Update README.md

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Author: kopeckylukas

README.md

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 # Chemometric data analysis tutorials
 
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/IPTC-DataAnalysisCourse/chemometrics-tutorials/HEAD)
-
 This repository contains a series of tutorials on multivariate analysis of metabolic profiling datasets: 
  - Import, scaling & normalisation.ipynb: Introduction to normalisation, scaling and data transformations.  
  - Multivariate Analysis - PCA.ipynb: Multivariate chemometric analysis using Principal Component Analysis.
  - Multivariate Analysis - Supervised Analysis with PLS-DA.ipynb: Discrimnination of 2 classes with PLS-DA.
- - Univariate Analysis.ipynb: Univariate analysis with linear models 
 
-To run these tutorials download the contents of this repository and run the Jupyter Notebooks. Alternatively, these can be run on the browser via Binder, by clicking on the sticker above (subject to load on the Binder servers). 
+To run these tutorials download the contents of this repository and run the Jupyter Notebooks. Alternatively, these can be run on the browser via Google Colaboratory by clickinking on the links at the IPTC - Data Analysis Course [repository](https://github.com/IPTC-DataAnalysisCourse).  
 
 All the data required to run these notebooks is provided on the 'data' folder. The dataset used in this tutorial comes from the following publication:
-- Blaise, Benjamin J. et al. “Metabotyping of Caenorhabditis elegans reveals latent phenotypes.” Proceedings of the National Academy of Sciences of the United States of America 104 50 (2007): 19808-12 .
+- Lovestone, S., Francis, P., Kloszewska, I., Mecocci, P., Simmons, A., Soininen, H., Spenger, C., Tsolaki, M., Vellas, B., Wahlund, L.-O., Ward, M. and (2009), AddNeuroMed—The European Collaboration for the Discovery of Novel Biomarkers for Alzheimer's Disease. Annals of the New York Academy of Sciences, 1180: 36-46. [https://doi.org/10.1111/j.1749-6632.2009.05064.x](https://doi.org/10.1111/j.1749-6632.2009.05064.x).
 
-It is a set of 139 proton high-resolution magic angle spinning NMR spectroscopy (1H HR-MAS NMR) spectra from 
-C. elegans nematodes. There are two main biological sources of variation in the dataset:
-- Genotype (1: wild-type, 2: *sod-2* mutants)
-- Age/life stage (1: younger L2 worms, 2: L4 worms)
+It is a set of urine samples 577 individuals processes by using Liquid Chromatography - Mass Spectrometery instrument.  
+There are two main biological sources of variation in the dataset:
+- Gender/Sex (0: Biological female (n=294)) , 1: Biological male (n=283))
+- Age 
 
 
 ## Installation instructions:
-To run these tutorials, first download and install the latest Anaconda Python 3.x distribution available. 
-The following packages also need to be installed:
- - plotly: Available via conda (open an Anaconda prompt and type "conda install plotly")
-After installing plotly, either clone this repository or download as .zip file. Access the notebooks contents via the Jupyter notebook environment. 
+To run these tutorials localy, first download and install the latest Anaconda Python 3.10.x distribution available. 
+The following packages also need to be installed (installed specific version if stated for compatibility):
+- plotly: Available via conda (open an Anaconda prompt and type "conda install plotly")
+- SciPy: 1.11.3
+- NumPy: 1.26.0
+- scikit-learn: 1.2.1
+- Pandas: 2.1.1
+- Matplotlib: 3.6.2
+- seaborn
+- kneed: 0.8.5 
 
 All other dependencies required to run the tutorials (pyChemometrics toolbox) are bundled with the repository, and no specific installation is required. 
 
+After installing plotly, either clone this repository or download as .zip file. Access the notebooks contents via the Jupyter notebook environment. 
+
 For more information on how to use Jupyter notebooks see [Using the Jupyter notebook](https://docs.anaconda.com/ae-notebooks/user-guide/basic-tasks/apps/jupyter/)