Signal-first training-data schemas

README.md

@@ -80,6 +80,19 @@ post filtering and negative-control pooling.
 `ortho.load` reads it from there, so an ortho loads and computes from any
 working directory.
 
+The embedded UMI-wise tables use schema version 2, recorded in `members.json`,
+with signal-first count names: `mpra_umis`, `mpra_reads`, `transfection_umis`,
+`transfection_reads`, and `dna_reads` where available. Counts are non-negative
+integers; null optional counts mean unknown. `cre_id_original` retains the
+original identity of pooled negative controls. The paper's supplementary note
+defines the complete read-wise, UMI-wise, and coarse-reporter schemas.
+
+Coarse transfection-reporter detections, when present, are stored separately
+as `_coarse_reporter` in `members.pkl`, with one presence-only row per
+`(rep_id, cell_bc, cre_id)`. They are loaded with the training data and are not
+paired with individual MPRA UMIs. Zero-handling settings determine how these
+detections contribute to a fit; the main count table alone does not encode them.
+
 Each fit is one archive, so download only the ones you need:
 
 ```python
@@ -97,8 +110,12 @@ client = Client(LocalCluster())
 o = ortho.load(client, "orthos", name)
 ```
 
-This needs `scMPRAforge` 0.1.0.dev1 or later
-(`pip install "scMPRAforge>=0.1.0.dev1"`).
+These archives require the schema-version-2 reader. A compatible package
+revision is [6e67ed8](https://github.com/Reilly-Lab-Yale/scMPRAforge/commit/6e67ed8c49609de57b64bd0b2b556f17b4b12467):
+
+```bash
+pip install "scMPRAforge @ git+https://github.com/Reilly-Lab-Yale/scMPRAforge.git@6e67ed8c49609de57b64bd0b2b556f17b4b12467"
+```
 
 
 ## Licence and source data

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