Single cell RNA-sequencing (scRNA)

Single cell RNA-sequencing (scRNA-seq) is revolutionizing our comprehension of essential biological processes like cell-related pathologies, immunity, and development. Advanced technology enables the high-resolution definition of the overall gene expression profiles of single cells, facilitating a profound characterization of heterogeneous cell populations, such as developing organs or tumors. In contrast to standard bulk RNA-seq techniques that assume all cells in a tissue function similarly, scRNA-seq provides exceptional specificity on distinct cells in a sample, allowing a thorough characterization of established cell types and unveiling new cell populations.

We provide a full range of scRNA data analysis services tailored to your specific goals and requirements.

Preprocessing steps:

  • Quality control: assessing the quality of raw sequencing data and filtering out low-quality reads.
  • Alignment: mapping the reads to a reference genome or transcriptome.
  • Gene expression quantification: estimating gene expression levels for each cell using transcript counts.
  • Ribosomal footprint
  • Normalization: adjusting for technical variability between cells to enable meaningful comparisons.

Possible downstream tasks:

  • Clustering: grouping cells based on similarity in gene expression patterns to identify distinct cell populations.
  • Differential gene expression analysis: identifying genes that are differentially expressed between cell types or conditions.
  • Trajectory analysis: inferring the developmental trajectory of cells to understand how they differentiate or transition between cell states.
  • Cell type annotation: assigning cell types to clusters based on known marker genes or external datasets.
  • Functional analysis: identifying enriched biological pathways or gene ontology terms to gain insights into the biological processes underlying gene expression changes.
  • Cell-cell interaction analysis: identifying ligand-receptor pairs between different cell types to understand cellular communication.
  • Pseudotime analysis: ordering cells along a developmental trajectory without prior knowledge of the trajectory.
  • Gene regulatory network inference: inferring regulatory relationships between genes to understand how they control cellular processes.

The number of downstream analyses that become available is constantly expanding and Karda Genomics supports all current approaches.

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