RamEx

A ramanome represents a single-cell-resolution metabolic phenome that is information-rich, revealing functional heterogeneity among cells, and universally applicable to all cell types. Ramanome Explorer (RamEx, Fig.1) is a toolkit for comprehensive and efficient analysis and comparison of ramanomes. Results from the multidimensional analysis are visualized via intuitive graphics. Implemented via R, RamEx is fully extendable and supports cross-platform use.By providing simple-to-use modules for computational tasks that otherwise would require substantial programming experience and algorithmic skills, RamEx should greatly facilitate the computational mining of ramanomes.

RamEx is built on the following principles:

• Reliability achieved via stringent statistical control
• Robustness achieved via flexible modelling of the data and automatic parameter selection
• Reproducibility promoted by thorough recording of all analysis steps
• Ease of use: high degree of automation, an analysis can be set up in several mouse clicks, no bioinformatics expertise required
• Powerful tuning options to enable unconventional experiments
• Scalability and speed: up to xxx runs processed per minutes

Download: https://github.com/qibebt-bioinfo/RamEx (it's recommended to use the latest version - RamEx 2.1)

Please cite
**RamEx : xxxx

xxxx, 2024

When using RamEx for xxxx
IRCA
x'x'x, 2021

When using RamEx for xxxx RBCS
x'x'x, 2022

When using RamEx for xxxx xxx x'x'x, 2022

Other key papers

• xxxx:
  x'x'x, 2020 and Cell Systems, 2021
• xxxx:
  x'x'x, 2021

R package some useful functions:https://github.com/qibebt-bioinfo/RamEx Visualisation of ramanome

Table of Contents

Installation
Getting started
Raw data formats
Output
Changing default settings
Visualisation
Automated pipelines
Functions
Frequently asked questions (FAQ)
Contact

Installation

RamEx will be installed from GitHub:.

library('devtools'); 
install_github("qibebt-bioinfo/RamEx")

Getting Started

RamEx can be installed simply:

1. load the RamEx package:

library('ramextest'); 

2. load the data read_spec and generate a ramanome object.
3. xxx
4. xxx
5. xxx

Raw data formats

Formats supported: xxx.

Output

The Output pane allows to specify where the output should be saved. There are xxx types of output files produced by RamEx: XXXX

Changing default settings

RamEx can be successfully used to process almost any experiment with default settings. In general, it is recommended to only change settings when specifically advised to xxx.

In many cases, one might want to change several parameters: .

• xxx should be enabled in most cases, xxx.

Visualisation

Automated pipelines

The pipeline allows to xxx.

functions

Import Import the xx data

• R Data xxx Quality Control Import the xx data
• Outlier Detection xxx Cell-level analysis Import the xx data
• Outlier Detection xxx Singal-level analysis Import the xx data
• RBCS xxx Visualization Import the xx data
• mean_spectrum xxx

Frequently asked questions

Q: Why RamEx?
A: xxx

Contact

Please post any questions, feedback, comments or suggestions on the GitHub Discussion board (alternatively can create a GitHub issue) or email SCC