dots_for_microarrays | Simple analysis of Agilent one-color arrays

by sandyjmacdonald Python Version: 0.2.2 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions Vulnerabilities Install Support

kandi X-RAY | dots_for_microarrays Summary

dots_for_microarrays is a Python library. dots_for_microarrays has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install dots_for_microarrays' or download it from GitHub, PyPI.

Simple analysis of Agilent one-color arrays

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Quality

Security

License

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Support

dots_for_microarrays has a low active ecosystem.

It has 4 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 17 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of dots_for_microarrays is 0.2.2

Quality

dots_for_microarrays has no bugs reported.

Security

dots_for_microarrays has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

dots_for_microarrays is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

dots_for_microarrays releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed dots_for_microarrays and discovered the below as its top functions. This is intended to give you an instant insight into dots_for_microarrays implemented functionality, and help decide if they suit your requirements.

Plot the box plot
Return list of sample ids
Returns a pandas dataframe containing the normalized exp values
Create a standard plot
Create an Experiment instance from a list of arrays
Read an array from file
Normalise the dataframe
Set the baseline values to the median
Create a heatmap
Calculates k - means clustering
Calculate the mean difference between each group
Returns a pandas DataFrame containing all the clusters in the given experiment
Run PCA
Run PCA on a given experiment
Return the number of colours
Read a numpy array
Reads an annotation file
Write fcs stats to file
Create the clusters plot
Generate a heatmap plot
Write a normalised expression table
Set the baseline to the median value

Get all kandi verified functions for this library.

dots_for_microarrays Key Features

No Key Features are available at this moment for dots_for_microarrays.

dots_for_microarrays Examples and Code Snippets

Dots,Installation

Python

Lines of Code : 11

License : Permissive (MIT)

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sudo pip install dots_for_microarrays

git clone https://github.com/sandyjmacdonald/dots_for_microarrays
cd dots_for_microarrays
sudo python setup.py install

conda create --yes -n dots_env python=2.7
source activate dots_env

git clone https://githu

Dots,The dots_analysis module

Python

Lines of Code : 9

License : Permissive (MIT)

Copy

fold_changes = get_fold_changes(experiment)

stats = run_stats(experiment)

pca_df = run_pca(experiment)

hier_clusters = find_clusters(experiment_med.df, how='hierarchical')
km_clusters = find_clusters(experiment_med.df, k_vals=range(3,11), how='kme

Dots,The dots_arrays module,The Array class and read_array function

Python

Lines of Code : 5

License : Permissive (MIT)

Copy

array = read_array(filename, group, replicate)

array_df = array.df

genes = array.genenames

norm_array = array.normalise()

norm_intensities = array.get_normalised_intensities()

Community Discussions

No Community Discussions are available at this moment for dots_for_microarrays.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install dots_for_microarrays

IMPORTANT Dots requires PhantomJS to render the html plots produced by Bokeh to png image files. The most recent version of PhantomJS (2.0) does not seem to work properly on OS X El Capitan, so I'd recommend using PhantomJS version 1.9.8. Download links for OS X, Windows and Linux are below.
Dots has a handy workflow script that takes as input a folder containing some Agilent array files (labelled correctly as explained above) and reads in the data, normalises it, and produces tables of data and all of the various volcano plots, etc.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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