Free academic users can access up to 5 public studies in our database, import Seurat or Scanpy objects, and run almost all downstream analyses in the software (excluding sub-clustering and DE analysis)
- Public studies: each free user will have 50 scores for downloading public datasets. Whenever downloading a dataset, your scores will be subtracted according to the scores assigned to each study.
- In-house data: free users cannot import in-house data into BioTuring Browser for analysis. Academic free users, meanwhile, can import Seurat or Scanpy objects.
You can import FASTQ, MTX, TSV, CSV, .H5, .H5AD, and .RDS files to BBrowser. For details about the structure of each file, please refer to our Documentation section 5_Get your data.
It depends on the format and structure of the file. If the file fulfills all the requirements of the software, you can import it to BBrowser.
Otherwise, if the author of the study is willing to share their annotations, the BioTuring team would be happy to consider hosting the data in our platform and will index the data based on our standard process.
First, open BBrowser > Data > Add new study to import all files, select your method for batch correction and name the study, then click Start to run the processing. Please refer to our Documentation section 5_Get your data.
To import sample info/annotations to the data, you can go to
Color By (to the right of the screen)> click on the drop down button > Add annotation from a file. Here you need to import an annotation file where the 1st column contains the barcodes, and other columns contain the annotations. Please refer to our Documentation section 13_Add an annotation.
BBrowser supports exporting multiple graphs: scatter plots, box plots, violin plots, etc. in either SVG or PNG format with a fixed design and layout. You can find the export button at the top right corner of each plot in BBrowser. Please refer to our Documentation section 18_Export Image and data
If you want to customize the color of the graph, go to Settings > Visualization and change the color scale there.
An alternative is to export data of the graph to tsv and reconstruct it by your preferred tools outside BBrowser. BioTuring team also offers a drag-and-drop data visualization tool called BioVinci (www.vinci.bioturing.com). To learn how you can directly export a plot in BBrowser into BioVinci, please refer to our Documentation section 18_Export Image and data
Exporting Seurat is already in our roadmap. We will let you know as soon as it is ready.
To pair clonotype data with expression data, please click on Clonotype (below the screen) and upload the clonotype files. The files should be in TSV/CSV, and should contain the following columns:
- v_gene :V gene
- j_gene :J gene
- cdr3 :CDR3 sequence
- barcode :Barcode of cells
- raw_clonotype_id :Clonotype ID
- full_length :Whether it has a valid V and J annotation
- productive :Whether the transcript translates to a protein with a CDR3 region
Please refer to our Documentation section 17_Study Clonotype.
To compare gene expression across different clusters:
- Choose the annotation with the clusters you are interested in.
- Type in the gene name or Ensembl ID in the gene query box and click Enter to query for the gene expression.
- Click on the arrow at the bottom of the color scale to extend the box and click on the Plot button to generate a box plot of gene expression across different clusters.
Please refer to our Documentation section 8_Query gene or protein expression.
Each enriched biological process will come with an enrichment score (ES), which is based on Gene Set Enrichment Analysis (GSEA) method. GSEA method can identify classes of genes or proteins that are over-represented in a large set of genes or proteins; these classes can be associated with biological functions or disease phenotypes (Aravind Subramanian et al, 2005). The package fgsea was used for gene set enrichment analysis in BBrowser. Please refer to our Documentation section 12_Find marker genes and enrichment analysis.
It depends on the unit of each study. For example if your matrix contains UMI counts, by default BBrowser will show the raw UMI counts when you query gene expression. You can change the expression unit to Log2 (raw value) or Log normalized by going to Settings > Analysis > Gene expression unit. Please refer to our Documentation section 12_Find marker genes and enrichment analysis.
When you query multiple gene’s expressions (eg. 3 genes), for each cell, we take the sum of 3 genes divided by the sum of all genes. You can refer to this publication for details about the method.
We recommend using a computer with 16GB RAM for data having more than 100,000 cells or processed from FASTQ file. However, on a computer with 8GB RAM, you can still open large Seurat objects if they are fully processed with PCA and dimensionality reduction results (tested with 300,000-cell object). If you want to submit count matrices, 8 GB RAM can smoothly process data of 30,000 cells. Please refer to our Documentation section 1.2_System requirements
Unfortunately, BBrowser 2 uses webGL to render graphs, and most analyses require the latest libraries (many of which are only possible to be compiled on a 64 bits system). We don’t think the software can work on Windows 32 bits anytime soon. I hope it won’t be a big problem for you.
If you are using a server with a proxy, the message might come up when you try to login to the software since the proxy connection to BioTuring server cannot be made to verify your credentials. Please click on Proxy settings at the bottom of the login screen and configure your server.
Since we cannot obtain all the parameters of the data processing steps from the authors, for some steps, our default parameters may be different from those of the authors.
We are curating public studies at the pace of 2.5 studies/ week
Yes, you could export the expression matrix from each project and upload both files into BBrowser as mtx format with options for batch effect removal.
At the moment, all datasets on BBrowser are manually curated by the BioTuring Team. We process data using the authors’ pipeline and add the annotations to them.
We will index a public study if it is
1. Highly requested by users
2. A high-impact study. Immunology and Neuroscience datasets will be prioritized