spacr-example-ops: spaCR test data from BioImage Archive S-BIAD394 (Funk et al. 2022)

README.md

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+---
+license: cc-by-4.0
+pretty_name: "spaCR example: optical pooled screen"
+tags:
+- microscopy
+- optical-pooled-screen
+- crispr
+- in-situ-sequencing
+- spacr
+size_categories:
+- n<1K
+source_datasets:
+- "BioImage Archive S-BIAD394"
+---
+
+# spaCR example: optical pooled screen
+
+Two neighbouring 10X fields (sites 331 and 332, the last two of the
+333-field well) of the in-situ sequencing acquisition, with all eleven
+sequencing cycles: `sequencing/c1/` holds cycle 1 as one five-plane stack
+(DAPI, CY3, A594, CY5, CY7) and `sequencing/c2/` ... `c11/` hold each later
+cycle's four base channels as separate files (1,480 x 1,480 px, uint16).
+`library/pool10_prefixes.csv` is the screen's guide library as 11-base
+barcode prefixes. About 394 MB.
+
+Sites 331 and 332 are chosen because spaCR's OPS engine takes a well's field
+count from its highest site number, and the last field keeps the measured
+333-field layout. There are no phenotype images: a 20X phenotype field is
+280 MB on its own, and placing phenotype fields needs anchors spread over the
+whole well.
+
+## Source and licence
+
+A small, **unmodified** sample of the primary fixed-cell optical pooled screen
+of Funk *et al.* 2022, *The phenotypic landscape of essential human genes*,
+Cell 185(24):4634-4653 ([doi:10.1016/j.cell.2022.10.017](https://doi.org/10.1016/j.cell.2022.10.017),
+PMID 36347254), deposited in the BioImage Archive as
+[S-BIAD394](https://www.ebi.ac.uk/biostudies/BioImages/studies/S-BIAD394):
+plate `20200202_6W-LaC024A`, well A1. Every image is byte-identical to the
+archived file; `manifest.csv` inside the archive gives each file's size,
+SHA-256 and its path under
+`https://ftp.ebi.ac.uk/biostudies/fire/S-BIAD/394/S-BIAD394/Files/`.
+
+The BioImage Archive releases directly submitted data under CC0 or CC-BY-4.0
+([policy](https://www.ebi.ac.uk/bioimage-archive/help-policies/)); S-BIAD394
+carries no other licence attribute. This sample is redistributed under
+CC-BY-4.0 with attribution to the original authors. **Cite the paper above**
+if you use it.
+
+The guide library is derived from `pool10_design.csv` in the authors' [code repository](https://github.com/lukebfunk/OpticalPooledScreens) (MIT licence) exactly as their Snakefile derives it.
+
+## Use in spaCR
+
+In the Mask screen press **OPS**, then **Load test data...** under
+*OPS input*: the set is downloaded into
+`~/.cache/spacr/example_data/ops_screen/` and `genotype_source`, `dst_root`,
+`ops_library` and `plate` are filled in. **Run** stitches the two fields,
+segments the nuclei with Cellpose and decodes each nucleus's barcode against
+the library; the tables go to `ops_output/measurements.db`. From Python:
+
+```python
+from spacr.ops_engine import run_ops
+run_ops({"genotype_source": "<folder>/sequencing",
+         "dst_root": "<folder>/ops_output",
+         "ops_library": "<folder>/library/pool10_prefixes.csv",
+         "plate": "20200202_6W-LaC024A"})
+```
+
+Measured on this sample with spaCR's CPU path (`ops_gpu` off): the stitch
+places both fields on one accepted edge; Cellpose (cpsam) numbers 10,675
+nuclei; decoding finds 22,733 spots over eleven cycles, 80 % of them an exact
+match to a library prefix, and assigns a barcode to 3,729 nuclei, 3,549 of
+them library-exact (95 %). On a CPU the segmentation takes about an hour; on a
+GPU it takes minutes. The decode takes about 20 s.
+
+## Files
+
+One uncompressed tar, `spacr-example-ops.tar`, unpacked with Python's tar data filter by
+spaCR. `SHA256SUMS` holds the archive's checksum.

SHA256SUMS

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+f046682d387c78e3208e689198924463c6d2d6c014dd63ebc9ced57e4b7766d6  README.md

spacr-example-ops.tar

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