1 Day In-Person Hands-On Training on Clustering Analysis using Gene Expression Dataset

INTRODUCTION TO CLUSTERING ANALYSIS USING GENE EXPRESSION DATASET

Clustering Analysis for Gene Expression Dataset is a powerful technique used to uncover patterns and group similar gene expression profiles, offering invaluable insights into biological processes and disease mechanisms. This method involves the application of various clustering algorithms to group genes or samples based on their expression levels, revealing underlying structures in the data that are not immediately apparent.

One common approach in Clustering Analysis for Gene Expression Dataset is hierarchical clustering. This method builds a tree-like structure, or dendrogram, to visualize the relationships between different genes or samples. By examining the branches of the dendrogram, researchers can identify clusters of genes that exhibit similar expression patterns, which often correspond to specific biological functions or pathways.

Another popular method is k-means clustering, which partitions the dataset into k distinct clusters. This algorithm iteratively assigns each gene or sample to the cluster with the nearest mean expression level, optimizing the cluster centers until convergence. K-means clustering is particularly useful for large datasets due to its computational efficiency and simplicity.

APPLICATIONS OF CLUSTERING ANALYSIS USING GENE EXPRESSION DATASET

1. Identifying Gene Function and Pathways: Clustering analysis helps identify groups of genes with similar expression patterns, which often share common biological functions or participate in the same pathways.
2. Cancer Subtyping: Clustering analysis is widely used to classify cancer patients into subtypes based on their gene expression profiles.
3. Biomarker Discovery: In the search for biomarkers, clustering analysis can identify genes or sets of genes whose expression levels are strongly associated with particular disease states or conditions.
4. Understanding Developmental Processes: During development, gene expression patterns change dynamically. Clustering analysis can group genes with similar temporal expression patterns, helping to uncover the regulatory mechanisms that drive developmental processes.
5. Drug Response Prediction: Clustering gene expression data from different treatment conditions can reveal how genes and pathways respond to various drugs.

DIFFERENT TYPES OF CLUSTERING ANALYSIS USING GENE EXPRESSION DATASET

1. Hierarchical Clustering: It creates a tree-like structure called a dendrogram, which illustrates the arrangement of the clusters produced by the algorithm.
2. K-means Clustering: It is a partition-based method that divides the gene expression dataset into k clusters, where k is a user-defined parameter. 
3. Self-Organizing Maps: They are a type of artificial neural network used for clustering and visualizing high-dimensional gene expression data. 

TOPICS COVERED

• Introduction to Gene Expression Analysis,
• Clustering Analysis,
• Installing Required Python Packages
• Gathering & understanding the data,
• Preprocessing the Data,
• K-Mean Clustering,
• DBSCAN Clustering
• Pathway Enrichment Analysis
• Generating the Plots

TAKEAWAY FROM THE MASTERCLASS

• Introductory Documentation: An introductory theory document to help you better understand the subject will also be provided.
  
• Trainers Slide Deck: After the completion of the session complete access to the trainers slide deck will also be provided
• Access to Trainers Repo: We will also be providing a complete access to trainers repository so that you can use it as reference later
• 8+ Hours of Live Training: During the course of 3 days we will be have live8+ hourse of training sessions with the participants.
• Guided Hands-On Exercises: Hands-On exercises are a must to better learn any technology and be able to reproduce it later.
• Participation Certificate: A Participations certificate is a must after successfully completing the training as a sign of accomplishment.

EXPECTED OUTCOMES OF CLUSTERING ANALYSIS USING GENE EXPRESSION DATASET

1. Identification of Co-Expressed Genes: Clustering can group genes that have similar expression patterns across different conditions or time points.
2. Discovery of Gene Modules: By clustering, you can identify gene modules or groups of genes that function together or are co-expressed.
3. Phenotypic Association: Clustering can reveal how gene expression patterns correlate with different phenotypes or disease states. 
4. Biological Insights and Hypothesis Generation: Clustering can uncover patterns or relationships that were not previously apparent, leading to new biological hypotheses.

TERMS & CONDITIONS

1. All fee paid is not refundable so please read all the terms & conditions before making any payments. If you still have any doubts please contact us and confirm and then only make the payment.
2. Participants need to bring their registration tickets along with a valid Institutional ID, then only they will be allowed to attend the session. Please reach out to our team in case of any exceptions.
3. Please fill all your details in the form correctly as those details will be used in your certificate as well.
4. Participants need to bring their own computer (laptop) system for the program.
5. The software tools and other required software tools will be provided from our side for the purpose of this program.
6. Participants need to reach the venue and report 30 minutes prior to the start of the sessions.
7. Participants need to wear masks all the time inside the premises and abide by the other rules at the premises.
8. Participants need to attend all the sessions in order to be eligible for getting the certificate.
9. Welcome email will be sent to all the participants with all the details related to the program. Please check your Inbox/Spam folder for the email.
10. All the details of the software installations and how to prepare your system for the Program will be shared with all the participants in the Welcome Email itself.