Pinpoint transcript location of different genes in the dataset and calculate the expression value for each cell.

Find top expressed genes in a cell population and present the locations of their transcripts on the image.

Generate cell-type labels with the help of AI: Automatic cell type prediction built upon BioTuring’s comprehensive database of over a hundred million single cells. Automatic labeling using custom gene sets. Annotate a small part of the data and let our machine learning model do the rest.

Easily select cells or regions and save their labels as metadata.

Annotate a small part of the data and let our machine learning model do the rest. Taking advantage of the spatial context in spatial transcriptomics data, this algorithm reveals interactions at the molecular level between neighboring cells.

Cell search allows you to integrate the power of BioTuring’s single-cell database into spatial transcriptomics data. You are now able to pull out cell populations that share similar biological characteristics, or biological signatures with your selected population from a huge single-cell database to expand your analysis.

Adjust each color channel to get the best fluorescent image.

Making use of the publicly available WikiPathways database, BBrowserX conducts pathway analysis with a collection of 900 pathways. It calculates the scores for each pathway and presents users with the most prominent discoveries.

Visualize complex experimental designs and dataset components, such as cell-type proportion in each sample.

Identify cells enriched for an active gene set.

Making use of the publicly available WikiPathways database, BBrowserX conducts pathway analysis with a collection of 900 pathways. It calculates the scores for each pathway and presents users with the most prominent discoveries.

Identify potential biological processes based on the AUCell enrichment score by utilizing the biological pathway database WikiPathways and calculating the enrichment score based on AUCell scores.