terraFlow looks at every combination of 1-5 markers in your data and picks out key differences between sample groups. It then connects results to primary literature, highlighting phenotypes that have been previously reported in the literature.

Key features

• Sort phenotypes by effect size, statistical significance, or literature relevance
• Summarize key differences between sample groups
• Export results as CSV tables

Report summary

The numbers at the top of the page give a high-level summary of the analysis.

• Markers evaluated: Number of markers included in the analysis. Excludes time, scatter, and any channels manually turned off during upload.
• Phenotypes evaluated: Number of phenotypes evaluated across all marker combinations.
• Papers mapped: Number of papers associated with Phenotypes evaluated.
• Topics matched: Papers mapped that also relate to your topic of interest. Click on this card to update your Topics of interest.
• Keywords: Number of keyword groupings. Click on this card to see a summary of your keyword groups.

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How many phenotypes does terraFlow look at?

terraFlow looks at every combination of 1-5 markers that is expressed by at least 50 cells per sample on average. Many combinations do not occur in biological datasets and will naturally drop out of the analysis.

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Topics of interest

terraFlow ranks papers and phenotypes by their relevance to your topics of interest. You can update your topics by clicking the Topics Matched card at the top of the report. Topics are based on MeSH terms and can include diseases, molecules, and immune processes. Once you’re done editing your topics, click Save. Then, click Close to hide the selector.

topics_of_interest_30fps.mp4

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How does terraFlow define relevance?

terraFlow scores papers on two factors.

• Priority: Where the phenotype appears in the paper. Papers that mention the phenotype in the title or abstract score higher. Papers that mention the phenotype in the supplement score lower.
• Relevance: How closely the paper matches the topic of interest. For example, if you were searching for Lymphoma and a paper mentioned Leukemia, it would receive partial credit because both diseases fall under Blood Cancers.

Phenotypes will have a high Relevance if they appear in high-scoring papers.

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Phenotype table

The main table lists all the phenotypes detected in your dataset. Click on a column name to sort by that value. Click again to toggle ascending or descending order.