Scarfweb
We support users getting started with Scarfweb from Nygen Analytics. We help preparing data for Scarfweb, help users get started, and we offer Scarfweb training.
Cellhub
We support users in getting started using the curated single-cell database Cellhub, hosted by Nygen Analytics. We help with curating data and compare it with deposited data sets.
Quality Control and Preprocessing
We at MultiD provide comprehensive quality control and preprocessing services to ensure the highest quality of your single-cell and spatial transcriptomics data. Our rigorous quality control measures evaluate data integrity, identify potential batch effects, and detect outliers. We perform read alignment, gene quantification, normalization, and batch effect correction using advanced tools and algorithms, ensuring your data is reliable and ready for downstream analysis.
Exploratory Data Analysis
We conduct exploratory data analysis to uncover your single-cell data’s underlying structure and patterns. Using dimensionality reduction techniques such as t-SNE, UMAP, and PCA, we visualize and cluster cells based on their gene expression profiles. Our interactive visualization tools, including scatter plots and heatmaps, enable you to explore and interact with your data, revealing distinct cell clusters and underlying biological insights.
Cell Type Identification
We provide cell type identification services to accurately annotate cell types within heterogeneous populations. By leveraging reference-based approaches, utilizing established cell type marker genes, publicly available databases like CellMarker, and automated methods like Cell Typist, we map your cells’ expression profiles to known cell types, giving you a comprehensive understanding of your sample’s cellular composition.
RNA Velocity Analysis
We offer RNA velocity analysis to infer the future states of individual cells based on the current snapshot of RNA expression. By analyzing the spliced and unspliced mRNA ratios, we predict cell differentiation pathways and dynamic changes in gene expression, providing insights into cellular development and transitions.
Enrichment Analysis
We perform enrichment analysis to identify significant biological pathways and gene sets associated with specific cell populations or conditions. By utilizing tools like Gene Set Enrichment Analysis (GSEA) and pathway analysis, we uncover the functional implications of differentially expressed genes, helping you understand the biological processes driving your observations.
Cell-Cell Interaction Analysis
We analyze cell-cell interactions to unravel the communication networks within your tissue. Using computational approaches like CellPhoneDB and ligand-receptor correlation analysis, we identify potential signaling interactions between different cell types. This analysis sheds light on the molecular mechanisms driving cellular responses and coordination within the tissue.
Trajectory Analysis
We provide trajectory analysis to infer developmental paths and transitions between different cell states. By employing algorithms such as PAGA and scFates, we reconstruct cellular trajectories and identify critical branching points. This analysis offers insights into the regulatory networks and molecular events guiding cell fate decisions.
Integrative Single-cell Analysis
We offer integrative single-cell analysis to compare and integrate scRNA-seq data from multiple samples or conditions. Our methods, including the scArches pipeline, allow for seamless data integration, enabling you to identify shared and distinct cell types, characterize condition-specific gene expression patterns, and uncover molecular mechanisms underlying phenotypic differences.
Ligand-Receptor Analysis
We provide ligand-receptor analysis to elucidate the intercellular communication within your scRNA-seq data. We infer potential signaling interactions between cells by identifying ligand-receptor pairs and integrating gene expression information with known databases. We offer insights into the communication pathways that regulate tissue homeostasis and cellular processes.
Spatial Transcriptomic Analysis
We offer spatial transcriptomic analysis to combine spatial information with gene expression data, allowing you to visualize and analyze gene expression patterns within the context of tissue architecture. By integrating spatial information obtained through techniques like spatial transcriptomics or in situ sequencing, we provide a deeper understanding of how gene expression varies across different tissue regions or cell types, highlighting spatially regulated gene expression and cell-cell interactions within the tissue microenvironment.
Differential Gene Expression Analysis
We perform differential gene expression analysis to identify uniquely upregulated or downregulated genes in specific cell clusters or conditions. This analysis helps pinpoint key regulatory genes and pathways involved in cellular functions, disease mechanisms, and treatment responses.
Functional Annotation and Pathway Analysis
We provide functional annotation and pathway analysis to link differentially expressed genes to biological functions and pathways. Utilizing databases such as KEGG, Reactome, and GO, we interpret your data in the context of known biological pathways, enhancing your understanding of the underlying molecular mechanisms.
Drug Target Analysis
We offer drug target analysis to identify potential therapeutic targets using databases such as DrugBank and DGiDb. By employing methods like Drug2cell, we correlate drug-target interactions with specific gene expression profiles, providing insights into potential drug efficacy and mechanisms of action. This service helps identify novel drug targets and understand existing drugs’ impact on cellular pathways.
Spatial Multiomics
Our services includes:
• High-Resolution Imaging: Utilize state-of-the-art imaging technologies to map molecular data onto tissue sections, preserving spatial relationships and providing detailed visualizations.
• Integrated Omics Analysis: Combine data from multiple omics fields to gain a holistic understanding of molecular interactions within their spatial context.
• Custom Solutions: Tailor our analysis services to meet your specific research needs, whether it's cancer research, neuroscience, developmental biology, or other fields.
Multiomics
Multiomics is an integrative approach that combines data from multiple "omics" disciplines, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics, to provide a comprehensive understanding of biological systems. By analysing these diverse datasets together, researchers can gain deeper insights into the complex interactions and regulatory mechanisms that govern cellular functions and disease processes.
Out-of-Sample Prediction
We provide out-of-sample prediction services using generative models such as CPA (Compositional Perturbation Autoencoder), Gears, and others. These models allow us to predict cellular responses to unseen perturbations or conditions, offering valuable insights into how cells might react to new treatments or environmental changes. This predictive capability enhances your understanding of cellular behavior and supports the development of targeted therapies.
By leveraging our expertise and advanced computational tools, MultiD delivers a comprehensive suite of services designed to maximize the value of your single-cell and spatial transcriptomics data, driving insights into cellular heterogeneity, dynamics, interactions, and therapeutic potentials.
