Stream 1.1 Error
bellenger-l opened this issue · 6 comments
bellenger-l commented
Hello,
I'm having trouble upgrading the version of STREAM. While before I had no errors, now none of my scripts work.
I work with conda environments and here is the version of the main packages:
With STREAM 1.0 (where everything is fine) :
print(stream.__version__)
print(scanpy.__version__)
print(pandas.__version__)
print(matplotlib.__version__)
print(numpy.__version__)
1.0
1.6.0
1.0.5
3.2.2
1.19.2
But when I upgrade only the stream package to 1.1 I have the following error :
stream.filter_features(adata,min_n_cells = 5, assay = 'rna')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_977625/1328372756.py in <module>
----> 1 st.filter_features(adata,min_n_cells = 5, assay = 'rna')
~/miniconda3/envs/stream_1.1_ter/lib/python3.7/site-packages/stream/core.py in filter_features(adata, min_n_cells, max_n_cells, min_pct_cells, max_pct_cells, min_n_counts, max_n_counts, expr_cutoff, assay)
627 else:
628 n_counts = np.sum(adata.X,axis=0).astype(int)
--> 629 adata.var['n_counts'] = n_counts
630 if('n_cells' in adata.var_keys()):
631 n_cells = adata.var['n_cells']
~/miniconda3/envs/stream_1.1_ter/lib/python3.7/site-packages/pandas/core/frame.py in __setitem__(self, key, value)
2936 else:
2937 # set column
-> 2938 self._set_item(key, value)
2939
2940 def _setitem_slice(self, key, value):
~/miniconda3/envs/stream_1.1_ter/lib/python3.7/site-packages/pandas/core/frame.py in _set_item(self, key, value)
2998
2999 self._ensure_valid_index(value)
-> 3000 value = self._sanitize_column(key, value)
3001 NDFrame._set_item(self, key, value)
3002
~/miniconda3/envs/stream_1.1_ter/lib/python3.7/site-packages/pandas/core/frame.py in _sanitize_column(self, key, value, broadcast)
3634
3635 # turn me into an ndarray
-> 3636 value = sanitize_index(value, self.index, copy=False)
3637 if not isinstance(value, (np.ndarray, Index)):
3638 if isinstance(value, list) and len(value) > 0:
~/miniconda3/envs/stream_1.1_ter/lib/python3.7/site-packages/pandas/core/internals/construction.py in sanitize_index(data, index, copy)
609
610 if len(data) != len(index):
--> 611 raise ValueError("Length of values does not match length of index")
612
613 if isinstance(data, ABCIndexClass) and not copy:
ValueError: Length of values does not match length of index
I also tried to create another conda environment by only fixing the version of STREAM :
print(stream.__version__)
print(scanpy.__version__)
print(pandas.__version__)
print(matplotlib.__version__)
print(numpy.__version__)
1.1
1.6.0
1.3.5
3.5.0
1.20.3
But I have about the same error when I run stream.filter_features(adata,min_n_cells = 5, assay = 'rna') :
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_978038/1328372756.py in <module>
----> 1 st.filter_features(adata,min_n_cells = 5, assay = 'rna')
~/miniconda3/envs/stream_1.1_bis/lib/python3.7/site-packages/stream/core.py in filter_features(adata, min_n_cells, max_n_cells, min_pct_cells, max_pct_cells, min_n_counts, max_n_counts, expr_cutoff, assay)
627 else:
628 n_counts = np.sum(adata.X,axis=0).astype(int)
--> 629 adata.var['n_counts'] = n_counts
630 if('n_cells' in adata.var_keys()):
631 n_cells = adata.var['n_cells']
~/miniconda3/envs/stream_1.1_bis/lib/python3.7/site-packages/pandas/core/frame.py in __setitem__(self, key, value)
3610 else:
3611 # set column
-> 3612 self._set_item(key, value)
3613
3614 def _setitem_slice(self, key: slice, value):
~/miniconda3/envs/stream_1.1_bis/lib/python3.7/site-packages/pandas/core/frame.py in _set_item(self, key, value)
3782 ensure homogeneity.
3783 """
-> 3784 value = self._sanitize_column(value)
3785
3786 if (
~/miniconda3/envs/stream_1.1_bis/lib/python3.7/site-packages/pandas/core/frame.py in _sanitize_column(self, value)
4507
4508 if is_list_like(value):
-> 4509 com.require_length_match(value, self.index)
4510 return sanitize_array(value, self.index, copy=True, allow_2d=True)
4511
~/miniconda3/envs/stream_1.1_bis/lib/python3.7/site-packages/pandas/core/common.py in require_length_match(data, index)
530 if len(data) != len(index):
531 raise ValueError(
--> 532 "Length of values "
533 f"({len(data)}) "
534 "does not match length of index "
ValueError: Length of values (1) does not match length of index (32738)
And it's seems to be a numpy error but I am not very familiar with python...
What do you recommend for a functional STREAM 1.1 environment?
Best,
Lea
huidongchen commented
Hi thanks for letting us know. Instead of upgrading it to the latest version, can you install the latest version from scratch? This should help solve most of the incompatible package issues.
tianchen2019 commented
Hi thanks for letting us know. Instead of upgrading it to the latest version, can you install the latest version from scratch? This should help solve most of the incompatible package issues.
i installed the latest version using the following code:
conda create -n stream python=3.7
conda activate stream
conda install -c bioconda stream
But, when i run
import stream as st
adata=st.read(file_name='/outs/filtered_peak_bc_matrix/matrix.mtx',
file_sample='/outs/filtered_peak_bc_matrix/barcodes.tsv',
file_feature='/outs/filtered_peak_bc_matrix/peaks.bed',
file_format='mtx',workdir='./result_atac')
adata.var.index =adata.var[0].astype(str) + '' + adata.var[1].astype(str) +'' + adata.var[2].astype(str)
st.cal_qc(adata2,assay='atac')
the error is
ValueError Traceback (most recent call last)
/tmp/ipykernel_352024/3261032759.py in
----> 1 st.cal_qc(adata,assay='atac')
~/app/ENTER/envs/stream1/lib/python3.7/site-packages/stream/core.py in cal_qc(adata, expr_cutoff, assay)
438
439 n_counts = adata.X.sum(axis=0)
--> 440 adata.var['n_counts'] = n_counts
441 n_cells = (adata.X>=expr_cutoff).sum(axis=0)
442 adata.var['n_cells'] = n_cells
~/app/ENTER/envs/stream1/lib/python3.7/site-packages/pandas/core/frame.py in setitem(self, key, value)
3610 else:
3611 # set column
-> 3612 self._set_item(key, value)
3613
3614 def _setitem_slice(self, key: slice, value):
~/app/ENTER/envs/stream1/lib/python3.7/site-packages/pandas/core/frame.py in _set_item(self, key, value)
3782 ensure homogeneity.
3783 """
-> 3784 value = self._sanitize_column(value)
3785
3786 if (
~/app/ENTER/envs/stream1/lib/python3.7/site-packages/pandas/core/frame.py in _sanitize_column(self, value)
4507
4508 if is_list_like(value):
-> 4509 com.require_length_match(value, self.index)
4510 return sanitize_array(value, self.index, copy=True, allow_2d=True)
4511
~/app/ENTER/envs/stream1/lib/python3.7/site-packages/pandas/core/common.py in require_length_match(data, index)
530 if len(data) != len(index):
531 raise ValueError(
--> 532 "Length of values "
533 f"({len(data)}) "
534 "does not match length of index "
ValueError: Length of values (1) does not match length of index (156605)
my package version is
stream=1.1
pandas=1.3.5
numpy=1.21.5
bellenger-l commented
Hello,
I managed to look a lit bit further this issue by testing my script with the STREAM dataset. It working pretty fine and I understood that it is because of the input format. What I don't understand why but when you use the 3 files format (or format = 'mtx') it end with the issue that @tianchen2019 and I mentioned.
In fact the format of adata.X change.
With my data in 3 files format I have :
print(adata.X)
[240 rows x 2 columns]
(0, 1) 1.0
(0, 5) 1.0
(0, 8) 3.0
(0, 11) 1.0
(0, 22) 1.0
...
When with the tsv file of Nestorowa dataset :
print(adata.X)
[4768 rows x 0 columns]
[[0. 4.8916044 1.4261482 ... 1.4261482 0. 7.869409 ]
[0. 6.8777246 0. ... 0. 0. 0. ]
[0. 0. 6.9133844 ... 2.0516593 0. 1.3634025 ]
...
[0. 1.2770978 0. ... 0. 0. 0.77547604]
[0. 1.878352 0. ... 0. 0. 0. ]
[0. 3.3741634 0. ... 0. 0. 0. ]]
So I tried this dataset with 3 files format (that end up with the same error as described in this issue) :
print(adata.X)
[4768 rows x 2 columns]
(0, 1) 4.8916044
(0, 2) 1.4261482
(0, 5) 2.599758
(0, 6) 2.9540346
(0, 7) 6.3573685
(0, 8) 2.12914
(0, 9) 1.4261482
...
And finally I tried to change my dataset into a tsv format where I have :
print(adata.X)
[240 rows x 0 columns]
[[0. 1. 0. ... 1. 0. 0.]
[0. 0. 0. ... 0. 1. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
So it's the same shape as the Nestorowa dataset in tsv but when I follow my script I end up with no variable feature where STREAM 1.0 succeed to detect 230 variable features...
Still stuck at this moment and I don't know why... For the time being I continue to use the version 1.0
Best,
Lea
bellenger-l commented
Hello @huidongchen,
I didn't succeed to upgrade and use STREAM v1.1. Would it be possible to provide the command line to install STREAM 1.1 with the dependency versions ? Like what you gave for STREAM 1.0 :
conda create -n env_stream python=3.7 stream=1.0 jupyter
Including the version for pandas, numpy, matplotlib ?
This would be greatly appreciated
Best regards,
Lea
huidongchen commented
Hi Lea, thanks for the feedback. Unfortunately It's not really trivial to figure out all the dependencies that are compatible with the old STREAM v1.0.. I would strongly recommend using the latest version.
As I suggested before, instead of upgrading stream from v1.0 to v1.1, can you try to install stream v1.1 from scratch with the code below:
conda create -n env_stream python=3.7 stream=1.1 jupyter
conda activate env_stream
The issue @tianchen2019 described is related to scATAC-seq analysis. I need to look further into that. but v1.1 should work fine with scRNA-seq data.
bellenger-l commented
Hello @huidongchen ! Sorry for the delay I had multiple problems with conda so it took time for me to create another environnement.
Unfortunately I had the same issue. Here is the conda list :
# Name Version Build Channel
_libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
_r-mutex 1.0.0 anacondar_1 r
_sysroot_linux-64_curr_repodata_hack 3 haa98f57_10
anndata 0.6.22.post1 py_0 bioconda
anyio 3.5.0 py37h06a4308_0
argon2-cffi 21.3.0 pyhd3eb1b0_0
argon2-cffi-bindings 21.2.0 py37h7f8727e_0
arpack 3.7.0 hc6cf775_2 conda-forge
attrs 22.1.0 py37h06a4308_0
backcall 0.2.0 pyhd3eb1b0_0
beautifulsoup4 4.11.1 py37h06a4308_0
binutils_impl_linux-64 2.35.1 h27ae35d_9
binutils_linux-64 2.35.1 h454624a_30
blas 1.0 mkl
bleach 4.1.0 pyhd3eb1b0_0
blosc 1.21.3 h6a678d5_0
bottleneck 1.3.5 py37h7deecbd_0
brotli 1.0.9 h5eee18b_7
brotli-bin 1.0.9 h5eee18b_7
bwidget 1.9.11 1
bzip2 1.0.8 h7b6447c_0
ca-certificates 2023.01.10 h06a4308_0
cairo 1.16.0 hb05425b_3
certifi 2022.12.7 py37h06a4308_0
cffi 1.15.1 py37h5eee18b_3
colorama 0.4.6 py37h06a4308_0
curl 7.69.1 hbc83047_0
cycler 0.11.0 pyhd3eb1b0_0
dbus 1.13.18 hb2f20db_0
debugpy 1.5.1 py37h295c915_0
decorator 5.1.1 pyhd3eb1b0_0
defusedxml 0.7.1 pyhd3eb1b0_0
dunamai 1.16.0 pyhd8ed1ab_0 conda-forge
entrypoints 0.4 py37h06a4308_0
expat 2.4.9 h6a678d5_0
flit-core 3.6.0 pyhd3eb1b0_0
fontconfig 2.14.1 h52c9d5c_1
fonttools 4.25.0 pyhd3eb1b0_0
freetype 2.12.1 h4a9f257_0
fribidi 1.0.10 h7b6447c_0
gcc_impl_linux-64 7.5.0 h7105cf2_17
gcc_linux-64 7.5.0 h8f34230_30
gdbm 1.18 hd4cb3f1_4
geos 3.8.0 he6710b0_0
get_version 3.5.4 pyhd8ed1ab_0 conda-forge
gettext 0.21.0 hf68c758_0
gfortran_impl_linux-64 7.5.0 ha8c8e06_17
gfortran_linux-64 7.5.0 h96bb648_30
giflib 5.2.1 h5eee18b_1
git 2.32.0 pl5262hc120c5b_1
glib 2.69.1 he621ea3_2
glpk 4.65 h276157c_3
gmp 6.2.1 h295c915_3
graphite2 1.3.14 h295c915_1
gsl 2.4 h14c3975_4
gst-plugins-base 1.14.1 h6a678d5_1
gstreamer 1.14.1 h5eee18b_1
gunicorn 20.1.0 py37h06a4308_0
gxx_impl_linux-64 7.5.0 h0a5bf11_17
gxx_linux-64 7.5.0 hffc177d_30
h5py 3.7.0 py37h737f45e_0
harfbuzz 4.3.0 hd55b92a_0
hdf5 1.10.6 hb1b8bf9_0
icu 58.2 he6710b0_3
idna 3.4 py37h06a4308_0
igraph 0.9.8 hf5496dd_0 conda-forge
importlib-metadata 4.11.3 py37h06a4308_0
importlib_metadata 4.11.3 hd3eb1b0_0
importlib_resources 5.2.0 pyhd3eb1b0_1
intel-openmp 2021.4.0 h06a4308_3561
ipykernel 6.15.2 py37h06a4308_0
ipython 7.31.1 py37h06a4308_1
ipython_genutils 0.2.0 pyhd3eb1b0_1
jedi 0.18.1 py37h06a4308_1
jinja2 3.1.2 py37h06a4308_0
joblib 1.1.1 py37h06a4308_0
jpeg 9e h7f8727e_0
jsonschema 4.17.3 py37h06a4308_0
jupyter_client 7.4.9 py37h06a4308_0
jupyter_core 4.11.2 py37h06a4308_0
jupyter_server 1.23.4 py37h06a4308_0
jupyterlab_pygments 0.1.2 py_0
kernel-headers_linux-64 3.10.0 h57e8cba_10
kiwisolver 1.4.4 py37h6a678d5_0
krb5 1.17.1 h173b8e3_0
lcms2 2.12 h3be6417_0
ld_impl_linux-64 2.35.1 h7274673_9
legacy-api-wrap 1.2 py_0 conda-forge
lerc 3.0 h295c915_0
libblas 3.9.0 12_linux64_mkl conda-forge
libbrotlicommon 1.0.9 h5eee18b_7
libbrotlidec 1.0.9 h5eee18b_7
libbrotlienc 1.0.9 h5eee18b_7
libcurl 7.69.1 h20c2e04_0
libdeflate 1.8 h7f8727e_5
libedit 3.1.20221030 h5eee18b_0
libffi 3.4.2 h6a678d5_6
libgcc-devel_linux-64 7.5.0 hbbeae57_17
libgcc-ng 11.2.0 h1234567_1
libgfortran-ng 7.5.0 ha8ba4b0_17
libgfortran4 7.5.0 ha8ba4b0_17
libgomp 11.2.0 h1234567_1
libiconv 1.16 h7f8727e_2
liblapack 3.9.0 12_linux64_mkl conda-forge
libllvm11 11.1.0 h9e868ea_6
libpng 1.6.37 hbc83047_0
libsodium 1.0.18 h7b6447c_0
libssh2 1.10.0 h8f2d780_0
libstdcxx-devel_linux-64 7.5.0 hf0c5c8d_17
libstdcxx-ng 11.2.0 h1234567_1
libtiff 4.5.0 h6a678d5_1
libuuid 1.41.5 h5eee18b_0
libwebp 1.2.4 h11a3e52_0
libwebp-base 1.2.4 h5eee18b_0
libxcb 1.15 h7f8727e_0
libxml2 2.9.14 h74e7548_0
libxslt 1.1.35 h4e12654_0
llvmlite 0.39.1 py37he621ea3_0
louvain 0.7.1 py37hd23a5d3_2 conda-forge
lxml 4.9.1 py37h1edc446_0
lz4-c 1.9.4 h6a678d5_0
lzo 2.10 h7b6447c_2
make 4.2.1 h1bed415_1
markupsafe 2.1.1 py37h7f8727e_0
matplotlib 3.5.3 py37h06a4308_0
matplotlib-base 3.5.3 py37hf590b9c_0
matplotlib-inline 0.1.6 py37h06a4308_0
metis 5.1.0 hf484d3e_4
mistune 0.8.4 py37h14c3975_1001
mkl 2021.4.0 h06a4308_640
mkl-service 2.4.0 py37h7f8727e_0
mkl_fft 1.3.1 py37hd3c417c_0
mkl_random 1.2.2 py37h51133e4_0
mock 4.0.3 pyhd3eb1b0_0
mpfr 4.0.2 hb69a4c5_1
munkres 1.1.4 py_0
natsort 7.1.1 pyhd3eb1b0_0
nbclassic 0.4.8 py37h06a4308_0
nbclient 0.5.13 py37h06a4308_0
nbconvert 6.5.4 py37h06a4308_0
nbformat 5.7.0 py37h06a4308_0
ncurses 6.4 h6a678d5_0
nest-asyncio 1.5.6 py37h06a4308_0
networkx 2.1 py37_0
notebook 6.5.2 py37h06a4308_0
notebook-shim 0.2.2 py37h06a4308_0
numba 0.56.4 py37h417a72b_0
numexpr 2.8.4 py37he184ba9_0
numpy 1.21.5 py37h6c91a56_3
numpy-base 1.21.5 py37ha15fc14_3
openssl 1.1.1t h7f8727e_0
packaging 22.0 py37h06a4308_0
pandas 1.3.5 py37h8c16a72_0
pandocfilters 1.5.0 pyhd3eb1b0_0
pango 1.50.7 h05da053_0
parso 0.8.3 pyhd3eb1b0_0
patsy 0.5.3 py37h06a4308_0
pcre 8.45 h295c915_0
pcre2 10.35 h14c3975_1
perl 5.34.0 h5eee18b_2
pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
pillow 9.3.0 py37h6a678d5_2
pip 22.3.1 py37h06a4308_0
pixman 0.40.0 h7f8727e_1
pkgutil-resolve-name 1.3.10 py37h06a4308_0
plotly 5.9.0 py37h06a4308_0
prometheus_client 0.14.1 py37h06a4308_0
prompt-toolkit 3.0.36 py37h06a4308_0
psutil 5.9.0 py37h5eee18b_0
ptyprocess 0.7.0 pyhd3eb1b0_2
pycparser 2.21 pyhd3eb1b0_0
pygments 2.11.2 pyhd3eb1b0_0
pynndescent 0.5.4 pyhd3eb1b0_0
pyparsing 3.0.9 py37h06a4308_0
pyqt 5.9.2 py37h05f1152_2
pyrsistent 0.18.0 py37heee7806_0
pytables 3.7.0 py37hf19a122_1
python 3.7.16 h7a1cb2a_0
python-dateutil 2.8.2 pyhd3eb1b0_0
python-fastjsonschema 2.16.2 py37h06a4308_0
python-igraph 0.9.10 py37h48d2a39_0 conda-forge
python-slugify 5.0.2 pyhd3eb1b0_0
python_abi 3.7 2_cp37m conda-forge
pytz 2022.7 py37h06a4308_0
pyzmq 23.2.0 py37h6a678d5_0
qt 5.9.7 h5867ecd_1
r-askpass 1.0 r36h14c3975_0 r
r-assertthat 0.2.1 r36h6115d3f_0 r
r-backports 1.1.4 r36h96ca727_0 r
r-base 3.6.1 haffb61f_2 r
r-base64enc 0.1_3 r36h96ca727_4 r
r-bh 1.69.0_1 r36h6115d3f_0 r
r-bit 1.1_14 r36h96ca727_0 r
r-bit64 0.9_7 r36h96ca727_0 r
r-blob 1.1.1 r36h6115d3f_0 r
r-boot 1.3_20 r36h6115d3f_0 r
r-broom 0.5.2 r36h6115d3f_0 r
r-callr 3.2.0 r36h6115d3f_0 r
r-caret 6.0_83 r36h96ca727_0 r
r-cellranger 1.1.0 r36h6115d3f_0 r
r-class 7.3_15 r36h96ca727_0 r
r-cli 1.1.0 r36h6115d3f_0 r
r-clipr 0.6.0 r36h6115d3f_0 r
r-clisymbols 1.2.0 r36h6115d3f_0 r
r-cluster 2.0.8 r36ha65eedd_0 r
r-codetools 0.2_16 r36h6115d3f_0 r
r-colorspace 1.4_1 r36h96ca727_0 r
r-cowplot 1.1.1 r36hc72bb7e_0 conda-forge
r-crayon 1.3.4 r36h6115d3f_0 r
r-curl 3.3 r36h96ca727_0 r
r-data.table 1.12.2 r36h96ca727_0 r
r-dbi 1.0.0 r36h6115d3f_0 r
r-dbplyr 1.4.0 r36h6115d3f_0 r
r-desc 1.2.0 r36h6115d3f_0 r
r-devtools 2.0.2 r36h6115d3f_0 r
r-dichromat 2.0_0 r36h6115d3f_4 r
r-digest 0.6.18 r36h96ca727_0 r
r-distutils 1.0 r36h516909a_1003 conda-forge
r-dplyr 0.8.0.1 r36h29659fb_0 r
r-ellipsis 0.1.0 r36h96ca727_0 r
r-elpigraph.r 1.0.0 r36h516909a_1003 conda-forge
r-essentials 3.6.0 r36_0 r
r-evaluate 0.13 r36h6115d3f_0 r
r-fansi 0.4.0 r36h96ca727_0 r
r-farver 1.1.0 r36h29659fb_0 r
r-forcats 0.4.0 r36h6115d3f_0 r
r-foreach 1.4.4 r36h6115d3f_0 r
r-foreign 0.8_71 r36h96ca727_0 r
r-formatr 1.6 r36h6115d3f_0 r
r-fs 1.2.7 r36h29659fb_0 r
r-generics 0.0.2 r36h6115d3f_0 r
r-gensa 1.1.7 r36h29659fb_0 r
r-ggforce 0.3.3 r36h03ef668_0 conda-forge
r-ggplot2 3.1.1 r36h6115d3f_0 r
r-ggpubr 0.2.5 r36h6115d3f_1 conda-forge
r-ggrepel 0.9.1 r36h03ef668_0 conda-forge
r-ggsci 2.9 r36h6115d3f_1003 conda-forge
r-ggsignif 0.6.1 r36hc72bb7e_0 conda-forge
r-gh 1.0.1 r36h6115d3f_0 r
r-git2r 0.25.2 r36h96ca727_0 r
r-glmnet 2.0_16 r36ha65eedd_0 r
r-glue 1.3.1 r36h96ca727_0 r
r-gower 0.2.0 r36h96ca727_0 r
r-gridextra 2.3 r36h6115d3f_0 r
r-gtable 0.3.0 r36h6115d3f_0 r
r-haven 2.1.0 r36h29659fb_0 r
r-hexbin 1.27.2 r36ha65eedd_0 r
r-highr 0.8 r36h6115d3f_0 r
r-hms 0.4.2 r36h6115d3f_0 r
r-htmltools 0.3.6 r36h29659fb_0 r
r-htmlwidgets 1.3 r36h6115d3f_0 r
r-httpuv 1.5.1 r36h29659fb_0 r
r-httr 1.4.0 r36h6115d3f_0 r
r-igraph 1.2.4.1 r36h80f5a37_0 r
r-infotheo 1.2.0 r36h29659fb_0 r
r-ini 0.3.1 r36h6115d3f_0 r
r-ipred 0.9_8 r36h96ca727_0 r
r-irdisplay 0.7.0 r36h6115d3f_0 r
r-irkernel 0.8.15 r36_0 r
r-irlba 2.3.3 r36h96ca727_0 r
r-iterators 1.0.10 r36h6115d3f_0 r
r-jsonlite 1.6 r36h96ca727_0 r
r-kernsmooth 2.23_15 r36ha65eedd_4 r
r-knitr 1.22 r36h6115d3f_0 r
r-labeling 0.3 r36h6115d3f_4 r
r-later 0.8.0 r36h29659fb_0 r
r-lattice 0.20_38 r36h96ca727_0 r
r-lava 1.6.5 r36h6115d3f_0 r
r-lazyeval 0.2.2 r36h96ca727_0 r
r-lubridate 1.7.4 r36h29659fb_0 r
r-magrittr 1.5 r36h6115d3f_4 r
r-maps 3.3.0 r36h96ca727_0 r
r-markdown 0.9 r36h96ca727_0 r
r-mass 7.3_51.3 r36h96ca727_0 r
r-matrix 1.2_17 r36h96ca727_0 r
r-memoise 1.1.0 r36h6115d3f_0 r
r-mgcv 1.8_28 r36h96ca727_0 r
r-mime 0.6 r36h96ca727_0 r
r-modelmetrics 1.2.2 r36h29659fb_0 r
r-modelr 0.1.4 r36h6115d3f_0 r
r-munsell 0.5.0 r36h6115d3f_0 r
r-nlme 3.1_139 r36ha65eedd_0 r
r-nnet 7.3_12 r36h96ca727_0 r
r-numderiv 2016.8_1 r36h6115d3f_0 r
r-openssl 1.3 r36h96ca727_0 r
r-pbdzmq 0.3_3 r36h5455479_1 r
r-pillar 1.3.1 r36h6115d3f_0 r
r-pkgbuild 1.0.3 r36h6115d3f_0 r
r-pkgconfig 2.0.2 r36h6115d3f_0 r
r-pkgload 1.0.2 r36h29659fb_0 r
r-plogr 0.2.0 r36h6115d3f_0 r
r-plyr 1.8.4 r36h29659fb_0 r
r-polyclip 1.10_0 r36h29659fb_0 r
r-polynom 1.4_0 r36h6115d3f_0 r
r-prettyunits 1.0.2 r36h6115d3f_0 r
r-processx 3.3.0 r36h96ca727_0 r
r-prodlim 2018.04.18 r36h29659fb_0 r
r-progress 1.2.0 r36h6115d3f_0 r
r-promises 1.0.1 r36h29659fb_0 r
r-ps 1.3.0 r36h96ca727_0 r
r-purrr 0.3.2 r36h96ca727_0 r
r-quantmod 0.4_14 r36h6115d3f_0 r
r-r6 2.4.0 r36h6115d3f_0 r
r-randomforest 4.6_14 r36ha65eedd_0 r
r-rbokeh 0.6.3 r36_0 r
r-rcmdcheck 1.3.2 r36h6115d3f_0 r
r-rcolorbrewer 1.1_2 r36h6115d3f_0 r
r-rcpp 1.0.1 r36h29659fb_0 r
r-rcpparmadillo 0.9.300.2.0 r36h29659fb_0 r
r-rcppeigen 0.3.3.5.0 r36h29659fb_0 r
r-rcpproll 0.3.0 r36h29659fb_0 r
r-readr 1.3.1 r36h29659fb_0 r
r-readxl 1.3.1 r36h29659fb_0 r
r-recipes 0.1.5 r36h6115d3f_0 r
r-recommended 3.6.0 r36_0 r
r-rematch 1.0.1 r36h6115d3f_0 r
r-remotes 2.0.4 r36h6115d3f_0 r
r-repr 0.19.2 r36h6115d3f_0 r
r-reprex 0.2.1 r36h6115d3f_0 r
r-reshape2 1.4.3 r36h29659fb_0 r
r-rlang 0.3.4 r36h96ca727_0 r
r-rmarkdown 1.12 r36h6115d3f_0 r
r-rpart 4.1_15 r36h96ca727_0 r
r-rprojroot 1.3_2 r36h6115d3f_0 r
r-rsqlite 2.1.1 r36h29659fb_0 r
r-rstudioapi 0.10 r36h6115d3f_0 r
r-rvest 0.3.3 r36h6115d3f_0 r
r-scales 1.0.0 r36h29659fb_0 r
r-segmented 1.0_0 r36h6115d3f_0 r
r-selectr 0.4_1 r36h6115d3f_0 r
r-sessioninfo 1.1.1 r36h6115d3f_0 r
r-shiny 1.3.2 r36h6115d3f_0 r
r-sourcetools 0.1.7 r36h29659fb_0 r
r-spatial 7.3_11 r36h96ca727_4 r
r-squarem 2017.10_1 r36h6115d3f_0 r
r-stringi 1.4.3 r36h29659fb_0 r
r-stringr 1.4.0 r36h6115d3f_0 r
r-survival 2.44_1.1 r36h96ca727_0 r
r-sys 3.2 r36h96ca727_0 r
r-tibble 2.1.1 r36h96ca727_0 r
r-tictoc 1.0 r36h6115d3f_0 r
r-tidyr 0.8.3 r36h29659fb_0 r
r-tidyselect 0.2.5 r36h29659fb_0 r
r-tidyverse 1.2.1 r36h6115d3f_0 r
r-timedate 3043.102 r36h6115d3f_0 r
r-tinytex 0.12 r36h6115d3f_0 r
r-ttr 0.23_4 r36ha65eedd_0 r
r-tweenr 1.0.2 r36h03ef668_0 conda-forge
r-usethis 1.5.0 r36h6115d3f_0 r
r-utf8 1.1.4 r36h96ca727_0 r
r-uuid 0.1_2 r36h96ca727_4 r
r-vgam 1.1_1 r36ha65eedd_0 r
r-viridislite 0.3.0 r36h6115d3f_0 r
r-whisker 0.3_2 r36h6115d3f_4 r
r-withr 2.1.2 r36h6115d3f_0 r
r-xfun 0.6 r36h6115d3f_0 r
r-xml 3.98_1.19 r36h96ca727_0 r
r-xml2 1.2.0 r36h29659fb_0 r
r-xopen 1.0.0 r36h6115d3f_0 r
r-xtable 1.8_4 r36h6115d3f_0 r
r-xts 0.11_2 r36h96ca727_0 r
r-yaml 2.2.0 r36h96ca727_0 r
r-zoo 1.8_6 r36h96ca727_0 r
readline 8.2 h5eee18b_0
rpy2 2.9.4 py37r36hd767a1f_3 conda-forge
scanpy 1.4.4.post1 py_0 bioconda
scikit-learn 1.0.2 py37h51133e4_1
scipy 1.7.3 py37hc147768_0
seaborn 0.12.2 py37h06a4308_0
send2trash 1.8.0 pyhd3eb1b0_1
setuptools 65.6.3 py37h06a4308_0
setuptools-scm 7.0.4 py37h06a4308_0
setuptools_scm 7.0.4 hd3eb1b0_0
shapely 1.8.4 py37h81ba7c5_0
simplegeneric 0.8.1 py37_2
sinfo 0.3.1 py_0 conda-forge
sip 4.19.8 py37hf484d3e_0
six 1.16.0 pyhd3eb1b0_1
sniffio 1.2.0 py37h06a4308_1
soupsieve 2.3.2.post1 py37h06a4308_0
sqlite 3.40.1 h5082296_0
statsmodels 0.13.5 py37h7deecbd_0
stdlib-list 0.8.0 py37h06a4308_0
stream 1.1 pyhdfd78af_0 bioconda
suitesparse 5.10.1 h446ee2e_2
sysroot_linux-64 2.17 h57e8cba_10
tbb 2021.6.0 hdb19cb5_1
tenacity 8.0.1 py37h06a4308_1
terminado 0.17.1 py37h06a4308_0
text-unidecode 1.3 pyhd3eb1b0_0
texttable 1.6.4 pyhd3eb1b0_0
threadpoolctl 2.2.0 pyh0d69192_0
tinycss2 1.2.1 py37h06a4308_0
tk 8.6.12 h1ccaba5_0
tktable 2.10 h14c3975_0
tomli 2.0.1 py37h06a4308_0
tornado 6.2 py37h5eee18b_0
tqdm 4.64.1 py37h06a4308_0
traitlets 5.7.1 py37h06a4308_0
typing-extensions 4.4.0 py37h06a4308_0
typing_extensions 4.4.0 py37h06a4308_0
tzlocal 2.1 py37h06a4308_1
umap-learn 0.5.3 py37h89c1867_0 conda-forge
unidecode 1.2.0 pyhd3eb1b0_0
unzip 6.0 h611a1e1_0
wcwidth 0.2.5 pyhd3eb1b0_0
webencodings 0.5.1 py37_1
websocket-client 0.58.0 py37h06a4308_4
wheel 0.38.4 py37h06a4308_0
xz 5.2.10 h5eee18b_1
zeromq 4.3.4 h2531618_0
zip 3.0 h7f8727e_1
zipp 3.11.0 py37h06a4308_0
zlib 1.2.13 h5eee18b_0
zstd 1.5.2 ha4553b6_0
And here is the conda info :
active environment : stream_mamba
active env location : /home/lea/miniconda3/envs/stream_mamba
shell level : 1
user config file : /home/lea/.condarc
populated config files : /home/lea/.condarc
conda version : 23.1.0
conda-build version : 3.23.3
python version : 3.10.9.final.0
virtual packages : __archspec=1=x86_64
__glibc=2.31=0
__linux=5.4.0=0
__unix=0=0
base environment : /home/lea/miniconda3 (writable)
conda av data dir : /home/lea/miniconda3/etc/conda
conda av metadata url : None
channel URLs : https://conda.anaconda.org/r/linux-64
https://conda.anaconda.org/r/noarch
https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
https://conda.anaconda.org/bioconda/linux-64
https://conda.anaconda.org/bioconda/noarch
https://conda.anaconda.org/conda-forge/linux-64
https://conda.anaconda.org/conda-forge/noarch
package cache : /home/lea/miniconda3/pkgs
/home/lea/.conda/pkgs
envs directories : /home/lea/miniconda3/envs
/home/lea/.conda/envs
platform : linux-64
user-agent : conda/23.1.0 requests/2.28.1 CPython/3.10.9 Linux/5.4.0-139-generic ubuntu/20.04.6 glibc/2.31
UID:GID : 1005:1005
netrc file : None
offline mode : False
As I described in my previous messages, all the environments in stream 1.1 don't work when we import our data in the bundle format (mtx). The structure of the expression matrix change and it seems cause the following error while running select_variable_genes :
---------------------------------------------------------------------------
AxisError Traceback (most recent call last)
/tmp/ipykernel_1515188/1347598060.py in <module>
1 ### Highly variable genes
----> 2 stream.select_variable_genes(Int_5CT_adata,loess_frac=0.01,percentile=95)
3 Int_5CT_adata.uns['var_genes_selected'] = Int_5CT_adata.uns['var_genes'] #keep the information in another slot if needed
/home/lea/miniconda3/envs/stream_mamba/lib/python3.7/site-packages/stream/core.py in select_variable_genes(adata, loess_frac, percentile, n_genes, n_jobs, save_fig, fig_name, fig_path, fig_size, pad, w_pad, h_pad)
900 fig_size = mpl.rcParams['figure.figsize'] if fig_size is None else fig_size
901 mean_genes = np.mean(adata.X,axis=0)
--> 902 std_genes = np.std(adata.X,ddof=1,axis=0)
903 loess_fitted = lowess(std_genes,mean_genes,return_sorted=False,frac=loess_frac)
904 residuals = std_genes - loess_fitted
<__array_function__ internals> in std(*args, **kwargs)
/home/lea/miniconda3/envs/stream_mamba/lib/python3.7/site-packages/numpy/core/fromnumeric.py in std(a, axis, dtype, out, ddof, keepdims, where)
3580
3581 return _methods._std(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
-> 3582 **kwargs)
3583
3584
/home/lea/miniconda3/envs/stream_mamba/lib/python3.7/site-packages/numpy/core/_methods.py in _std(a, axis, dtype, out, ddof, keepdims, where)
261 where=True):
262 ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
--> 263 keepdims=keepdims, where=where)
264
265 if isinstance(ret, mu.ndarray):
/home/lea/miniconda3/envs/stream_mamba/lib/python3.7/site-packages/numpy/core/_methods.py in _var(a, axis, dtype, out, ddof, keepdims, where)
197 arr = asanyarray(a)
198
--> 199 rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
200 # Make this warning show up on top.
201 if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
/home/lea/miniconda3/envs/stream_mamba/lib/python3.7/site-packages/numpy/core/_methods.py in _count_reduce_items(arr, axis, keepdims, where)
74 items = nt.intp(1)
75 for ax in axis:
---> 76 items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
77 else:
78 # TODO: Optimize case when `where` is broadcast along a non-reduction
AxisError: axis 0 is out of bounds for array of dimension 0
The structure of adata.X in stream 1.1 is :
<24570x2000 sparse matrix of type '<class 'numpy.float32'>'
with 6980887 stored elements in Compressed Sparse Row format>
But in stream 1.0 (where everything is fine) the structure is :
array([[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ],
[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ],
[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ],
...,
[-0.39902183, -0.03658423, 0. , ..., 0. ,
0. , 0. ],
[ 0. , -0.04271474, 0. , ..., 0. ,
0. , 0. ],
[ 0.00093189, -0.22098285, -0.1300799 , ..., 0. ,
0. , 0. ]], dtype=float32)
Here is the conda list for the environment 1.0 :
# Name Version Build Channel
_libgcc_mutex 0.1 main
_r-mutex 1.0.0 anacondar_1
alabaster 0.7.12 pyhd3eb1b0_0
anndata 0.7.8 py37h89c1867_1 conda-forge
argon2-cffi 20.1.0 py37h27cfd23_1
async_generator 1.10 pyhd3eb1b0_0
attrs 20.3.0 pyhd3eb1b0_0
babel 2.9.1 pyhd3eb1b0_0
backcall 0.2.0 pyhd3eb1b0_0
binutils_impl_linux-64 2.33.1 he6710b0_7
binutils_linux-64 2.33.1 h9595d00_15
blas 1.0 mkl
bleach 3.3.0 pyhd3eb1b0_0
blosc 1.21.0 h8c45485_0
brotlipy 0.7.0 py37h27cfd23_1003
bwidget 1.9.11 1
bzip2 1.0.8 h7b6447c_0
ca-certificates 2021.10.26 h06a4308_2
cairo 1.14.12 h8948797_3
certifi 2021.10.8 py37h06a4308_2
cffi 1.14.5 py37h261ae71_0
charset-normalizer 2.0.4 pyhd3eb1b0_0
colorama 0.4.4 pyhd3eb1b0_0
cryptography 36.0.0 py37h9ce1e76_0
curl 7.69.1 hbc83047_0
cycler 0.10.0 py37_0
dbus 1.13.18 hb2f20db_0
decorator 4.4.2 pyhd3eb1b0_0
defusedxml 0.6.0 pyhd3eb1b0_0
docutils 0.17.1 py37h06a4308_1
dunamai 1.5.5 pyhd8ed1ab_0 conda-forge
entrypoints 0.3 py37_0
expat 2.2.10 he6710b0_2
fontconfig 2.13.1 h6c09931_0
freetype 2.10.4 h5ab3b9f_0
fribidi 1.0.10 h7b6447c_0
gcc_impl_linux-64 7.3.0 habb00fd_1
gcc_linux-64 7.3.0 h553295d_15
geos 3.8.0 he6710b0_0
get_version 3.5 pyhd8ed1ab_0 conda-forge
gfortran_impl_linux-64 7.3.0 hdf63c60_1
gfortran_linux-64 7.3.0 h553295d_15
glib 2.67.3 h36276a3_1
graphite2 1.3.14 h23475e2_0
gsl 2.4 h14c3975_4
gst-plugins-base 1.14.0 h8213a91_2
gstreamer 1.14.0 h28cd5cc_2
gunicorn 20.0.4 py37_0
gxx_impl_linux-64 7.3.0 hdf63c60_1
gxx_linux-64 7.3.0 h553295d_15
h5py 2.10.0 py37hd6299e0_1
harfbuzz 2.4.0 hca77d97_1
hdf5 1.10.6 hb1b8bf9_0
icu 58.2 he6710b0_3
idna 3.3 pyhd3eb1b0_0
imagesize 1.3.0 pyhd3eb1b0_0
importlib-metadata 2.0.0 py_1
importlib_metadata 2.0.0 1
intel-openmp 2020.2 254
ipykernel 5.3.4 py37h5ca1d4c_0
ipython 7.20.0 py37hb070fc8_1
ipython_genutils 0.2.0 pyhd3eb1b0_1
ipywidgets 7.6.3 pyhd3eb1b0_1
jedi 0.17.2 py37h06a4308_1
jinja2 2.11.3 pyhd3eb1b0_0
joblib 1.0.0 pyhd3eb1b0_0
jpeg 9b h024ee3a_2
jsonschema 3.2.0 py_2
jupyter 1.0.0 py37_7
jupyter_client 6.1.7 py_0
jupyter_console 6.2.0 py_0
jupyter_core 4.7.1 py37h06a4308_0
jupyterlab_pygments 0.1.2 py_0
jupyterlab_widgets 1.0.0 pyhd3eb1b0_1
kiwisolver 1.3.1 py37h2531618_0
krb5 1.17.1 h173b8e3_0
ld_impl_linux-64 2.33.1 h53a641e_7
legacy-api-wrap 1.2 py_0 conda-forge
libcurl 7.69.1 h20c2e04_0
libedit 3.1.20191231 h14c3975_1
libffi 3.3 he6710b0_2
libgcc-ng 9.1.0 hdf63c60_0
libgfortran-ng 7.3.0 hdf63c60_0
libllvm10 10.0.1 hbcb73fb_5
libpng 1.6.37 hbc83047_0
libsodium 1.0.18 h7b6447c_0
libssh2 1.9.0 h1ba5d50_1
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.1.0 h2733197_1
libuuid 1.0.3 h1bed415_2
libxcb 1.14 h7b6447c_0
libxml2 2.9.10 hb55368b_3
llvmlite 0.34.0 py37h269e1b5_4
lz4-c 1.9.3 h2531618_0
lzo 2.10 h7b6447c_2
make 4.2.1 h1bed415_1
markupsafe 1.1.1 py37h14c3975_1
matplotlib 3.2.2 0
matplotlib-base 3.2.2 py37hef1b27d_0
mistune 0.8.4 py37h14c3975_1001
mkl 2020.2 256
mkl-service 2.3.0 py37he8ac12f_0
mkl_fft 1.2.0 py37h23d657b_0
mkl_random 1.1.1 py37h0573a6f_0
mock 4.0.3 pyhd3eb1b0_0
natsort 7.1.1 pyhd3eb1b0_0
nbclient 0.5.1 py_0
nbconvert 6.0.7 py37_0
nbformat 5.1.2 pyhd3eb1b0_1
ncurses 6.2 he6710b0_1
nest-asyncio 1.4.3 pyhd3eb1b0_0
networkx 2.1 py37_0
notebook 6.2.0 py37h06a4308_0
numba 0.51.2 py37h04863e7_1
numexpr 2.7.3 py37hb2eb853_0
numpy 1.19.2 py37h54aff64_0
numpy-base 1.19.2 py37hfa32c7d_0
openssl 1.1.1m h7f8727e_0
packaging 20.9 pyhd3eb1b0_0
pandas 1.0.5 py37h0573a6f_0
pandoc 2.11 hb0f4dca_0
pandocfilters 1.4.3 py37h06a4308_1
pango 1.45.3 hd140c19_0
parso 0.7.0 py_0
patsy 0.5.1 py37_0
pcre 8.44 he6710b0_0
pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
pip 20.3.3 py37h06a4308_0
pixman 0.40.0 h7b6447c_0
plotly 4.14.3 pyhd3eb1b0_0
prometheus_client 0.9.0 pyhd3eb1b0_0
prompt-toolkit 3.0.8 py_0
prompt_toolkit 3.0.8 0
ptyprocess 0.7.0 pyhd3eb1b0_2
pycparser 2.20 py_2
pygments 2.7.4 pyhd3eb1b0_0
pynndescent 0.5.2 pyh44b312d_0 conda-forge
pyopenssl 21.0.0 pyhd3eb1b0_1
pyparsing 2.4.7 pyhd3eb1b0_0
pyqt 5.9.2 py37h05f1152_2
pyrsistent 0.17.3 py37h7b6447c_0
pysocks 1.7.1 py37_1
pytables 3.6.1 py37he17a9a8_3 conda-forge
python 3.7.9 h7579374_0
python-dateutil 2.8.1 pyhd3eb1b0_0
python-slugify 4.0.1 py_0
python_abi 3.7 1_cp37m conda-forge
pytz 2021.1 pyhd3eb1b0_0
pyzmq 20.0.0 py37h2531618_1
qt 5.9.7 h5867ecd_1
qtconsole 5.0.2 pyhd3eb1b0_0
qtpy 1.9.0 py_0
r-askpass 1.0 r36h14c3975_0
r-assertthat 0.2.1 r36h6115d3f_0
r-backports 1.1.4 r36h96ca727_0
r-base 3.6.1 haffb61f_2
r-base64enc 0.1_3 r36h96ca727_4
r-bh 1.69.0_1 r36h6115d3f_0
r-bit 1.1_14 r36h96ca727_0
r-bit64 0.9_7 r36h96ca727_0
r-blob 1.1.1 r36h6115d3f_0
r-boot 1.3_20 r36h6115d3f_0
r-broom 0.5.2 r36h6115d3f_0
r-callr 3.2.0 r36h6115d3f_0
r-caret 6.0_83 r36h96ca727_0
r-cellranger 1.1.0 r36h6115d3f_0
r-class 7.3_15 r36h96ca727_0
r-cli 1.1.0 r36h6115d3f_0
r-clipr 0.6.0 r36h6115d3f_0
r-clisymbols 1.2.0 r36h6115d3f_0
r-cluster 2.0.8 r36ha65eedd_0
r-codetools 0.2_16 r36h6115d3f_0
r-colorspace 1.4_1 r36h96ca727_0
r-cowplot 1.1.1 r36hc72bb7e_0 conda-forge
r-crayon 1.3.4 r36h6115d3f_0
r-curl 3.3 r36h96ca727_0
r-data.table 1.12.2 r36h96ca727_0
r-dbi 1.0.0 r36h6115d3f_0
r-dbplyr 1.4.0 r36h6115d3f_0
r-desc 1.2.0 r36h6115d3f_0
r-devtools 2.0.2 r36h6115d3f_0
r-dichromat 2.0_0 r36h6115d3f_4
r-digest 0.6.18 r36h96ca727_0
r-distutils 1.0 r36h516909a_1003 conda-forge
r-dplyr 0.8.0.1 r36h29659fb_0
r-ellipsis 0.1.0 r36h96ca727_0
r-elpigraph.r 1.0.0 r36h516909a_1003 conda-forge
r-essentials 3.6.0 r36_0
r-evaluate 0.13 r36h6115d3f_0
r-fansi 0.4.0 r36h96ca727_0
r-farver 1.1.0 r36h29659fb_0
r-forcats 0.4.0 r36h6115d3f_0
r-foreach 1.4.4 r36h6115d3f_0
r-foreign 0.8_71 r36h96ca727_0
r-formatr 1.6 r36h6115d3f_0
r-fs 1.2.7 r36h29659fb_0
r-generics 0.0.2 r36h6115d3f_0
r-gensa 1.1.7 r36h29659fb_0
r-ggforce 0.3.2 r36h0357c0b_0 conda-forge
r-ggplot2 3.1.1 r36h6115d3f_0
r-ggpubr 0.2.5 r36h6115d3f_1 conda-forge
r-ggrepel 0.8.2 r36h0357c0b_1 conda-forge
r-ggsci 2.9 r36h6115d3f_1003 conda-forge
r-ggsignif 0.6.0 r36h6115d3f_1 conda-forge
r-gh 1.0.1 r36h6115d3f_0
r-git2r 0.25.2 r36h96ca727_0
r-glmnet 2.0_16 r36ha65eedd_0
r-glue 1.3.1 r36h96ca727_0
r-gower 0.2.0 r36h96ca727_0
r-gridextra 2.3 r36h6115d3f_0
r-gtable 0.3.0 r36h6115d3f_0
r-haven 2.1.0 r36h29659fb_0
r-hexbin 1.27.2 r36ha65eedd_0
r-highr 0.8 r36h6115d3f_0
r-hms 0.4.2 r36h6115d3f_0
r-htmltools 0.3.6 r36h29659fb_0
r-htmlwidgets 1.3 r36h6115d3f_0
r-httpuv 1.5.1 r36h29659fb_0
r-httr 1.4.0 r36h6115d3f_0
r-igraph 1.2.4.1 r36h80f5a37_0
r-infotheo 1.2.0 r36h29659fb_0
r-ini 0.3.1 r36h6115d3f_0
r-ipred 0.9_8 r36h96ca727_0
r-irdisplay 0.7.0 r36h6115d3f_0
r-irkernel 0.8.15 r36_0
r-irlba 2.3.3 r36h96ca727_0
r-iterators 1.0.10 r36h6115d3f_0
r-jsonlite 1.6 r36h96ca727_0
r-kernsmooth 2.23_15 r36ha65eedd_4
r-knitr 1.22 r36h6115d3f_0
r-labeling 0.3 r36h6115d3f_4
r-later 0.8.0 r36h29659fb_0
r-lattice 0.20_38 r36h96ca727_0
r-lava 1.6.5 r36h6115d3f_0
r-lazyeval 0.2.2 r36h96ca727_0
r-lubridate 1.7.4 r36h29659fb_0
r-magrittr 1.5 r36h6115d3f_4
r-maps 3.3.0 r36h96ca727_0
r-markdown 0.9 r36h96ca727_0
r-mass 7.3_51.3 r36h96ca727_0
r-matrix 1.2_17 r36h96ca727_0
r-memoise 1.1.0 r36h6115d3f_0
r-mgcv 1.8_28 r36h96ca727_0
r-mime 0.6 r36h96ca727_0
r-modelmetrics 1.2.2 r36h29659fb_0
r-modelr 0.1.4 r36h6115d3f_0
r-munsell 0.5.0 r36h6115d3f_0
r-nlme 3.1_139 r36ha65eedd_0
r-nnet 7.3_12 r36h96ca727_0
r-numderiv 2016.8_1 r36h6115d3f_0
r-openssl 1.3 r36h96ca727_0
r-pbdzmq 0.3_3 r36h5455479_1
r-pillar 1.3.1 r36h6115d3f_0
r-pkgbuild 1.0.3 r36h6115d3f_0
r-pkgconfig 2.0.2 r36h6115d3f_0
r-pkgload 1.0.2 r36h29659fb_0
r-plogr 0.2.0 r36h6115d3f_0
r-plyr 1.8.4 r36h29659fb_0
r-polyclip 1.10_0 r36h29659fb_0
r-polynom 1.4_0 r36h6115d3f_0
r-prettyunits 1.0.2 r36h6115d3f_0
r-processx 3.3.0 r36h96ca727_0
r-prodlim 2018.04.18 r36h29659fb_0
r-progress 1.2.0 r36h6115d3f_0
r-promises 1.0.1 r36h29659fb_0
r-ps 1.3.0 r36h96ca727_0
r-purrr 0.3.2 r36h96ca727_0
r-quantmod 0.4_14 r36h6115d3f_0
r-r6 2.4.0 r36h6115d3f_0
r-randomforest 4.6_14 r36ha65eedd_0
r-rbokeh 0.6.3 r36_0
r-rcmdcheck 1.3.2 r36h6115d3f_0
r-rcolorbrewer 1.1_2 r36h6115d3f_0
r-rcpp 1.0.1 r36h29659fb_0
r-rcpparmadillo 0.9.300.2.0 r36h29659fb_0
r-rcppeigen 0.3.3.5.0 r36h29659fb_0
r-rcpproll 0.3.0 r36h29659fb_0
r-readr 1.3.1 r36h29659fb_0
r-readxl 1.3.1 r36h29659fb_0
r-recipes 0.1.5 r36h6115d3f_0
r-recommended 3.6.0 r36_0
r-rematch 1.0.1 r36h6115d3f_0
r-remotes 2.0.4 r36h6115d3f_0
r-repr 0.19.2 r36h6115d3f_0
r-reprex 0.2.1 r36h6115d3f_0
r-reshape2 1.4.3 r36h29659fb_0
r-rlang 0.3.4 r36h96ca727_0
r-rmarkdown 1.12 r36h6115d3f_0
r-rpart 4.1_15 r36h96ca727_0
r-rprojroot 1.3_2 r36h6115d3f_0
r-rsqlite 2.1.1 r36h29659fb_0
r-rstudioapi 0.10 r36h6115d3f_0
r-rvest 0.3.3 r36h6115d3f_0
r-scales 1.0.0 r36h29659fb_0
r-segmented 1.0_0 r36h6115d3f_0
r-selectr 0.4_1 r36h6115d3f_0
r-sessioninfo 1.1.1 r36h6115d3f_0
r-shiny 1.3.2 r36h6115d3f_0
r-sourcetools 0.1.7 r36h29659fb_0
r-spatial 7.3_11 r36h96ca727_4
r-squarem 2017.10_1 r36h6115d3f_0
r-stringi 1.4.3 r36h29659fb_0
r-stringr 1.4.0 r36h6115d3f_0
r-survival 2.44_1.1 r36h96ca727_0
r-sys 3.2 r36h96ca727_0
r-tibble 2.1.1 r36h96ca727_0
r-tictoc 1.0 r36h6115d3f_0
r-tidyr 0.8.3 r36h29659fb_0
r-tidyselect 0.2.5 r36h29659fb_0
r-tidyverse 1.2.1 r36h6115d3f_0
r-timedate 3043.102 r36h6115d3f_0
r-tinytex 0.12 r36h6115d3f_0
r-ttr 0.23_4 r36ha65eedd_0
r-tweenr 1.0.1 r36h0357c0b_1002 conda-forge
r-usethis 1.5.0 r36h6115d3f_0
r-utf8 1.1.4 r36h96ca727_0
r-uuid 0.1_2 r36h96ca727_4
r-vgam 1.1_1 r36ha65eedd_0
r-viridislite 0.3.0 r36h6115d3f_0
r-whisker 0.3_2 r36h6115d3f_4
r-withr 2.1.2 r36h6115d3f_0
r-xfun 0.6 r36h6115d3f_0
r-xml 3.98_1.19 r36h96ca727_0
r-xml2 1.2.0 r36h29659fb_0
r-xopen 1.0.0 r36h6115d3f_0
r-xtable 1.8_4 r36h6115d3f_0
r-xts 0.11_2 r36h96ca727_0
r-yaml 2.2.0 r36h96ca727_0
r-zoo 1.8_6 r36h96ca727_0
readline 8.1 h27cfd23_0
requests 2.27.1 pyhd3eb1b0_0
retrying 1.3.3 py_2
rpy2 2.9.4 py37r36hd767a1f_3 conda-forge
scanpy 1.6.0 py_0 bioconda
scikit-learn 0.23.2 py37h0573a6f_0
scipy 1.6.0 py37h91f5cce_0
seaborn 0.11.1 pyhd3eb1b0_0
send2trash 1.5.0 pyhd3eb1b0_1
setuptools 52.0.0 py37h06a4308_0
setuptools-scm 6.3.2 pyhd3eb1b0_0
setuptools_scm 6.3.2 hd3eb1b0_0
shapely 1.7.1 py37h98ec03d_0
simplegeneric 0.8.1 py37_2
sinfo 0.3.1 py_0 conda-forge
sip 4.19.8 py37hf484d3e_0
six 1.15.0 pyhd3eb1b0_0
snowballstemmer 2.2.0 pyhd3eb1b0_0
sphinx 4.2.0 pyhd3eb1b0_1
sphinxcontrib-applehelp 1.0.2 pyhd3eb1b0_0
sphinxcontrib-devhelp 1.0.2 pyhd3eb1b0_0
sphinxcontrib-htmlhelp 2.0.0 pyhd3eb1b0_0
sphinxcontrib-jsmath 1.0.1 pyhd3eb1b0_0
sphinxcontrib-qthelp 1.0.3 pyhd3eb1b0_0
sphinxcontrib-serializinghtml 1.1.5 pyhd3eb1b0_0
sqlite 3.33.0 h62c20be_0
statsmodels 0.12.1 py37h27cfd23_0
stdlib-list 0.7.0 py_2 conda-forge
stream 1.0 py_0 bioconda
tbb 2020.3 hfd86e86_0
terminado 0.9.2 py37h06a4308_0
testpath 0.4.4 pyhd3eb1b0_0
text-unidecode 1.3 py_0
threadpoolctl 2.1.0 pyh5ca1d4c_0
tk 8.6.10 hbc83047_0
tktable 2.10 h14c3975_0
tomli 1.2.2 pyhd3eb1b0_0
tornado 6.1 py37h27cfd23_0
tqdm 4.62.3 pyhd3eb1b0_1
traitlets 5.0.5 pyhd3eb1b0_0
tzlocal 2.1 py37_0
umap-learn 0.5.1 py37h89c1867_0 conda-forge
unidecode 1.1.2 pyhd3eb1b0_0
unzip 6.0 h611a1e1_0
urllib3 1.26.7 pyhd3eb1b0_0
wcwidth 0.2.5 py_0
webencodings 0.5.1 py37_1
wheel 0.36.2 pyhd3eb1b0_0
widgetsnbextension 3.5.1 py37_0
xz 5.2.5 h7b6447c_0
zeromq 4.3.3 he6710b0_3
zip 3.0 h1adfe0e_0
zipp 3.4.0 pyhd3eb1b0_0
zlib 1.2.11 h7b6447c_3
zstd 1.4.5 h9ceee32_0
What are your suggestions ?
Thanks for the help,
Best
Lea