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This project provides an Spectre's implementation of Data Mining Group's algorithms for processing mass spectrometry imaging (MSI) datasets.

Home Page: https://spectre-team.github.io/

License: Apache License 2.0

CSS 2.85% HTML 7.55% JavaScript 1.58% PowerShell 0.89% Batchfile 0.23% TypeScript 86.91%

msi gaussian-mixture-models proteomics classification clustering mass-spectrometry-imaging tumor-heterogeneity pattern-recognition

spectre's Introduction

Spectre

Spectre is a versatile tool used for analysis of MALDI-MSI data sets.

For the sake of simplicity, the toolset provided is available to be used through web application interface, which is currently a work-in-progress.

In order to build and run the application, please refer to the installation section.

About

The project is currently in its early stage. However, it comprises the implementation of our own spectra modelling based on Gaussian Mixture Models, and Divisive IK-means algorithm for unsupervised segmentation, which can be used for efficient dataset compression, as well as for knowledge discovery. Aformentioned algorithms have already been published and refering links have been enclosed under references section.

Also, several classification and clusterization methods will be provided soon, along with supporting statistics.

Install

Spectre service & web application

The Web service is all the time available here. If you still would like to host it yourself, please contact us by e-mail. We will provide you with exhaustive explanation.

Exemplary usage

Spectre service & web application

The application is available online here.

Right now, our web application allows only for an interactive visualization of some of the data we were using in the research, along with Divisive Intelligent K-means algorithm results. More features will get documented when they appear.

How to contribute?

Please contact us by e-mail. We will answer you in details.

Short contribution guide is actively constructed on the project's wiki as the development progresses.

Angular

This project was generated with Angular CLI version 1.0.0-rc.2.

Installation

In order to install Angular CLI, please run scripts/InstallNodeDev.ps1 using Windows Powershell. If the script doesn't have permission to run, please execute Set-ExecutionPolicy Unrestricted command to enable running scripts from Powershell, run InstallNodeDev.ps1, and finish with Set-ExecutionPolicy Restricted (default value) if you will.

Troubleshooting:

If nodejs.msi couldn't have been successfully downloaded and installed, please install it manually and the rest of the script should run just fine.

Development server

Run ng serve for a dev server. Navigate to http://localhost:4200/. The app will automatically reload if you change any of the source files.

If calling ng serve causes errors regarding uninstalled packages, please delete src\Spectre.Angular2Client\node_modules directory, enter src\Spectre.Angular2Client\ and run npm install.

Code scaffolding

Run ng generate component component-name to generate a new component. You can also use ng generate directive/pipe/service/class/module.

Build

Run ng build to build the project. The build artifacts will be stored in the dist/ directory. Use the -prod flag for a production build.

Running unit tests

Run ng test to execute the unit tests via Karma.

Running end-to-end tests

Run ng e2e to execute the end-to-end tests via Protractor. Before running the tests make sure you are serving the app via ng serve.

Further help

To get more help on the Angular CLI use ng help or go check out the Angular CLI README.

References

This software is part of contribution made by Data Mining Group of Silesian University of Technology, rest of which is published here.

spectre's People

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spectre's Issues

Provide interface to list all preparations available

The task is to provide API functionality of listing all preparations which were uploaded to database yet.

Preparations in the list should be restricted to only those available to a particular authenticated user.

Provide a heatmap for selected mass channel

The task is to expose a heatmap of selected mass channel through HTTP.

Exact form of sharing this with frontend will be announced soon.

Prepare specification of REST API

The task is to provide a specification of exposed REST API as detailed as possible.

Summary of statistical libraries

The task is to prepare an article in wiki, which explains pros and cons of different statistical libraries for C++ or .NET.

Important aspects are:

documentation
support
community
coverage of our needs
integration with our data structures and standard C++ / .NET structures

Factory of data sets

Restrict creation of data set to a factory.

Save feature for data set

Add functionality to dump a .txt file with data.

Testing rules

The task is to create rules for automatic testing of the solution.

Documentation requirements

The task is to specify documentation comments requirements.

Add license headers

Add appropriate license header to all files missing, using following guidelines (copied from the bottom of LICENSE file):

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "{}"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright {yyyy} {name of copyright owner}

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.```

Docker

Prepare Docker configuration and quick tutorial for the rest of the team.

C++ Outlier detection

Adaptive outlier detection algorithm. In the case of skewed distribution works with Tukey criterion, medcouple otherwise.

Build simple MVVM framework for WPF

The task is to provide a simple base speeding up WPF application design and implementation process.

Find another statistical libraries

Prepare substantive tests for DiviK

The task is to provide testing methods which checks, whether DiviK algorithm works properly and outputs the same partition.

Prepare standalone DiviK software

The task is to prepare a standalone tool for performing DiviK clustering / compression.

Docs update on NUnit test framework

The task is to update Wiki with informations regarding .NET testing to cover only NUnit test framework.

Move specific settings of DiviK to 'Advanced' section

Configure AppVeyor

The task is to configure AppVeyor CI to run tests on our commits.

Create simple WPF client for DiviK

The task is to create simple WPF standalone application which just runs DiviK algorithm on a data format we provide.

Main functionality should cover:

selection of input dataset
selection of output destination
specification of DiviK parameters
running analysis, as already implemented

.NET interfaces for algorithms

The task is to prepare .NET interfaces for accessing C++/MATLAB implementations of algorithms.

Create sample user stories

The task is to create scenarios of software usage and place them on Wiki.

Add license headers to WPF client

CI tool comparison

The task is to compare tools use in continuous integration.

Add usage manual

The task is to add usage manual to Readme file and to create a simple pdf file with a description.

Equality comparer for DivikResult

There is an Equals() method overload missing.

Database schema

The task is to design a database schema allowing to store:

users; their permission levels; information, whether a user has been activated by an admin; recent login
preparations already uploaded to the server; their location on disk; comments; metadata (should be similar to those provided in imzml); dependencies describing derived datasets; users allowed to view a dataset (some may be shared among all users, some may be private and some shared to selected friends)
job log entries (i.e. scheduler log)

C++ Resampling implementation

This task is to create an efficient implementation of spectra resampling across investigated data.

Functions selecting common m/z range (truncating range) and common m/z values (through interpolation) should be provided.

Provide interface to list all available m/z for a preparation

The task is to provide REST API interface for listing m/z values available for each preparation. Assume sample is after resampling and m/z axes for all spots are identical.