Quanp – Quantitative Analysis in Python¶
Quanp is a scalable toolkit for analyzing cross-sectional and longitudinal/time-series quantitative data. It was first inspired by scanpy and jointly built with anndata. It includes preprocessing, visualization, clustering, features selection/importance.
Read the documentation. If you’d like to contribute by opening an issue or creating a pull request, please take a look at our contributing guide. If Quanp is useful for your research, consider being a contributor.
- Discuss usage on Discourse and development on GitHub.
- Get started by browsing tutorials, usage principles or the main API.
- Follow changes of AnnData in the release notes.
News¶
Latest additions¶
Tutorials¶
Clustering¶
For getting started, we recommend Quanp’s implementations for S&P500 member companies that contain preprocessing, clustering and the identification of features that defined a group/cluster of companies.
Factor Analysis¶
This tutorial analysed and visualized the underlying features that explain each principle component/factor extracted from the S&P500 member companies.
Usage Principles¶
Import Quanp as:
import quanp as qp
Workflow¶
The typical workflow consists of subsequent calls of data analysis tools in qp.tl, e.g.:
qp.tl.umap(adata, **tool_params) # embed a neighborhood graph of the data using UMAP
where adata is an AnnData object.
Each of these calls adds annotation to an expression matrix X,
which stores n_obs observations (subjects) of n_vars variables (features).
For each tool, there typically is an associated plotting function in qp.pl:
qp.pl.umap(adata, **plotting_params)
If you pass show=False, a Axes instance is returned
and you have all of matplotlib’s detailed configuration possibilities.
To facilitate writing memory-efficient pipelines, by default,
Quanp tools operate inplace on adata and return None –
this also allows to easily transition to out-of-memory pipelines.
If you want to return a copy of the AnnData object
and leave the passed adata unchanged, pass copy=True or inplace=False.
AnnData¶
Quanp is based on anndata, which provides the AnnData class.
At the most basic level, an AnnData object adata stores
a data matrix adata.X, annotation of observations
adata.obs and variables adata.var as pd.DataFrame and unstructured
annotation adata.uns as dict. Names of observations and
variables can be accessed via adata.obs_names and adata.var_names,
respectively. AnnData objects can be sliced like
dataframes, for example, adata_subset = adata[:, list_of_feature_names].
For more, see this blog post.
To read a data file to an AnnData object, call:
adata = qp.read(filename)
to initialize an AnnData object. Possibly add further annotation using, e.g., pd.read_csv:
import pandas as pd
anno = pd.read_csv(filename_sample_annotation)
adata.obs['subject_groups'] = anno['subject_groups'] # categorical annotation of type pandas.Categorical
adata.obs['time'] = anno['time'] # numerical annotation of type float
# alternatively, you could also set the whole dataframe
# adata.obs = anno
To write, use:
adata.write(filename)
adata.write_csvs(filename)
adata.write_loom(filename)
Installation¶
Anaconda¶
If you do not have a working installation of Python 3.6, consider installing Anaconda with Python=3.6 and create a vitualenv using conda. Then run:
conda install seaborn scikit-learn statsmodels numba pytables
conda install -c conda-forge python-igraph leidenalg
The extra python-igraph and leidenalg installs two packages that are needed for popular parts of quanp but aren’t requirements: python-igraph [Csardi06] and leiden [Traag18].
Pull Quanp from PyPI (consider using pip3 to access Python 3):
pip install quanp
Development Version¶
To work with the latest version on GitHub: clone the repository and cd into its root directory. To install using symbolic links (stay up to date with your cloned version after you update with git pull) call:
pip install -e .
Troubleshooting¶
If you get a Permission denied error, never use sudo pip. Instead, use virtual environments or:
pip install --user quanp
On MacOS, if not using conda, you might need to install the C core of igraph via homebrew first
brew install igraph
If python-igraph still fails to install, see the question on compiling igraph. Alternatively consider installing gcc via brew install gcc –without-multilib and exporting the required variables:
export CC="/usr/local/Cellar/gcc/X.x.x/bin/gcc-X" export CXX="/usr/local/Cellar/gcc/X.x.x/bin/gcc-X"
where X and x refers to the version of gcc; in my case, the path reads /usr/local/Cellar/gcc/6.3.0_1/bin/gcc-6.
On Windows, there also often problems installing compiled packages such as igraph, but you can find precompiled packages on Christoph Gohlke’s unofficial binaries. Download those and install them using pip install ./path/to/file.whl
Installing Anaconda¶
After downloading Anaconda, in a unix shell (Linux, Mac), run
cd DOWNLOAD_DIR
chmod +x Anaconda3-latest-VERSION.sh
./Anaconda3-latest-VERSION.sh
and accept all suggestions. Either reopen a new terminal or source ~/.bashrc on Linux/ source ~/.bash_profile on Mac. The whole process takes just a couple of minutes.
References¶
| [Csardi06] | Csardi et al. (2006), The igraph software package for complex network research, InterJournal Complex Systems. |
| [Traag18] | Traag et al. (2018), From Louvain to Leiden: guaranteeing well-connected communities arXiv. |