Bulk RNA-Seq Data Analysis in Partek Flow

This training demonstrates how to import raw RNA-Seq data in .fastq format, align it to the reference genome, and generate read counts. It will introduce how to detect differentially expressed genes and interpret the biology with KEGG pathway enrichment analysis using a significant gene list.

Time: 1.5 hours

• Import FASTQ files
• Manage sample attributes
• Perform QA/QC (pre-alignment and post-alignment QC)
• Align reads to a reference genome
• Quantify gene/transcript abundance
• Filter and normalize gene counts
• Detect differential expression
• Identify enriched KEGG pathways and/or GO term
• Visualizations: PCA scatterplot, Dot plot, Volcano plot, Heatmap, chromosome view, etc.

CHIP/ATAC-Seq Data Analysis in Partek Flow

This is a demonstration of ATAC-Seq data analysis starting with the import of raw sequencing .fastq files, followed by alignment to the reference genome, peak detection, and annotation. Comparing peaks between different groups with multiple replicates is introduced. Motif detection is performed on significant regions and interpretation of the biology by conducting KEGG pathway enrichment analysis on genes overlap regions.

Time: 1.5 hours

• Import fastq files
• Attribute management
• QA/QC
• Alignment
• Detect enriched regions with MACS2
• Annotate regions
• Compare regions among different groups
• Search for motifs on significant regions
• KEGG enrichment analysis

Visualization: scatterplot, TSS plot, volcano plot, heatmap etc.

Single Cell RNA-Seq Analysis in Partek Flow

This training demonstrates single cell RNA-Seq data analysis starting with importing a count matrix file, followed by QC and filtering data, and concludes with cluster analysis and dimension reduction techniques to visualize and identify the subtype of cells. Differential expression detection among different subtypes will be performed followed by KEGG pathway enrichment analysis.

This session requires basic knowledge about Partek Flow.

Time: 1.5 hours

• Import count matrix in .h5 (10X Genomics Cell Ranger output)
• Single cell QC and filter
• Normalize UMI counts
• Perform PCA, graph-based cluster, and tSNE/UMAP
• Classify subtypes of cells
• Detect differential expressed genes among different cell types
• KEGG enrichment analysis

Visualizations: scatterplot, dot plot, violin plot, volcano plot, heatmap etc.