In this video, we will continue our discussion on how single-cell RNA sequencing (scRNA-seq) analysis helps in identifying potential biomarkers for the diagnosis and prognosis of non-small cell lung cancer (NSCLC).
Single-cell RNA-sequencing scRNA-seq analysis can identify signaling pathways that are specifically dysregulated in NSCLC, thereby providing potential biomarkers for diagnosis and prognosis.
scRNA-seq analysis can be also used to identify genes and pathways that are associated with treatment response in NSCLC patients, providing potential biomarkers for predicting patient response to treatment.
Overall, scRNA-seq analysis provides a powerful tool for identifying potential biomarkers for the diagnosis and prognosis of NSCLC. The ability to analyze individual cells at the molecular level provides insights into the heterogeneity of NSCLC tumors, and can potentially lead to the development of more personalized treatment options for NSCLC patients.
BioCode is offering a comprehensive single-cell RNA-seq data analysis course designed for beginners and advanced researchers in life sciences who are interested in exploring the exciting field of single-cell genomics. This course is focused on the analysis of scRNA-seq data using command-line tools and Python packages, including Scanpy. No prior Python programming or Linux knowledge is required.
This course includes:
-In-depth Introduction to scRNA-seq
-Complete end-to-end scRNA-seq analysis
-Cellular and Tumor Heterogeneity
-Bulk vs. Single Cell RNA-Seq Analysis
-Single-Cell RNA-Seq Technologies
-10x Genomics Complete Pipeline
-From Raw Datasets to Cell Sub-populations
-Normalization, Quality Control, and Dimension Reduction
-Cell Clustering and Cell Annotation
-Differential Gene Expression Analysis
-Downstream Analysis
-ScanPy Package Complete Guidelines
-Python-based scRNA-Seq Analysis
To learn more about Single-Cell Genomics, DM us we can help you get started. BioCode provides an interactive platform to learn biological programming in Python & R, bioinformatics techniques, tools, databases, and biological data analysis in a cooperative manner covering both theoretical and practical aspects of computational biology topics. BioCode provides you with videos regarding every topic along with exercises. BioCode allows you to learn at your pace according to your schedule. Along with every video BioCode provides you with transcriptions and PowerPoint presentations regarding that topic. If you have any queries during the lectures, there’s a dedicated section available for you to ask questions from your tutor.
Join BioCode’s Hands-on: Single-Cell RNA- Sequencing Data Analysis Using Python Course: [ Ссылка ]
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