infercnvpy.pl.chromosome_heatmap shows 1e9 map values for copykat results
zsfrbkv opened this issue · comments
Report
Thank you for your wrapper for copykat, very useful!
I was wondering what can be done to remove values over 1e9 from the plot?
I use the following code on my dataset:
infercnvpy.tl.copykat(adata_ref1, inplace=True, norm_cell_names=adata_ref1[adata_ref1.obs['annotation_level_2'].isin(['Myeloid', 'Vascular', 'Lymphoid'])].obs.index)
infercnvpy.pl.chromosome_heatmap(adata_ref1, groupby="annotation_level_3")
Version information
anndata 0.8.0
infercnvpy 0.4.2
matplotlib 3.7.1
numpy 1.23.5
pandas 1.5.3
rpy2 3.5.10
scanpy 1.9.3
scipy 1.10.1
seaborn 0.12.2
session_info 1.0.0
tqdm 4.65.0
PIL 9.4.0
asttokens NA
backcall 0.2.0
backports NA
cffi 1.15.1
comm 0.1.3
cycler 0.10.0
cython_runtime NA
dateutil 2.8.2
debugpy 1.6.6
decorator 5.1.1
defusedxml 0.7.1
executing 1.2.0
gtfparse NA
h5py 3.8.0
igraph 0.10.4
importlib_metadata NA
importlib_resources NA
ipykernel 6.22.0
ipython_genutils 0.2.0
ipywidgets 8.0.6
jedi 0.18.2
jinja2 3.1.2
joblib 1.2.0
jupyter_server 2.5.0
kiwisolver 1.4.4
leidenalg 0.9.1
llvmlite 0.39.1
markupsafe 2.1.2
matplotlib_inline 0.1.6
mpl_toolkits NA
natsort 8.3.1
numba 0.56.4
packaging 23.0
parso 0.8.3
pexpect 4.8.0
pickleshare 0.7.5
pkg_resources NA
platformdirs 3.2.0
polars 0.16.16
prompt_toolkit 3.0.38
psutil 5.9.4
ptyprocess 0.7.0
pure_eval 0.2.2
pydev_ipython NA
pydevconsole NA
pydevd 2.9.5
pydevd_file_utils NA
pydevd_plugins NA
pydevd_tracing NA
pygments 2.14.0
pyparsing 3.0.9
pyreadr 0.4.7
pytz 2023.3
pytz_deprecation_shim NA
setuptools 67.6.1
six 1.16.0
sklearn 1.2.2
stack_data 0.6.2
statsmodels 0.13.5
texttable 1.6.7
threadpoolctl 3.1.0
tornado 6.2
traitlets 5.9.0
typing_extensions NA
tzlocal NA
wcwidth 0.2.6
yaml 6.0
zipp NA
zmq 25.0.2
IPython 8.11.0
jupyter_client 8.1.0
jupyter_core 5.3.0
jupyterlab 3.5.0
notebook 6.5.3
Python 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0]
Linux-3.10.0-1160.76.1.el7.x86_64-x86_64-with-glibc2.17
Session information updated at 2023-04-04 13:42
Hi @zseferbekova,
in principle, you can manipulate the matrix in adata.obsm["X_cnv"] before plotting. However, looking at the matrix, it seems something is wrong, as you have literally no variation between the chromosomal positions.
Here are some suggestions what you could check:
- try running the infercnv algorithm to check if you have the same problem
- try running copykat without specifying "normal" cells to check if the problem could be related to that.
Cheers,
Gregor
Thank you, Gregor @grst!
I was thinking the same thing, and indeed in the resultant matrix, there are values around 1e9. However, CopyKat itself saves plots that seem quite normal, e.g. (it creates them automatically after finishing the run):
So I was wondering, is it the plotting function that is not adjusted for plotting CopyKat results?
I'm wondering if the problem is just the plotting, or if something goes wrong when loading the copykat results into python.
Could you maybe try to plot adata.obsm["X_cnv"] with an external plotting library (e.g. seaborn clustermap)?
I tried sns.clustermap on adata.obsm["X_cnv"] and the results are the same. however, if I import copykat_result_copykat_CNA_results.txt manually and plot it, the result is somewhat similar to the plot above:
cnvs = pd.read_table('copykat_full_transcriptome/copykat_result_copykat_CNA_results.txt')
cnvs = cnvs.iloc[:, 3:]
sns.clustermap(cnvs.T.values)
so I believe for copykat one needs to preprocess the resultant matrix before plotting it. however, I could not find any docs for copykat with a detailed explanation of what is needed to be done :(