What are the limitations of RNA-seq?
Limitations of RNA-seq Lack of standardization between sequencing platforms and read depth, equivalent to the percentage of total transcripts sequenced, can compromise reproducibility. Although RNA-seq has become increasingly affordable, its cost remains prohibitive for many laboratories.
Is RNA sequencing difficult?
Read Alignment Mapping RNA-Seq reads to the genome is considerably more challenging than mapping DNA sequencing reads because many reads map across splice junctions. In fact, conventional read mapping algorithms, such as Bowtie (Langmead et al.
What is drop seq?
Drop-Seq is a low-cost, high-throughput platform to profile thousands of cells by encapsualting them into individual droplets. Uniquely barcoded mRNA capture microparticles and cells are coconfined through a microfluidic device within the droplets where they undergo cell lysis and RNA hybridiztion.
How is scRNA Seq data processed in Seurat?
The steps below encompass the standard pre-processing workflow for scRNA-seq data in Seurat. These represent the creation of a Seurat object, the selection and filtration of cells based on QC metrics, data normalization and scaling, and the detection of highly variable genes.
What are the features of Seurat for Drop seq?
All features in Seurat have been configured to work with sparse matrices which results in significant memory and speed savings for Drop-seq/inDrop/10x data. # Initialize the Seurat object with the raw (non-normalized data). Keep all # genes expressed in >= 3 cells (~0.1% of the data).
How to perform cluster specific pseudo bulk analysis?
GitHub – vertesy/pseudoBulk: Cluster-specific pseudo-bulk analysis of 10X single-cell RNA-seq data by connecting Seurat to the VBC RNA-seq pipeline. Use Git or checkout with SVN using the web URL. Work fast with our official CLI.
What can be regressed out of a Seurat analysis?
This could include not only technical noise, but batch effects, or even biological sources of variation (cell cycle stage). As suggested in Buettner et al, NBT, 2015, regressing these signals out of the analysis can improve downstream dimensionality reduction and clustering.