba March 20, 2023 Hi,
I've been testing pySCENIC by following this tutorial: http://htmlpreview.github.io/?https://github.com/aertslab/SCENICprotocol/blob/master/notebooks/SCENIC%20Protocol%20-%20Case%20study%20-%20Cancer%20data%20sets.html. The dataset I tried was the first one (Accession ID: GSE115978, Cancer type: SKCM). Everything looked pretty compatible until the AUCell + tSNE projection, which didn't seem to be able to cluster cells by cell type at all, in contrast with the corresponding plot in the tutorial.
Previous steps such as grn, ctx and aucell ran smoothly, and the clustermap based on binarized AUCell matrix I obtained also suggests tSNE computed from AUCell scores should be able to cluster out at least half the number of cell types.
I understand the nature of the algorithm makes the outputs of different runs vary slightly from one another but I don't think they should by this much. I don't think the error stems from tSNE because the PCA + tSNE projection looks like it's supposed to.
I would really appreciate it if someone could share their thoughts on this.
Here are the databases that I used:
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Human TFs: https://github.com/aertslab/pySCENIC/blob/master/resources/lambert2018.txt
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Ranking databases: https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/
- hg19-500bp-upstream-10species.mc9nr.genes_vs_motifs.rankings.feather
- hg19-tss-centered-5kb-10species.mc9nr.genes_vs_motifs.rankings.feather
- hg19-tss-centered-10kb-10species.mc9nr.genes_vs_motifs.rankings.feather
-
Motifs annotation: https://resources.aertslab.org/cistarget/motif2tf/motifs-v9-nr.hgnc-m0.001-o0.0.tbl
Here is my session info:
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anndata 0.8.0
cytoolz 0.12.1
matplotlib 3.7.1
numpy 1.23.5
pandas 1.5.3
pyscenic 0.12.1
scanpy 1.9.3
seaborn 0.12.2
session_info 1.0.0
-----
PIL 9.4.0
appnope 0.1.2
asttokens NA
attr 22.1.0
backcall 0.2.0
boltons NA
cffi 1.15.1
cloudpickle 2.2.1
comm 0.1.2
ctxcore 0.2.0
cycler 0.10.0
cython_runtime NA
dask 2023.3.0
dateutil 2.8.2
debugpy 1.5.1
decorator 5.1.1
defusedxml 0.7.1
entrypoints 0.4
executing 0.8.3
frozendict 2.3.5
fsspec 2023.3.0
h5py 3.8.0
ipykernel 6.19.2
ipython_genutils 0.2.0
jedi 0.18.1
jinja2 3.1.2
joblib 1.2.0
jupyter_server 1.23.4
kiwisolver 1.4.4
llvmlite 0.39.1
loompy 3.0.7
lxml 4.9.1
markupsafe 2.1.1
matplotlib_inline 0.1.6
mpl_toolkits NA
natsort 8.3.1
networkx 3.0
numba 0.56.4
numexpr 2.8.4
numpy_groupies 0.9.20
openpyxl 3.1.1
packaging 22.0
parso 0.8.3
pexpect 4.8.0
pickleshare 0.7.5
pkg_resources NA
platformdirs 2.5.2
prompt_toolkit 3.0.36
psutil 5.9.0
ptyprocess 0.7.0
pure_eval 0.2.2
pyarrow 11.0.0
pycparser 2.21
pydev_ipython NA
pydevconsole NA
pydevd 2.6.0
pydevd_concurrency_analyser NA
pydevd_file_utils NA
pydevd_plugins NA
pydevd_tracing NA
pygments 2.11.2
pyparsing 3.0.9
pytz 2022.7
scipy 1.10.1
setuptools 65.6.3
six 1.16.0
sklearn 1.2.2
stack_data 0.2.0
statsmodels 0.13.5
tblib 1.7.0
threadpoolctl 3.1.0
tlz 0.12.1
toolz 0.12.0
tornado 6.2
tqdm 4.65.0
traitlets 5.7.1
typing_extensions NA
wcwidth 0.2.5
yaml 6.0
zmq 23.2.0
zoneinfo NA
-----
IPython 8.10.0
jupyter_client 7.4.9
jupyter_core 5.2.0
jupyterlab 3.5.3
notebook 6.5.2
-----
Python 3.10.9 (main, Mar 8 2023, 04:44:36) [Clang 14.0.6 ]
macOS-10.16-x86_64-i386-64bit
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Session information updated at 2023-03-20 14:01
