Single Cell RNASeq (scRNASeq)

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique that enables the profiling of gene expression in individual cells. Unlike traditional bulk RNA sequencing, which provides an average gene expression signal across a population of cells, scRNA-seq captures the transcriptomic landscape at single-cell resolution. This allows researchers to investigate cellular heterogeneity, identify distinct cell types and states, and uncover rare subpopulations that might be masked in bulk analyses.

By examining thousands to millions of cells independently, scRNA-seq has become an indispensable tool in fields such as developmental biology, immunology, cancer research, and neuroscience. It enables a deeper understanding of complex biological systems by revealing how individual cells function and interact within tissues or in response to disease and treatment.

Single-cell RNA sequencing (scRNA-seq) is a powerful technique used to measure gene expression at the resolution of individual cells.

You’ll learn how to design and conduct single-cell RNA sequencing experiments, prepare samples, and process raw data with quality control. You’ll master visualization and clustering techniques to identify different cell types and states. You’ll perform differential gene expression and pathway analysis to uncover biological insights. The course will also cover advanced topics like trajectory analysis and batch correction. Throughout, you’ll gain practical experience using tools like Seurat and Scanpy, preparing you to analyze complex single-cell datasets confidently.

👉 Enroll now and take the first step toward a future in data-driven biology and beyond.

## 10% Discount if you register before 15th October, 2025. Hurry up!!

RNASeq [R]
RNASeq [D]
scRNA-Seq
lncRNA-Seq
miRNA-Seq
PSI-Seq
ChipSeq
ATAC-Seq
scATA-Seq
Spatial Transcriptome
DNASeq
MethylSeq
Whole Genome de novo Assembly
Targeted metagenomics
Whole Genome Metagenomics
Metatranscriptomics
RAD-Seq
SNAP-ChIP

7 Days Training
15 Days Training

Course Information
Course	Single Cell RNASeq - Single Cell RNA expression analysis
Duration	online 7 Days Training [ 2 Hours Daily [ Monday To Friday ] ]
Slots	Our working Time is 9:00 AM to 6:00 PM Indian Time Available slots - 9:00 AM to 11:00 AM / 11:00 AM to 1:00 PM / 2:00 PM to 4:00 PM / 4:00 PM to 6:00 PM For training slots after 6 PM or before 9 am as well as weekends training kindly mention during registration accordingly it will be scheduled.
Mode	👉 For online training candidate have to install ZOOM (with remote control on candidate system which makes 100% interactive) 👉 Run time video recording candidate can make as well as pdf manual will be provided for future reference. 👉 All our training is 100% practical and 100% industrial and 100% interactive which provides same as offline learning. 👉 For doubt clear there will be extra support will be provided based on the requirement. 👉 Certificate will be provided
Sequencing Platform	genome10X.
Raw data	Candidate can include maximum 4 datasets of their own during training. Publication standards figures and tables will be generated.
Training Fees
Module-NGS Single Cell RNAseq Data Analysis	📘 Introduction to Single‑Cell Technologies - Overview of single‑cell RNA‑sequencing (scRNA‑seq) - Applications in biology and medicine - Technologies: 10x Genomics - Experimental workflow 📘 Linux Basics & NGS Environment Setup - Linux installation (Ubuntu/WSL) - Basic Linux commands - Installing tools with conda, apt, or docker - Tool installation: Cell Ranger, STAR, R, RStudio, and Seurat 📘 Introduction to R & Bioconductor - Installing packages via CRAN and Bioconductor - R data types and standardized data containers - Data manipulation with tidyverse and Bioconductor classes 📘 Data Retrieval & Formats - Accessing NCBI SRA, ENA, Genome10x - Download using prefetch and fastq-dump - Common file formats: FASTQ, BCL, H5 📘 Mapping & Matrix Generation - Mapping sequencing data with Cell Ranger - Generating count matrices for downstream analysis 📘 scRNA‑Seq Data Analysis Quality Control - UMI counts per cell - Genes detected per cell - Gene‑to‑UMI ratios - Plotting: bar plots, scatter, box, density Filtering - Remove low‑quality cells (e.g., low genes, high mitochondrial content) - Filter out low‑abundance genes Normalization - LogNormalize or SCTransform - Regress out confounders (e.g., cell cycle, batch effects) 📘 Dimensionality Reduction & Clustering - PCA (Principal Component Analysis) - UMAP for cluster visualization - Clustering algorithms: Louvain & Leiden - Cluster annotation using known marker genes 📘 Differential Gene Expression (DEG) - Identify marker genes between clusters or conditions - Use FindMarkers() in Seurat - Visualizations: heatmaps, volcano plots 📘 Functional Enrichment - Gene Ontology (GO) analysis with clusterProfiler, enrichR - Pathway analysis (KEGG, Reactome) - Gene interaction networks using STRING & Cytoscape 📘 Visualizations in R - UMAP plots with cluster labels - Feature plots for individual gene expression - Violin & dot plots for marker genes - Heatmaps of cluster‑wise expression patterns
Preparation	- scRNA-Seq : https://en.wikipedia.org/wiki/Single-cell_sequencing - NCBI : https://pmc.ncbi.nlm.nih.gov/articles/PMC9315519/
Instructor	Industry Experienced
Target Audiance	This course is designed for graduate students, postdoctoral researchers, and professionals working in the fields of conservation biology, evolutionary genomics, and population genetics or any life sciences who are interested in applying genomic tools to real-world conservation challenges.
Contact	Please write us at info@arraygen.com or call or whatsapp us on mobile +91-9673625446 if you need any clarification or for any custom training based on candidate reference paper or candidate own content/tools.

Course Information
Course	Single Cell RNASeq - Single Cell RNA expression analysis
Duration	online 15 Days Training [ 2 Hours Daily [ Monday To Friday ] ]
Slots	Our working Time is 9:00 AM to 6:00 PM Indian Time Available slots - 9:00 AM to 11:00 AM / 11:00 AM to 1:00 PM / 2:00 PM to 4:00 PM / 4:00 PM to 6:00 PM For training slots after 6 PM or before 9 am as well as weekends training kindly mention during registration accordingly it will be scheduled.
Mode	👉 For online training candidate have to install ZOOM (with remote control on candidate system which makes 100% interactive) 👉 Run time video recording candidate can make as well as pdf manual will be provided for future reference. 👉 All our training is 100% practical and 100% industrial and 100% interactive which provides same as offline learning. 👉 For doubt clear there will be extra support will be provided based on the requirement. 👉 Certificate will be provided
Sequencing Platform	genome10X.
Raw data	Candidate can include maximum 4 datasets of their own during training. Publication standards figures and tables will be generated.
Training Fees
Module-I	Advanced Bioinformatics & basic programming
Topics	📘 Introduction to Bioinformatics - Overview of bioinformatics and its applications - Key concepts in computational biology - Role of bioinformatics in genomics, transcriptomics, and proteomics 📘 Understanding NGS and Genomics Bioinformatics - Basics of Next-Generation Sequencing (NGS) - Types of NGS data (RNA-seq, WGS, WES) - Overview of NGS data formats: FASTQ, BAM, VCF - Introduction to pipelines and tools for NGS data analysis 📘 Databases & Data Retrieval (NCBI and UCSC) - Learning how to retrieve biologically correct data - Performing complete batch retrieval (e.g., whole exome) - NCBI: understanding gene-level data retrieval - UCSC: handling large-scale data retrieval - UCSC Genome Browser and Table Browser usage - Batch Coordinate Retrieval and Genomic Data downloads - GFF/GTF gene annotation formats and how to retrieve them - Using BLAT for sequence-based search and alignment 📘 Gene Prediction and Functional Annotation - Gene prediction approaches and tools - Functional annotation using Gene Ontology (GO) - Pathway analysis using KEGG, Reactome - Interpreting gene sets and biological relevance 📘 Standalone/Offline BLAST for Large-Scale Genomic Data - Installing and setting up standalone BLAST - Running local BLAST for batch sequence alignment - Applications in genome-wide homology searches - Custom BLAST databases and performance optimization 📘 PCR Primer Designing and Specificity Check - Designing accurate primers for PCR amplification - Tools: Primer3, NCBI Primer-BLAST - Checking primer specificity using genome-wide BLAST - Avoiding non-target amplification through design best practices 📘 Understanding Python Programming - Introduction to Python for bioinformatics - Scripting basics: variables, loops, functions - Libraries like Biopython, pandas for biological data handling - Automating genomic workflows with Python scripts
AND
Module-II	Next Generation Sequencing (NGS) - Single Cell RNASeq Data Analysis
Topics	📘 Introduction to Single‑Cell Technologies - Overview of single‑cell RNA‑sequencing (scRNA‑seq) - Applications in biology and medicine - Technologies: 10x Genomics - Experimental workflow 📘 Linux Basics & NGS Environment Setup - Linux installation (Ubuntu/WSL) - Basic Linux commands - Installing tools with conda, apt, or docker - Tool installation: Cell Ranger, STAR, R, RStudio, and Seurat 📘 Introduction to R & Bioconductor - Installing packages via CRAN and Bioconductor - R data types and standardized data containers - Data manipulation with tidyverse and Bioconductor classes 📘 Data Retrieval & Formats - Accessing NCBI SRA, ENA, Genome10x - Download using prefetch and fastq-dump - Common file formats: FASTQ, BCL, H5 📘 Mapping & Matrix Generation - Mapping sequencing data with Cell Ranger - Generating count matrices for downstream analysis 📘 scRNA‑Seq Data Analysis Quality Control - UMI counts per cell - Genes detected per cell - Gene‑to‑UMI ratios - Plotting: bar plots, scatter, box, density Filtering - Remove low‑quality cells (e.g., low genes, high mitochondrial content) - Filter out low‑abundance genes Normalization - LogNormalize or SCTransform - Regress out confounders (e.g., cell cycle, batch effects) 📘 Dimensionality Reduction & Clustering - PCA (Principal Component Analysis) - UMAP for cluster visualization - Clustering algorithms: Louvain & Leiden - Cluster annotation using known marker genes 📘 Differential Gene Expression (DEG) - Identify marker genes between clusters or conditions - Use FindMarkers() in Seurat - Visualizations: heatmaps, volcano plots 📘 Functional Enrichment - Gene Ontology (GO) analysis with clusterProfiler, enrichR - Pathway analysis (KEGG, Reactome) - Gene interaction networks using STRING & Cytoscape 📘 Visualizations in R - UMAP plots with cluster labels - Feature plots for individual gene expression - Violin & dot plots for marker genes - Heatmaps of cluster‑wise expression patterns
Preparation	- scRNA-Seq : https://en.wikipedia.org/wiki/Single-cell_sequencing - NCBI : https://pmc.ncbi.nlm.nih.gov/articles/PMC9315519/
Instructor	Industry Experienced
Target Audiance	This course is designed for graduate students, postdoctoral researchers, and professionals working in the fields of conservation biology, evolutionary genomics, and population genetics or any life sciences who are interested in applying genomic tools to real-world conservation challenges.
Contact	Please write us at info@arraygen.com or call or whatsapp us on mobile +91-9673625446 if you need any clarification or for any custom training based on candidate reference paper or candidate own content/tools.