75N95024Q00158

Bulk, Single cell RNA-Seq, and Spatial Transcriptomics Bioinformatic Analysis – Advances in single cell technologies have led to rapid generation of large biological data sets which require informatics approaches to analyze. Single cell resolution of -omics data sets holds tremendous promise for advancing swaths of biology but to fully utilize the data sets, marriage of biological and computational knowledge is required. This kind of transdisciplinary work requires a firm partnership with constant interaction.

These services will be utilized by the SCTL to obtain expertise in explaining results and translating biological questions into computational pipelines with dedication and involvement in each project to obtain top tier analysis. The data obtained from this study will be used for publication

Single cell RNA sequencing analysis:

Experimental design for single cell RNA sequencing including technology selection, hashing, power analysis, and coordiation with local facilities for library construction and sequencing parameters. Alignment including custom genome annotations and other preprocessing necessary for 10X, BioRad, Parse, and homebrew techniques like SmartSeq2, DropSeq, inDrop, and SeqWell. Sample level quality control including examination of sequencing quality and visualization and filtering of UMI count, feature count, and mitochondrial gene content. Batch quality control and batch effect correction if applicable. Clustering with visualization of marker genes and differentially expressed genes for cluster identification. Differential expression and gene set analyses for all comparison groups of interest. RNA velocity and pseudotime analyses including visualization of velocities, construction of pseudotime trajectories, and identification of genes driving differentiation by trajectory. Customized intercellular signaling analysis linking transcriptional changes with putative signaling pathways. Data set specific analyses such as copy-number variation, transfer learning, and gene regulatory network analysis. Generation of files and instructions for interactive data viewing with the CellXGene platform.

Spatial transcriptomics analysis:

All services available for single cell RNA sequencing as presented above. Region of interest selection and downstream analyses (these can include ANY services available for single cell RNA sequencing as well). Label transfer to identify spots enriched for specific clusters identified from other single cell RNA sequencing data sets.

Bulk RNA sequencing analysis:

Power calculations, experimental planning, and coordination with your local core facility or sequencing service provider for improved turn-around times. Quality control and visualization of raw data. Alignment including custom genome annotations where necessary. Sample and batch level quality control including visualization and filtration on number of reads, proportion of reads aligning to the transcriptome, and correlation and PCA based outlier identification. Differential expression and gene set analyses accounting for complex experimental design, clinical data, and/or batch effect issues if present. Cell type deconvolution to estimate cellular composition of samples. Data set specific analyses such as copy number variation, TCR repertoire estimation, repeat element expression, and others.

Intercellular signaling analyses:

Customized, in-house intercellular signaling package based on Domino, a previously published software package developed by our founder and the only scRNAseq intercellular signaling packages capable of identifying ligand-receptor pairs and their transcription factor targets. Package applicable to single cell RNA sequencing, spatial transcriptomics, and high-sample bulk RNA sequencing data sets. Global inter-group signaling overview quantifying all estimated intercellular signaling between groups. Visualization of top ligand-receptor-transcription factor pathways between each specific pair of groups. Activation and expression of each ligand, receptor, and transcription factor.