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sbslee / dokdo

A Python package for microbiome sequencing analysis with QIIME 2
https://dokdo.readthedocs.io
MIT License
43 stars 12 forks source link

ModuleNotFoundError: No module named 'skbio' #47

Closed rupesh-sinha closed 2 years ago

rupesh-sinha commented 2 years ago

Hi,

I installed dokdo using the following command when in conda environment qiime2-2022.8

$ git clone https://github.com/sbslee/dokdo
$ cd dokdo
$ pip install .

However, I do not understand where I have to run the below script for plotting the taxa barplot. I assumed I can run it on Jupyter Notebook and did that but got the below error. Please let me know where I am going wrong

import sys
sys.path.append('/home/streptomyces/Rupesh/Suzanne/aqua_fastq/qiime2/dokdo/')
import dokdo
dokdo.taxa_abundance_bar_plot(
    '/home/streptomyces/Rupesh/Suzanne/aqua_fastq/qiime2/taxa-bar-plots-nc-wobl-nmcl.qzv',
    figsize=(10, 7),
    level=6,
    count=8,
    legend_short=True
)

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Input In [7], in <cell line: 3>()
      1 import sys
      2 sys.path.append('/home/streptomyces/Rupesh/Suzanne/aqua_fastq/qiime2/dokdo/')
----> 3 import dokdo
      4 dokdo.taxa_abundance_bar_plot(
      5     '/home/streptomyces/Rupesh/Suzanne/aqua_fastq/qiime2/taxa-bar-plots-nc-wobl-nmcl.qzv',
      6     figsize=(10, 7),
   (...)
      9     legend_short=True
     10 )

File ~/Rupesh/Suzanne/aqua_fastq/qiime2/dokdo/dokdo/__init__.py:1, in <module>
----> 1 from .api import *

File ~/Rupesh/Suzanne/aqua_fastq/qiime2/dokdo/dokdo/api/__init__.py:1, in <module>
----> 1 from .common import get_mf, pname
      2 from .ordinate import ordinate
      3 from .num2sig import num2sig

File ~/Rupesh/Suzanne/aqua_fastq/qiime2/dokdo/dokdo/api/common.py:15, in <module>
     13 from matplotlib.patches import Patch
     14 import seaborn as sns
---> 15 import skbio as sb
     16 from skbio.stats.ordination import OrdinationResults
     17 from scipy import stats

ModuleNotFoundError: No module named 'skbio'

I am new to API, your guidelines will be a big help!

sbslee commented 2 years ago

@rupesh-sinha,

You are correct to use Dokdo API with Jupyter Notebook. The error message suggests that you don't have the skbio package installed in your current conda environment, which is a required package for running QIIME 2. Therefore, this indicates that you are not currently running your Jupyter Notebook in a conda environment with QIIME 2 installed.

  1. Are you sure you activated your qiime2-2022.8 environment ($ conda activate qiime2-2022.8)?
  2. If yes, can you show me the output of the following command: $ conda list
  3. If yes, can you also show me the output of the command: $ dokdo -v
  4. Finally, you don't need sys.path.append() if you installed Dokdo correctly.

Please let me know if you have further questions.

rupesh-sinha commented 2 years ago

Hi sbslee,

Many thanks for your reply. You are right I was not running the jupyter notebook from conda environment. However, I pip install jupyter in conda environment now and running jupyter notebook from my qiime2-2022.8 env. I got the output for the script but unable to understand where to look for the result or whether it created any plot. Sorry I am very new to API! Below is script I run on jupyter notebook webserver kernel and its output:

In [4]: import dokdo
dokdo.taxa_abundance_bar_plot(
    '/home/streptomyces/Rupesh/Suzanne/aqua_fastq/qiime2/taxa-bar-plots-nc-wobl-nmcl.qzv',
    figsize=(10, 7),
    level=6,
    count=8,
    legend_short=True
)

Out [4]: <AxesSubplot:ylabel='Relative abundance (%)'>

What does this output mean?

Also for your queries: C(qiime2-2022.8) streptomyces@SBIO-KT-LIN-2:~$ dokdo -v dokdo 1.15.0

(qiime2-2022.8) streptomyces@SBIO-KT-LIN-2:~$ conda list

packages in environment at /home/streptomyces/Rupesh/envir/Rupesh/Anaconda3/envs/qiime2-2022.8:

#

Name Version Build Channel

_libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge _r-mutex 1.0.1 anacondar_1 conda-forge _tflow_select 2.3.0 mkl
absl-py 1.2.0 pypi_0 pypi aiohttp 3.6.3 py38h7b6447c_0
alsa-lib 1.2.6.1 h7f98852_0 conda-forge argcomplete 2.0.0 pyhd8ed1ab_0 conda-forge argon2-cffi 21.3.0 pypi_0 pypi argon2-cffi-bindings 21.2.0 pypi_0 pypi astor 0.8.1 py38h06a4308_0
asttokens 2.0.8 pyhd8ed1ab_0 conda-forge astunparse 1.6.3 py_0
async-timeout 3.0.1 py38h06a4308_0
atpublic 3.0.1 pyhd8ed1ab_0 conda-forge attr 2.5.1 h166bdaf_1 conda-forge attrs 22.1.0 pyh71513ae_1 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge backports 1.0 py_2 conda-forge backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge beautifulsoup4 4.11.1 pypi_0 pypi bibtexparser 1.3.0 pyhd8ed1ab_0 conda-forge binutils_impl_linux-64 2.36.1 h193b22a_2 conda-forge bioconductor-biobase 2.54.0 r41h5c21468_1 bioconda bioconductor-biocgenerics 0.40.0 r41hdfd78af_0 bioconda bioconductor-biocparallel 1.28.3 r41h619a076_0 bioconda bioconductor-biostrings 2.62.0 r41h5c21468_1 bioconda bioconductor-dada2 1.22.0 r41h619a076_1 bioconda bioconductor-delayedarray 0.20.0 r41h5c21468_1 bioconda bioconductor-genomeinfodb 1.30.0 r41hdfd78af_0 bioconda bioconductor-genomeinfodbdata 1.2.7 r41hdfd78af_2 bioconda bioconductor-genomicalignments 1.30.0 r41h5c21468_1 bioconda bioconductor-genomicranges 1.46.1 r41h5c21468_0 bioconda bioconductor-iranges 2.28.0 r41h5c21468_1 bioconda bioconductor-matrixgenerics 1.6.0 r41hdfd78af_0 bioconda bioconductor-rhtslib 1.26.0 r41h5c21468_1 bioconda bioconductor-rsamtools 2.10.0 r41h619a076_1 bioconda bioconductor-s4vectors 0.32.3 r41h5c21468_0 bioconda bioconductor-shortread 1.52.0 r41h619a076_1 bioconda bioconductor-summarizedexperiment 1.24.0 r41hdfd78af_0 bioconda bioconductor-xvector 0.34.0 r41h5c21468_1 bioconda bioconductor-zlibbioc 1.40.0 r41h5c21468_1 bioconda biom-format 2.1.12 py38h71d37f0_1 conda-forge blast 2.13.0 hf3cf87c_0 bioconda bleach 5.0.1 pypi_0 pypi blinker 1.4 py38h06a4308_0
bokeh 2.4.3 pyhd8ed1ab_3 conda-forge bowtie2 2.4.5 py38hfbc8389_3 bioconda brotli 1.0.9 h166bdaf_7 conda-forge brotli-bin 1.0.9 h166bdaf_7 conda-forge brotlipy 0.7.0 py38h0a891b7_1004 conda-forge bwidget 1.9.14 ha770c72_1 conda-forge bzip2 1.0.8 h7f98852_4 conda-forge c-ares 1.18.1 h7f98852_0 conda-forge ca-certificates 2022.9.14 ha878542_0 conda-forge cachecontrol 0.12.11 pyhd8ed1ab_0 conda-forge cachetools 5.2.0 pypi_0 pypi cairo 1.16.0 ha61ee94_1013 conda-forge certifi 2022.9.14 pyhd8ed1ab_0 conda-forge cffi 1.15.1 py38h4a40e3a_0 conda-forge chardet 3.0.4 py38h06a4308_1003
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge click 8.1.3 py38h578d9bd_0 conda-forge colorama 0.4.5 pyhd8ed1ab_0 conda-forge cryptography 37.0.4 py38h2b5fc30_0 conda-forge curl 7.83.1 h7bff187_0 conda-forge cutadapt 4.1 py38hbff2b2d_1 bioconda cycler 0.11.0 pyhd8ed1ab_0 conda-forge cython 0.29.32 py38hfa26641_0 conda-forge dataclasses 0.8 pyh6d0b6a4_7
dbus 1.13.6 h5008d03_3 conda-forge deblur 1.1.1 pyhdfd78af_0 bioconda debugpy 1.6.3 py38hfa26641_0 conda-forge decorator 4.4.2 py_0 conda-forge defusedxml 0.7.1 pypi_0 pypi deicode 0.2.4 py38h32f6830_0 conda-forge dendropy 4.5.2 pyh3252c3a_0 bioconda dill 0.3.5.1 pyhd8ed1ab_0 conda-forge dnaio 0.9.1 py38hbff2b2d_1 bioconda dokdo 1.15.0 pypi_0 pypi emperor 1.0.3 py38h578d9bd_0 conda-forge entrez-direct 16.2 he881be0_1 bioconda entrypoints 0.4 pyhd8ed1ab_0 conda-forge executing 0.10.0 pyhd8ed1ab_0 conda-forge expat 2.4.8 h27087fc_0 conda-forge fastcluster 1.2.6 py38h47df419_1 conda-forge fasttree 2.1.11 hec16e2b_1 bioconda fftw 3.3.10 nompi_hf0379b8_4 conda-forge flatbuffers 2.0.7 pypi_0 pypi flufl.lock 7.0 pyhd8ed1ab_0 conda-forge font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge font-ttf-inconsolata 3.000 h77eed37_0 conda-forge font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge font-ttf-ubuntu 0.83 hab24e00_0 conda-forge fontconfig 2.14.0 h8e229c2_0 conda-forge fonts-conda-ecosystem 1 0 conda-forge fonts-conda-forge 1 0 conda-forge fonttools 4.37.1 py38h0a891b7_0 conda-forge freetype 2.12.1 hca18f0e_0 conda-forge fribidi 1.0.10 h36c2ea0_0 conda-forge future 0.18.2 py38h578d9bd_5 conda-forge gast 0.4.0 pyhd3eb1b0_0
gcc_impl_linux-64 12.1.0 hea43390_16 conda-forge gettext 0.19.8.1 h73d1719_1008 conda-forge gfortran_impl_linux-64 12.1.0 h1db8e46_16 conda-forge giflib 5.2.1 h36c2ea0_2 conda-forge glib 2.72.1 h6239696_0 conda-forge glib-tools 2.72.1 h6239696_0 conda-forge gneiss 0.4.6 py_0 bioconda google-auth 2.11.0 pypi_0 pypi google-auth-oauthlib 0.4.6 pypi_0 pypi google-pasta 0.2.0 pyhd3eb1b0_0
graphite2 1.3.13 h58526e2_1001 conda-forge grpcio 1.49.0 pypi_0 pypi gsl 2.7 he838d99_0 conda-forge gst-plugins-base 1.20.3 hf6a322e_0 conda-forge gstreamer 1.20.3 hd4edc92_0 conda-forge gxx_impl_linux-64 12.1.0 hea43390_16 conda-forge h5py 2.10.0 nompi_py38h9915d05_106 conda-forge harfbuzz 5.1.0 hf9f4e7c_0 conda-forge hdf5 1.10.6 nompi_h6a2412b_1114 conda-forge hdmedians 0.14.2 py38h71d37f0_2 conda-forge hmmer 3.1b2 3 bioconda htslib 1.15.1 h9753748_0 bioconda icu 70.1 h27087fc_0 conda-forge idna 3.3 pyhd8ed1ab_0 conda-forge ijson 3.1.3 pyhd3deb0d_0 conda-forge importlib-metadata 4.11.4 py38h578d9bd_0 conda-forge importlib_metadata 4.11.4 hd8ed1ab_0 conda-forge importlib_resources 5.9.0 pyhd8ed1ab_0 conda-forge iniconfig 1.1.1 pyh9f0ad1d_0 conda-forge iow 1.0.5 py38h8ded8fe_1 bioconda ipykernel 6.15.1 pyh210e3f2_0 conda-forge ipython 8.4.0 pyh41d4057_1 conda-forge ipython_genutils 0.2.0 py_1 conda-forge ipywidgets 8.0.1 pyhd8ed1ab_0 conda-forge iqtree 2.2.0.3 hb97b32f_1 bioconda isa-l 2.30.0 ha770c72_4 conda-forge jack 1.9.18 h8c3723f_1002 conda-forge jedi 0.18.1 pyhd8ed1ab_2 conda-forge jinja2 3.1.2 pyhd8ed1ab_1 conda-forge joblib 1.1.0 pyhd8ed1ab_0 conda-forge jpeg 9e h166bdaf_2 conda-forge jq 1.6 h36c2ea0_1000 conda-forge jsonschema 4.14.0 pyhd8ed1ab_0 conda-forge jupyter_client 7.3.4 pyhd8ed1ab_0 conda-forge jupyter_core 4.11.1 py38h578d9bd_0 conda-forge jupyterlab-pygments 0.2.2 pypi_0 pypi jupyterlab_widgets 3.0.2 pyhd8ed1ab_0 conda-forge keras 2.10.0 pypi_0 pypi keras-preprocessing 1.1.2 pyhd3eb1b0_0
kernel-headers_linux-64 2.6.32 he073ed8_15 conda-forge keyutils 1.6.1 h166bdaf_0 conda-forge kiwisolver 1.4.4 py38h43d8883_0 conda-forge krb5 1.19.3 h3790be6_0 conda-forge lcms2 2.12 hddcbb42_0 conda-forge ld_impl_linux-64 2.36.1 hea4e1c9_2 conda-forge lerc 3.0 h9c3ff4c_0 conda-forge libblas 3.9.0 16_linux64_openblas conda-forge libbrotlicommon 1.0.9 h166bdaf_7 conda-forge libbrotlidec 1.0.9 h166bdaf_7 conda-forge libbrotlienc 1.0.9 h166bdaf_7 conda-forge libcap 2.64 ha37c62d_0 conda-forge libcblas 3.9.0 16_linux64_openblas conda-forge libclang 14.0.6 pypi_0 pypi libclang13 14.0.6 default_h3a83d3e_0 conda-forge libcups 2.3.3 h3e49a29_2 conda-forge libcurl 7.83.1 h7bff187_0 conda-forge libdb 6.2.32 h9c3ff4c_0 conda-forge libdeflate 1.10 h7f98852_0 conda-forge libedit 3.1.20191231 he28a2e2_2 conda-forge libev 4.33 h516909a_1 conda-forge libevent 2.1.10 h9b69904_4 conda-forge libffi 3.4.2 h7f98852_5 conda-forge libflac 1.3.4 h27087fc_0 conda-forge libgcc-devel_linux-64 12.1.0 h1ec3361_16 conda-forge libgcc-ng 12.1.0 h8d9b700_16 conda-forge libgfortran-ng 12.1.0 h69a702a_16 conda-forge libgfortran5 12.1.0 hdcd56e2_16 conda-forge libglib 2.72.1 h2d90d5f_0 conda-forge libgomp 12.1.0 h8d9b700_16 conda-forge libiconv 1.16 h516909a_0 conda-forge libidn2 2.3.3 h166bdaf_0 conda-forge liblapack 3.9.0 16_linux64_openblas conda-forge liblapacke 3.9.0 16_linux64_openblas conda-forge libllvm10 10.0.1 he513fc3_3 conda-forge libllvm14 14.0.6 he0ac6c6_0 conda-forge libnghttp2 1.47.0 hdcd2b5c_1 conda-forge libnsl 2.0.0 h7f98852_0 conda-forge libogg 1.3.4 h7f98852_1 conda-forge libopenblas 0.3.21 pthreads_h78a6416_2 conda-forge libopus 1.3.1 h7f98852_1 conda-forge libpng 1.6.37 h753d276_4 conda-forge libpq 14.5 hd77ab85_0 conda-forge libprotobuf 3.20.1 h4ff587b_0
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Many thanks for your patience!

sbslee commented 2 years ago

@rupesh-sinha,

I got the output for the script but unable to understand where to look for the result or whether it created any plot.

If the issue is that you are not seeing any plots after running dokdo.taxa_abundance_bar_plot(), you need to set the magic function %matplotlib inline as shown below:

import matplotlib.pyplot as plt
%matplotlib inline

Now the output of plotting methods will be displayed inline within Jupyter Notebook. More information can be found in Dokdo API which I highly recommend you take a look.

rupesh-sinha commented 2 years ago

Thanks sbslee!

import matplotlib.pyplot as plt %matplotlib inline

solved the issue. Also got to learn other useful parameters which I wanted to apply like using exclude_sample etc. Just wanted to know if we want to plot more than 8 counts what would be options to choose color palette. At the moment when I do count 10 or more it starts repeating the colors.

Thanks for your valuable guidance!

sbslee commented 2 years ago

@rupesh-sinha,

If you look at the documentation for dokdo.taxa_abundance_bar_plot() I provide a number of examples. There, you will see that cmap_name='tab20' is used with count=20. You can find more color map options here.

rupesh-sinha commented 2 years ago

Thanks! For legends I found a script to plot it separately from the figure which was very helpful, thanks for providing these examples. Is there a way to remove characters like 'D5' from the legends?

sbslee commented 2 years ago

@rupesh-sinha, if you are asking how to modify legend text labels, I recommend that you search in places like Stack Overflow (e.g. check out this post).

sbslee commented 2 years ago

Please feel free to reopen this if necessary!