Users can perform pathway analysis on the list of differentially expressed genes using external services - pantherdb or enrichr. Differential expression results can be filtered further, for example, by selecting only upregulated genes. Users can calculate differential expression between cell sets within a sample/group or compare a cell set between samples and groups. Automatic cluster annotation is available for Human and Mouse species. Standard analysis actions such as marker heatmap and UMAP are pre-loaded. It's easy to rename clusters or recolor by sample, metadata, or gene. Custom cell sets can be created using selection tools or based on the expression of one or more genes. Cellenics® has a wide variety of data exploration features. All quality control plots have customizable options for dimensions, title, axis, and font.ĭata processing steps have options for suggested automatic settings and manual override.ĭata exploration. UMAP and t-SNE projections are available for embedding with Louvain as a default clustering method. Additionally, any of the filtering steps can also be disabled if needed.Ĭellenics® supports fast MNN, Harmony, Seurat v4 for data integration, log-transformation for data normalization, and user control over dimensionality reduction. Data are filtered per sample basis with a plot for each sample within each filter. There is a classifier, cell size distribution, mitochondrial content, doublet, and a number of genes versus UMIs filter for step-by-step filtering of empty droplets, dead cells, poor quality cells and doublets. Cellenics® offers an in-depth data processing and quality control workflow where the data are filtered and integrated. This is because the species is defined at the pre-processing stage when the reads are aligned to a specific genome, which is upstream of data upload to Cellenics®.ĭata processing. The platform is essentially species agnostic for all functions except pathway analysis. Cellenics® currently supports no multi-omics technologies. īiomage offers additional bioinformatics support to import other data types for a small fee. Users can also import BD Rhapsody data - expression_data.st or expression_data.st.gz files. This common data type is generated after processing FASTQ files with 10x Genomics’ Cell Ranger. Users can import raw count matrices into Cellenics® in the shape of three files per sample: barcodes.tsv, features.tsv and matrix.mtx. Cellenics® also has a user-friendly graphical interface divided into four components – data management, data processing, data exploration, and plots and tables.ĭata import. So, the user doesn't need a powerful workstation for single-cell data analysis and storage. Being in the cloud means users can analyze their dataset from anywhere in the world at any time. Cellenics® was first released in August 2021.Ĭellenics® is a cloud-based single-cell RNA-seq analysis software that allows you to explore and analyze your dataset without prior programming knowledge. Biomage is an open-source software company that provides services for the design and development of Cellenics® and currently provides services focused on the deployment, training, and user support of Cellenics®. Peter Kharchenko and the administrative supervision of the Department of Biomedical Informatics at Harvard Medical School. You can find a detailed list of features in the overview table below.Ĭellenics® is an open-source scRNA-seq analysis software developed by © 2020-2022 President and Fellows of Harvard College under the scientific supervision of Prof. The most expensive software doesn't necessarily have the most features. Users should not overlook the many robust free solutions available. Some scRNA-seq data analysis tools are free, while others charge licensing fees. You should also consider price and licensing. What input formats and single-cell technologies are accepted by the software? How in-depth cell filtering and data exploration do you want to perform? Do you need single-cell analysis software with automatic cell type prediction? The next things to think about are your specific requirements for data analysis. You should evaluate the type of application your workstation can handle and the level of intuitiveness of the platform. But how do you know which one is a good fit for you? We look at the common and unique features across the most popular scRNA-seq analysis tools currently out there. Once you have your scRNA-seq data, you can gain insights from it using a single-cell analysis software.
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