BioTuring

This support page details everything you need to know to leverage our collaboration with BioTuring, which provides our clients with a free license to the BBrowserX. It is possible to jump to different sections by using the contents menu on the left (desktop) or at the top (smartphone).

This tutorial has been updated on March 31 2023 to apply to BBrowserX.

Introduction video

BBrowserX User Guide

Create and open your BBrowserX account

• Step 1: Create an account on the Single Cell Discoveries server on the BioTuring website: https://bioturingbbx.scdiscoveries.com/login. No download is required.
• Step 2: First, log in with the same email address we use to communicate with your email data person.
• Step 3: On the next page, click create an account. Again, use that same email address.
• Step 4: Activate your new account in the BioTuring activation email.
• Step 5: Now sign in with your new account. You will see the page of Figure 1 (below) where you can click on explore to open the BBrowserX. This will open on a new tab. You are now in your BBrowserX account.

Open your data file

• Step 1: You can upload all your data files to your BBrowserX personal workspace. First-timers will find this workspace to be empty. Click on Add studies. This opens up a menu to select your file type.
• Step 2: By default, Single Cell Discoveries delivers a Seurat object of your single-cell data. This will be attached to the data email we sent. Thus, in the ‘select your file type’–menu, you can click on Seurat object to upload your data.

• Step 3: Click on the rightmost column called Input files to upload a local file.
• Step 4: On the next page, click upload under the tab local computer, and select your file. it’s called “filtered_processed_seurat_object.rds", located in preliminary_analysis/analysis_results/.You will now see the file appear in the input menu, as shown in Figure 3 below.
• Step 5: Deselect Apply log-normalization (we have already applied this to the Seurat file). Study info (on the left) is optional and can be filled in later on.
• Step 6: Click submit. This opens up a submission log pop-up (see Figure 4). Submission may take a few minutes.
• Step 7: After successful submission, click on the study to open your data in BBrowserX. You will now see a t-SNE plot of your data. Cells are, by default, colored by metadata tags, such as sample numbers, which are listed on the right. The labels are editable and you can create new labels for selected cells. Please continue reading underneath the images.

Open the plots from the Single Cell Discoveries pipeline

Important notice: If the BBrowserX does not give the option to skip the BioTuring pipeline (see Figure 3), you need to manually open the dimensionality reduction plots (PCA, t-SNE, and UMAP) from the Single Cell Discoveries data pipeline. Follow these steps to change to the SCD-generated plots.

• Step 1: Click on the tab t-SNE/UMAP near the top of the screen. This opens up a pop-up menu (see Figure 6).
• Step 2: On the right of the pop-up menu, click tsne (small letters) to immediately open the SCD-generated t-SNE plot . You can also click umap (small letters) to open the SCD-generated UMAP plot or click pca (small letters) to open the SCD-generated PCA plot. Learn more about these plots by reading our data analysis support page.
• Learn about the left part of this pop-up menu, creating new dimensionality reduction plots, later on this support page; click here.

Available analyses with the SCD License

There are differences between the different licenses. Please note that you will receive an academic or commercial license based on if you are an academic (universities/institutes) or commercial (biotech/pharma) client of Single Cell Discoveries and that this cannot be changed. Full BBrowserX licenses can be acquired at the BioTuring website.

	SCD Academic License	SCD Commercial License	BBrowserX Advanced License
License duration	Unlimited	4 weeks	Unlimited
Create dimensionality reduction plots	Yes	Yes	Yes
Query gene expression	Yes	Yes	Yes
Plot gene expression (e.g. violin plots, heatmaps, density plots)	Yes	Yes	Yes
Find marker genes	Yes	Yes	Yes
Study cell composition	Yes	Yes	Yes
Cell search	Yes	Yes	Yes
Cell Type Prediction based on user-input knowledge base	Yes	Yes	Yes
Run enrichment analysis	Yes	Yes	Yes
Import Seurat/Scanpy objects	Yes	Yes	Yes
Import FASTQ files, count matrices	No	Yes	Yes
Download BioTuring curated datasets	No	No	Yes
Run differential expression analysis	No	Yes	Yes
Sub-clustering	No	Yes	Yes
Find variable genes along cell trajectories	No	Yes	Yes

Most important BBrowserX functionalities

For comprehensive documentation and tutorials for the BBrowserX, see the BioTuring website. Here, we highlight some of the most useful, easy-to-learn functionalities of the BBrowserX license.

Create new dimensionality reduction plots (t-SNE/UMAP)

BioTuring’s BBrowserX includes the option to rerun dimensionality reduction and create new t-SNE or UMAP plots. You can use this if you have rerun a principle components analysis (PCA) and create new plots, if you want to redo dimensionality reduction with the same parameters, or if you want to change parameters to learn how this impacts your visualization.

Note that both t-SNE and UMAP have a stochastic, i.e. randomization, step. Each new dimensionality reduction plot will thus produce a new visualization even with unchanged parameters.

Follow these steps:

• Step 1: Click on the tab t-SNE/UMAP near the top of the screen. This opens up a pop-up menu (see Figure 7).
• Step 2: Create a new embedding. Do this by selecting an input embedding, method, and perplexity/number of neighbors on the left of the pop-up menu.
  • Input embedding: select pca (small letters) — this is the SCD-generated principle component analysis results. We advise always basing your new t-SNE or UMAP plots on the same PCA results. Learn more about this analysis by reading our data analysis support page. If you have rerun your PCA, you can upload the resulting file into the BBrowserX by clicking upload under the tab Import from file.
  • Method: select either t-SNE or UMAP. Learn about their differences here. You can also select one method first and the other later for comparison.
  • Perplexity: if you selected t-SNE as the method, you can adjust the perplexity parameter. Learn about what this parameter means here.
  • Number of neighbors: if you selected UMAP as the method, you can adjust the number of neighbors parameter. Learn about what this parameter means here.
• Step 3: click run. Afterward, you will find the new plot back in this pop-up menu.

Visualize gene expression

If you want to analyze or visualize the expression of specific genes or gene sets on your data, you can utilize the input genes function. Follow these steps:

• Step 1: Click on the Input genes search bar and type in the names of the genes of interest (e.g., MAFA, MAFB, KRT19, PPY, SST, INS, and GCG) (see Figure 8).
• Step 2: Click on a specific gene (e.g., MAFA). Click on Unit to open a drop-down menu and select the desired unit of visualization:
  • Log normalized (see Figure 8) to get a gradient coloring of the log normalized expression;
  • Smoothed binary to get a binary (yes or no) expression color per cell;
• It’s also possible to select the entire gene panel by clicking on Color by and selecting one of the following options:
  • Gene panel (count) will assign each cell a color for how much of the gene panel is expressed;
  • Gene panel (binary) will assign a color yes/no if the entire gene panel is expressed in each cell;
• Step 3: Optionally save a gene set/gene panel on the server.

• Step 4: Click on the symbols above the input genes search bar to create various plots:
  • Violin Plots (see Figure 9);
  • Bar Chart;
  • Bubble Heatmap;
  • Circos Plot;
  • Intersection Plot;
  • Coexpressed genes;
• Step 5: Select the labels of the groups of cells that you want to compare via the library on the right of the page. Remember that you can add and edit labels in the main analysis dashboard.
• Step 6: Easily switch from plot to plot with the same labels and input genes via the tabs at the top of the page.

Select cells for further analysis

For some analyses, such as sub-clustering, finding marker genes, or cell search, you need to select a subset of cells in the analysis dashboard. Follow these steps to select cells:

• Step 1: Click on the lasso tool (draw selection) on the top left (see Figure 10).
• Step 2: Click and hold to draw a selection around the cells that you want to analyze. When you finished selecting, the selected points will grow in size while all other points shrink.
  • Hold shift or ctrl while drawing a selection to add cells to your selection.
  • You will see how many cells you have selected on the top left of the analysis dashboard.
• Step 3: Click on the analysis you want to perform.

Cell Search within published data (BioTuring database)

The Cell Search Engine is designed to help you find cell populations in the BioTuring public database that have similar transcription profiles to your selected cells. Thereby, this Cell Search can suggest the cell type and signature genes, informing on the enrichment processes of the selected group.

• Step 1: Select a group of cells you want to perform a Cell Search on. Finish by clicking on the tab Cell Search (see Figure 10).
• Step 2: A summary of the selected cells’ profiles will be sent to our server for a query for similar profiles. If such similar profiles can be found, the BBrowserX returns query cards (see Figure 11). If not, it returns no result found. The cards contain the following information:
  • The cell type name (e.g., B cell);
  • The number of cells matched with this cell type;
  • Dimensionality reduction plot in which the matched cells are highlighted;
  • The signatures, or signature genes, for the cell type (e.g., SNX2, IGLL5, and BCL11A)
  • The amount of studies and cells with which the cells of this type are matched
• Step 3: Click explore to see the Cell Search results.

• Step 4: The analysis dashboard shows the clusters from the studies used in Cell Search (see Figure 12).
  • Select each study to see those cells highlighted.
  • Open the drop-down menu on the right that by default says Study ID to change the view to Author’s cell types.
  • Click on Important genes (top left) to import the signature genes from the query card and visualize their expression (Figure 13). Then click Color by and Gene panel (binary) to visualize which cells express the gene panel. Click on each gene separately to visualize which cells express that gene.

Find marker genes

Finding marker genes/proteins and enriched processes in a group of cells helps you to see the genes/proteins and processes that are differently expressed in that selected group, compared to the rest of the cell population. The information is essential to define which cell type the cluster belongs to.

• Step 1: Step 1: Select a group of cells you want to find marker genes for (see Figure 10). Finish by clicking on the tab Marker Gene on the left to the analysis dashboard. This will get you to the Marker Genes dashboard (Figure 14).
• Step 2: Here, you can redo marker gene finding by selecting how many genes a panel should maximally consist of and whether to include negative genes or not (i.e. whether to define clusters by the absence of an expressed gene). You can visualize the expression of each gene panel on the entire UMAP or t-SNE plot by clicking on each gene panel. And you can choose three options of coloring:
  • Gene panel (count) will assign each cell a color for how much of the gene panel is expressed;
  • Gene panel (tricolor) will assign a color for expressing either one or both genes;
  • Gene panel (binary) will assign a color yes/no if the entire gene panel is expressed in each cell;
• For each marker gene panel, the browser assigns an F1 score and shows a decision tree.

Differential gene expression analysis

You can perform differential gene expression analysis on a selection of cells and compare these to another selection of cells. This way, for example, you can compare the gene expression profile of two cell clusters, or you can compare the gene expression profile of one cell clusters versus all other cells. Follow these steps to do so:

• Step 1: Click on the menu item Differential Expression to open the differential gene expression dashboard (see Figure 15).

• Step 2: Give a title to your differential gene expression analysis (see Figure 15), e.g. cluster 0 vs. all clusters. All analyses will be saved and accessible via the tab ‘previous results’ (top right corner).
• Step 3: Give a group name to the first group of cells you want to select, e.g. cluster 0.
• Step 4: You can select the Lasso tool to select a group of cells. It’s also possible to select cells by their metadata tags, such as their sample numbers or other tags.
• Step 5: Click and hold to select a group of cells. Use ctrl or shift to add cells to your selection.

• Step 6: Click on the second group toggle to switch to the second group you want to include in the analysis (see Figure 17).
• Step 7: Give a group name to the second group of cells, e.g. all other cells.
• Step 8: Click and hold to select these cells.
• Step 9: Choose a statistical method for performing the differential gene expression analysis. You can change this method later on. The choice is between:
  • Venice (read more about it here);
  • T-test
  • Wilcoxon
• Step 10: Click run. You will now see the interactive differential gene expression analysis results (Figure 18). It’s possible to redo the analysis with a different analysis method here. Moreover, you can perform pathway analysis or generate a heatmap from these results.

FAQ