Single cell RNA-Seq

We offer bioinformatics analysis of NGS data for single cells.

Home " Single cell RNA-Seq

Study of gene expression at the cellular level

Single cell RNA-Seq analysis allows the transcriptome of each cell to be analyzed individually and can reveal specific biological processes and molecular mechanisms that might not be detected by RNA sequencing techniques at the population or tissue level.

Our bioinformatics analysis includes:

Sequencing and mapping quality control.
Alignment of reads against reference genome.
Quantification of gene expression at the single cell level.
Reduction of data dimensionality.
Clustering or division of cells into different groups and subgroups.
Differential gene expression analysis.
Study of the evolutionary pattern or cell maturation (Velocity or Trajectory Analysis).
Enrichment study of gene ontologies and pathways.

Documentation

Accepted files

We can carry out bioinformatics analysis from:

Illumina: FASTQ
MGI: FASTQ

You can send us your raw files on a hard disk or share them via FTP. If you have any questions please contact us by sending an email to info@dreamgenics.com or calling 985 088 180 and we will help you.

Results in Genome One Reports

The results of all all analyses result in interactive graphs that can be explored on our which can be explored on our Genome One Reports platform and downloaded as PDFs for use in scientific publications.

Principal Component Analysis

Interactive graph of the variabilities explained by each of the principal components calculated (n=50).

Cell Clustering (2D & 3D)

tSNE/UMAP graph with cells grouped into different populations and annotated according to the group to which they belong or by cell type.

Cluster Evaluation

Graph studying how well or poorly the cell groups are separated in the tSNE/UMAP graph.

Differentially Expressed Genes

Interactive graph representing, for each population or group of cells, the differentially expressed TOP2 genes.

TOP Differentially Expressed Genes

Interactive Violin Plot and Feature Plot that reflect in which cluster or clusters a certain gene is being expressed in the majority.

RNA Velocity

Graph showing the pattern of cell evolution as a function of latency time generated by the Velocity algorithm.

Trajectory Analysis

Interactive graph showing Cell Clustering clustering but with trajectory lines, so that it marks a temporal evolution based on changes in gene expression along the cell populations following that trajectory.