Single Cell RNA

scRNA

scRNA sequencing (scRNA-seq) is a common and powerful technique for profiling the whole transcriptome of a large number of individual cells. The analysis of the huge volumes of data generated by these experiments requires the use of sophisticated statistical and computational tools. In RNA sequencing, RNA is typically extracted from a tissue that contains a variety of cell types. This issue has been solved by using a single cell. Cells would be sorted prior to RNA-seq, and RNAseq would be performed on specific cell types separately. The data will then be analyzed using a variety of cell types.

Three Main Stages for Performing scRNA-Seq Data Analysis

scRNA-Seq is processed for the first part like general steps for any high throughput sequencing data. However, after mapping and read count, the later steps require a mix of existing RNASeq analysis methods and novel methods to address the technical difference of scRNASeq. Finally, the biological interpretation is analyzed with methods specifically developed for scRNASeq. Followed is the workflow we use to perform scRNA-seq analysis.

Regardless of droplet method, the following are important for a good quantification at the cellular level

• Sample Index: to keep track of which sample the read come from
• Cellular barcode: to keep track of which cell the read come from
• Unique molecular identifier (UMI): to find out of which transcript molecule the read comes from
• Sequencing read1: the read1 sequence
• Sequencing read2: the read2 sequence

In RNA sequencing, RNA is typically extracted from a tissue that contains a variety of cell types. This issue has been solved by using a single cell. Cells would be sorted prior to RNA-seq, and RNA-seq would be performed on specific cell types separately. The data will then be analyzed using a variety of cell types. The following is the workflow we use to perform scRNA-seq analysis:

• Step 1 - Quality Control and Data Preprocessing
• Formatting reads and filtering noisy cellular barcodes
• Assigning reads to their cellular barcodes and molecules of origin
• Demultiplexing the samples
• Generate a file of counts for each cellular barcode
• Step 2 - Mapping
• Mapping/pseudo-mapping to transcriptome
• Gene assignment
• Barcode counts
• Step 3 - Post-QC Mapping
• Removal of unwanted cells
• Unique feature counts
• Step 4 - Matrix generation
• Collapsing UMIs and Quantification of Reads
• Find which cDNA was the origin of the tag a UMI was attached to
• Count unique UMIs for (gene, UMI) pairings
• Step 5 - Data Normalization
• Correct for differing droplets sizes
• Impute missing values
• Reduce noise, make it easier to identify the structure of the data
• Step 6 - Feature Selection
• Calculate Cell-to-cell variation in the dataset (i.e., genes that are highly expressed in some cells and lowly expressed in others)
• Identify most highly variable gene
• Step 7 - Dimensionality Reduction
• Perform linear dimensional reduction
• Perform PCA analysis
• Step 8 - cell Clustering
• Gene cluster with their assigned p-values, foldchange ratios, p-value adjustments
• Step 9 - Differential Expression for Expressed Features
• Find markers for differential expression
• Compare distribution of expression levels
• Compare values for each gene
• Comparison of cell from different replicates
• Step 10 - Marker Identification
• Find markers for each cluster
• Find all markers distinguishing one cluster from another
• Find markers for every cluster compared to all remaining cells
• Step 11 - Data visualization
• Plot variable features
• Visualize both cells and features based on their PCA score
• Visualize marker expression based on raw counts or differential expression
• Venn, elbow, and PCA plots
• Step 12 - Functional Annotation
• Gene ontology and KEGG pathways