Neuro QP Docs

Co-expression Analysis

Co-expression analysis lets you compare two stainings together instead of interpreting each staining separately.

Use it when the scientific question is about overlap, separation, or relationship between two markers.

What co-expression means here

In this context, co-expression analysis asks how cells are distributed across combinations of two marker states.

Depending on the mode you choose, Neuro QP can summarize cells that are:

  • positive for both markers
  • positive for the first marker but not the second
  • positive for the second marker but not the first
  • negative for both markers
  • positive for either marker

This allows you to move from a single-marker question to a paired-marker question.

Before you calculate

Co-expression analysis is only meaningful if both stainings are individually trustworthy first.

Before using it, make sure that:

  • both stainings have models you trust
  • the corresponding single-staining Results look reasonable
  • the comparison itself matches a real biological question

If one staining is weak or unreliable, the co-expression summary will inherit that weakness.

The co-expression modes

Each mode answers a slightly different question:

  • ON / ON: cells expressing both markers
  • ON / OFF: cells expressing the first marker but not the second
  • OFF / ON: cells expressing the second marker but not the first
  • OFF / OFF: cells expressing neither marker
  • EITHER: cells expressing one marker or the other or both

In practice, ON / ON is the most intuitive starting point when you want overlap.

The asymmetric modes, ON / OFF and OFF / ON, are especially useful when you want to study separation between two marker-defined populations.

How to interpret the output

Once calculated, co-expression can be viewed with the same plot families used elsewhere in Statistics.

That means you can study co-expression patterns as:

  • broad heatmap patterns
  • direct grouped comparisons
  • anatomical distributions across brain regions

The key is to keep the selected mode in mind at all times.

A strong pattern in ON / ON and a strong pattern in EITHER do not mean the same thing biologically.

Common pitfalls

A few interpretation mistakes are easy to make:

  • treating any overlap as evidence of strong co-expression without checking the exact mode
  • interpreting co-expression before validating each staining separately
  • comparing modes without realizing they represent different biological subsets

A practical workflow

A good workflow for co-expression is:

  1. review each staining on its own first
  2. choose the staining pair based on a clear biological hypothesis
  3. start with ON / ON if overlap is the main question
  4. use the other modes only when they help answer a more specific population question
  5. compare the co-expression view with the single-staining summaries so the interpretation stays grounded

Used this way, co-expression analysis is not just an extra visualization. It becomes a more precise way to describe how two markers relate within the same study.