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Single-cell Omics

Single-cell Omics

Transcriptomics, Immune Repertoire (TCR & BCR), Cell surface proteins and Epigenetics

Single-cell Transcriptomics

Single-cell RNAseq

Pre Processing & Standard Analysis

Stay up to date with basic worflows and methods for single cell RNAseq, is time consuming.

We provide the latest, most robust, and actionable pipelines for QC, filtration, normalization or dimension reduction (PCA, t-SNE, UMAP..)

Pre Processing

QC, Trimming if necessary, trimmed data QC. Alignment of data scRNA-Seq with the latest reference genome, Count data QC.

Deliverables

Fastq – QC reports – Filtered data.

Standard Analysis

Normalization, Gene Selection, Dimension reduction. (Advance calculation of the PCA, t-SNE and UMAP axes)

Deliverables

Bioinformatics object (e.g. Seurat, SingleCellExperiment, …) including counting data, normalized data, and reduction dimensions.

Advanced Analysis

Reach next level of analysis, with our advanced data science protocols. We run cell analysis, differential tests, ORA, cluster annotation with you and full graphical representation for your exploratory analysis.

Advanced Analysis

Sample Integration Sample batch correction,
Cell analysis : Identification of cell clusters by K-nearest neighbor (K-NN) graph and by the Leuven community detection method,
Identification of markers associated with each cluster: Differential tests between each cluster versus all cells,
ORA (Over Representation Analysis): Functional analysis of differentially expressed genes (e.g. representation of genes in databases such as KEGG (pathways) and/or in GO ontologies),
Cluster Annotation: Predict the functions of each cluster based on predefined pathways, oncologies, and molecular signatures,
Graphical representation: Graph of results obtained (volcano, heatmap, UMAP, gene networks).

Deliverables

Bioinformatics object, complemented by the analyses listed above and their relevant graphical representations.

Check our advanced report

Single-cell Immune Repertoire

Single-cell REPseq

Pre Processing & Standard Analysis

As per bulk immune repertoire analysis, at Parean, we master TCR & BCR at single cell level.

Use our pipeline for descriptive diversity profils, spectra typing analysis, repertoire sharing, and much more..

Pre Processing

QC, Trimming if necessary,
Alignment with the latest reference genome and/or identification of chain pairing clones,
Somatic hypermutations (SHMs) identification (Only for BCR),
QC & elimination of ambiguous clones.

Deliverables

FastQ – QC reports – Filtered data in MiAIRR format.

Standard Analysis

Descriptive gene usage (V, J and V-J),
Descriptive diversity profiles (Richness, Shannon entropy, Mean clonal frequency),
Spectratyping analysis (CDR3 length distributions),
Repertoire sharing analysis (clonal overlap).

Deliverables

Bioinformatics object (e.g. Seurat, SingleCellExperiment, …), including counting, normalized, integrated sample data, and reduction dimensions, with graphs & analysis and meeting with our teams.

Advanced Analysis if combined with scRNAseq

We developed since several years tailored and cutting edge tools for immune repertoire analysis.

Combined with scRNAseq, we give your access to analysis such as clonal architecture, repertoire classification, clonal specificity inference (with internal data base).

Advanced Analysis if combined with scRNAseq

Analysis of the differential expression (between clusters) of V, J and VJ usages,
Spectratyping perturbation analysis,
Repertoire sharing analysis (Morisita-horn, Jaccard, Jensen–Shannon divergence index),
Identification of clonotypes based on phenotypes,
Clonal architecture (Networks, K-mers, Logo Representation),
Physicochemical properties,
Immunoglobulin Phylogenetic analysis (only BCR),
Repertoire classification,
Generation probability inference (chain separately),
Clonal specificity inference (Using manually curated database).

Deliverables

Bioinformatics object, complemented by the analyses listed above and their relevant graphical representations.

Single-cell CITEseq

scRNAseq + cell surface proteins

Pre Processing & Standard Analysis

Protocol combining transcriptomics and proteins informations, the first steps of analysis are not an easy tasks.

Our regular pipelines offer QC, alignement, one selection and dimension reduction, for both protein data & transcriptomics

Pre Processing

QC, Trimming if necessary , trimmed data QC,
Alignment and/or identification of chain pairing clones,
Count data QC,
Protein Data alignment and Mapping,
Cell Barcode Processing.

Deliverables

FastQ – QC reports – Filtered data.

Standard Analysis

Normalization, Gene Selection, Dimension reduction. (Advance calculation of the PCA, t-SNE and UMAP axes.)

Deliverables

Bioinformatics object (e.g. Seurat, SingleCellExperiment, …) including counting data, normalized data, integrated sample data, and reduction dimensions.

Advanced Analysis

With advanced analysis, get more of your data and your samples.

Use our pipelines for cell analysis, markers associated with each cluster, ORA, correlation analysis between RNA & proteins…

Sample Batch Correction & Integration

Cell analysis

Identification of cell clusters by K-nearest neighbor (K-NN) graph and by the Leuven community detection method.

Identification of markers associated with each cluster

Differential tests between each cluster versus all cells.

ORA (over representation analysis)

Functional analysis of differentially expressed genes (e.g. representation of genes in databases such as KEGG (pathways) and/or in GO (ontologies).

Cluster Annotation

Predict the functions of each cluster based on predefined pathways, ontologies, and molecular signatures.

Correlation Analysis

Correlation between RNA and protein expression levels to identify genes that are translated into proteins and examine post-transcriptional regulation.

Graphical representation

volcano, heatmap, UMAP, gene networks,…

Deliverables

Previous bioinformatics object (e.g. Seurat, SingleCellExperiment, …), completed with the analyses listed above, differential and functional analysis tables, and figures.

Single-cell ATACseq

scATACseq

Pre Processing & Standard Analysis

Powerful while complex, scATACseq data sets are difficult to analyse, even on pre processing or regular analysis.

Use our pipelines for QC, peak calling, normalization, handle multi mapping & PCR ..

Pre Processing & Standard Analysis

Quality Control (QC), Read Trimming and Filtering Alignment and Mapping.
Handle the multimapping and PCR duplicates to ensure accurate mapping.
Peak Calling: Identify regions of open chromatin (peaks) using peak calling algorithms such as MACS2, HMMRATAC, or MARGI, and significance of peaks based on statistical measures like false discovery rate (FDR).
Cell Barcode Processing & Normalization.

Advanced Analysis

Various complex methods exist to analyse in depth scATACseq datasets. We have selected, and update the most relevant ones.

Our advanced pipelines enables clustering, differential accessibility analysis, Gene regulatory network analysis (ex SCENIC) or functional enrichment analysis.

Advanced Analysis

Dimensionality Reduction

principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) to visualize and explore the data.

Clustering

chromatin accessibility profiles using methods such as k-means clustering, hierarchical clustering, or density-based clustering algorithms.

Cell Type Identification

Known marker genes or reference datasets to identify different cell types (CellAssign, SingleR, or SCENIC for cell type annotation).

Differential Accessibility Analysis

Differentially accessible regions (DARs) or peaks between different cell types or conditions using statistical tests like DESeq2, edgeR, or MAST.

Gene Regulatory Network Analysis

Infer gene regulatory networks (GRNs) from single-cell ATAC-seq data to elucidate transcriptional regulatory relationships using tools like SCENIC or Cicero.