inferCNV output matrix has the same dimension with different window size
YubinXie opened this issue · comments
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Hi, thanks for the python version of inferCNV. I am trying to use the output from inferCNV to see where are the chromosome location of each CNV. I could not find the info about the output matrix in the document.
In my case, I got a Cell number by 2118 matrix. How do I interpret the 2118 here? And I was assuming it is the average mean for genes in that window. However, when I tried 100 and 250 for the window size but the matrix has the same shape. it would be great if you can provide some insights on this! Thanks!
Version information
anndata 0.8.0
infercnvpy 0.4.1
matplotlib 3.6.2
numpy 1.21.6
pandas 1.5.0
phenograph 1.5.7
scanpy 1.9.1
scipy 1.9.1
seaborn 0.12.2
session_info 1.0.0
sklearn 1.1.2
statsmodels 0.13.2
tqdm 4.64.1
PIL 9.2.0
asciitree NA
asttokens NA
backcall 0.2.0
beta_ufunc NA
binom_ufunc NA
brotli NA
cffi 1.15.1
click 8.1.3
cloudpickle 2.2.0
colorama 0.4.5
cupy 11.2.0
cupy_backends NA
cupyx NA
cycler 0.10.0
cython_runtime NA
cytoolz 0.12.0
dask 2022.9.2
dateutil 2.8.2
debugpy 1.6.3
decorator 5.1.1
defusedxml 0.7.1
distributed 2022.9.2
entrypoints 0.4
executing 1.1.1
fasteners 0.17.3
fastrlock 0.8
fsspec 2022.8.2
google NA
gtfparse NA
h5py 3.7.0
heapdict NA
hypergeom_ufunc NA
igraph 0.10.1
importlib_metadata NA
ipykernel 6.15.2
ipywidgets 8.0.2
jedi 0.18.1
jinja2 3.1.2
joblib 1.2.0
kiwisolver 1.4.4
leidenalg 0.9.0
llvmlite 0.39.1
locket NA
lz4 4.0.0
markupsafe 2.1.1
matplotlib_inline 0.1.6
mpl_toolkits NA
msgpack 1.0.4
natsort 8.2.0
nbinom_ufunc NA
ncf_ufunc NA
numba 0.56.3
numcodecs 0.10.2
packaging 21.3
parso 0.8.3
pexpect 4.8.0
pickleshare 0.7.5
pkg_resources NA
polars 0.16.14
prompt_toolkit 3.0.31
psutil 5.9.2
ptyprocess 0.7.0
pure_eval 0.2.2
pyarrow 9.0.0
pydev_ipython NA
pydevconsole NA
pydevd 2.8.0
pydevd_file_utils NA
pydevd_plugins NA
pydevd_tracing NA
pygments 2.13.0
pynvml 11.4.1
pyparsing 3.0.9
pyreadr 0.4.7
pytz 2022.4
setuptools 60.10.0
six 1.16.0
sortedcontainers 2.4.0
sphinxcontrib NA
stack_data 0.5.1
tblib 1.7.0
texttable 1.6.4
threadpoolctl 3.1.0
tlz 0.12.0
toolz 0.12.0
tornado 6.1
traitlets 5.4.0
typing_extensions NA
urllib3 1.26.11
wcwidth 0.2.5
xarray 2022.10.0
yaml 6.0
zarr 2.13.3
zict 2.2.0
zipp NA
zmq 24.0.1
zoneinfo NA
IPython 8.4.0
jupyter_client 7.0.6
jupyter_core 4.11.1
Python 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:56:21) [GCC 10.3.0]
Linux-3.10.0-957.12.2.el7.x86_64-x86_64-with-glibc2.17
Session information updated at 2023-03-24 20:16
@YubinXie I think you are confusing two different parameters. One thing is the window_size: size of the running window (number of genes in to include in the window); the other parameter is the step : only compute every nth running window where n = step. Set to 1 to compute all windows. If you change the first you change how many genes are used for each step, but you won't change the number of steps, that is why you get the same size matrix.
@YubinXie I think you are confusing two different parameters. One thing is the
window_size: size of the running window (number of genes in to include in the window); the other parameter is thestep: only compute every nth running window where n = step. Set to 1 to compute all windows. If you change the first you change how many genes are used for each step, but you won't change the number of steps, that is why you get the same size matrix.
Oh I see. so as the default step as 10, each value in the output is the the average of 10 genes CNV score. Now it makes sense!