Handling datasets with multiple modalities

%load_ext autotime
%config InlineBackend.figure_format = 'retina'

import scarf
scarf.__version__

'0.7.8'

time: 1.55 s (started: 2021-08-22 18:10:02 +00:00)

---

1) Fetch and convert data

For this tutorial we will use CITE-Seq data from 10x genomics. This dataset contains two modalities: gene expression and surface protein abundance. Throughout this tutorial we will refer to gene expression modality as RNA and surface protein as ADT. We start by downloading the data and converting it into Zarr format

scarf.fetch_dataset('tenx_8K_pbmc_citeseq', save_path='scarf_datasets')

INFO: Download started...

--2021-08-22 18:10:12--  https://files.de-1.osf.io/v1/resources/zeupv/providers/osfstorage/609096075533b40325e1d205
Resolving files.de-1.osf.io (files.de-1.osf.io)... 35.186.249.111
Connecting to files.de-1.osf.io (files.de-1.osf.io)|35.186.249.111|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://storage.googleapis.com/cos-osf-prod-files-de-1/9d8e4be82c0b76dd8683a1f6659011c65277aa2d464853cfccd49e27835c4f88?response-content-disposition=attachment%3B%20filename%3D%22data.zarr.tar.gz%22%3B%20filename%2A%3DUTF-8%27%27data.zarr.tar.gz&GoogleAccessId=files-de-1%40cos-osf-prod.iam.gserviceaccount.com&Expires=1629655873&Signature=dgIUb5ibhZqsMPsIV%2BrBNM8VI7ruibvZF6Ro%2BIt4rSxesDvRJ88r134rRx2BDyKYcH3VKJQkybVsNJPxkNt8adrSWKERo%2FovQxmqJ%2Br%2FmQl170svSEEwE7PWylEEYV7t6eIdy%2BfmKgrsTrXGdeyKSHcw0Fsk5aCi9OcH0QcmZy7OgBrt5pumFPkocBCowgosXYXSPNRhMprndZAiS8Itot7fMRq6kZ0sPhOmtgrx0OLT6pyS6iRdMVyWn5Fu14eUBq7PY2REINBYd%2By5YubKW2AD129zHF%2FUYUdEVPZrGPE4%2BVdLDr4Q7UM1MF8hZE0vnGSTYrSwlDouDgSNGyw3Fg%3D%3D [following]
--2021-08-22 18:10:13--  https://storage.googleapis.com/cos-osf-prod-files-de-1/9d8e4be82c0b76dd8683a1f6659011c65277aa2d464853cfccd49e27835c4f88?response-content-disposition=attachment%3B%20filename%3D%22data.zarr.tar.gz%22%3B%20filename%2A%3DUTF-8%27%27data.zarr.tar.gz&GoogleAccessId=files-de-1%40cos-osf-prod.iam.gserviceaccount.com&Expires=1629655873&Signature=dgIUb5ibhZqsMPsIV%2BrBNM8VI7ruibvZF6Ro%2BIt4rSxesDvRJ88r134rRx2BDyKYcH3VKJQkybVsNJPxkNt8adrSWKERo%2FovQxmqJ%2Br%2FmQl170svSEEwE7PWylEEYV7t6eIdy%2BfmKgrsTrXGdeyKSHcw0Fsk5aCi9OcH0QcmZy7OgBrt5pumFPkocBCowgosXYXSPNRhMprndZAiS8Itot7fMRq6kZ0sPhOmtgrx0OLT6pyS6iRdMVyWn5Fu14eUBq7PY2REINBYd%2By5YubKW2AD129zHF%2FUYUdEVPZrGPE4%2BVdLDr4Q7UM1MF8hZE0vnGSTYrSwlDouDgSNGyw3Fg%3D%3D
Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.4.48, 172.217.4.80, 172.217.4.208, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.4.48|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 68237599 (65M) [application/octet-stream]
Saving to: ‘scarf_datasets/tenx_8K_pbmc_citeseq/data.zarr.tar.gz’

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 12000K .......... .......... .......... .......... .......... 18%  197M 2s
 12050K .......... .......... .......... .......... .......... 18%  209M 2s
 12100K .......... .......... .......... .......... .......... 18%  237M 2s
 12150K .......... .......... .......... .......... .......... 18%  237M 2s
 12200K .......... .......... .......... .......... .......... 18%  206M 2s
 12250K .......... .......... .......... .......... .......... 18%  245M 2s
 12300K .......... .......... .......... .......... .......... 18%  229M 2s
 12350K .......... .......... .......... .......... .......... 18%  228M 2s
 12400K .......... .......... .......... .......... .......... 18% 1.00M 2s
 12450K .......... .......... .......... .......... .......... 18% 7.62M 2s
 12500K .......... .......... .......... .......... .......... 18% 16.2M 2s
 12550K .......... .......... .......... .......... .......... 18% 13.6M 2s
 12600K .......... .......... .......... .......... .......... 18% 9.15M 2s
 12650K .......... .......... .......... .......... .......... 19%  191M 2s
 12700K .......... .......... .......... .......... .......... 19%  191M 2s
 12750K .......... .......... .......... .......... .......... 19%  187M 2s
 12800K .......... .......... .......... .......... .......... 19%  206M 2s
 12850K .......... .......... .......... .......... .......... 19%  164M 2s
 12900K .......... .......... .......... .......... .......... 19%  207M 2s
 12950K .......... .......... .......... .......... .......... 19%  154M 2s
 13000K .......... .......... .......... .......... .......... 19%  207M 2s
 13050K .......... .......... .......... .......... .......... 19%  213M 2s
 13100K .......... .......... .......... .......... .......... 19%  202M 2s
 13150K .......... .......... .......... .......... .......... 19%  180M 2s
 13200K .......... .......... .......... .......... .......... 19%  212M 2s
 13250K .......... .......... .......... .......... .......... 19%  196M 2s
 13300K .......... .......... .......... .......... .......... 20%  184M 2s
 13350K .......... .......... .......... .......... .......... 20%  168M 2s
 13400K .......... .......... .......... .......... .......... 20%  189M 2s
 13450K .......... .......... .......... .......... .......... 20%  199M 2s
 13500K .......... .......... .......... .......... .......... 20%  217M 2s
 13550K .......... .......... .......... .......... .......... 20% 17.2M 2s
 13600K .......... .......... .......... .......... .......... 20%  189M 2s
 13650K .......... .......... .......... .......... .......... 20%  183M 2s
 13700K .......... .......... .......... .......... .......... 20%  204M 2s
 13750K .......... .......... .......... .......... .......... 20%  161M 2s
 13800K .......... .......... .......... .......... .......... 20%  199M 2s
 13850K .......... .......... .......... .......... .......... 20%  219M 2s
 13900K .......... .......... .......... .......... .......... 20%  205M 2s
 13950K .......... .......... .......... .......... .......... 21%  183M 2s
 14000K .......... .......... .......... .......... .......... 21%  208M 2s
 14050K .......... .......... .......... .......... .......... 21%  199M 2s
 14100K .......... .......... .......... .......... .......... 21%  224M 2s
 14150K .......... .......... .......... .......... .......... 21%  157M 2s
 14200K .......... .......... .......... .......... .......... 21%  205M 2s
 14250K .......... .......... .......... .......... .......... 21%  223M 2s
 14300K .......... .......... .......... .......... .......... 21%  213M 2s
 14350K .......... .......... .......... .......... .......... 21%  199M 2s
 14400K .......... .......... .......... .......... .......... 21%  198M 2s
 14450K .......... .......... .......... .......... .......... 21%  223M 2s
 14500K .......... .......... .......... .......... .......... 21%  210M 2s
 14550K .......... .......... .......... .......... .......... 21% 30.0M 2s
 14600K .......... .......... .......... .......... .......... 21% 4.08M 2s
 14650K .......... .......... .......... .......... .......... 22% 12.2M 2s
 14700K .......... .......... .......... .......... .......... 22% 7.01M 2s
 14750K .......... .......... .......... .......... .......... 22% 10.4M 2s
 14800K .......... .......... .......... .......... .......... 22% 16.8M 2s
 14850K .......... .......... .......... .......... .......... 22%  212M 2s
 14900K .......... .......... .......... .......... .......... 22%  227M 2s
 14950K .......... .......... .......... .......... .......... 22%  190M 2s
 15000K .......... .......... .......... .......... .......... 22%  231M 2s
 15050K .......... .......... .......... .......... .......... 22%  227M 2s
 15100K .......... .......... .......... .......... .......... 22%  230M 2s
 15150K .......... .......... .......... .......... .......... 22%  205M 2s
 15200K .......... .......... .......... .......... .......... 22%  216M 2s
 15250K .......... .......... .......... .......... .......... 22%  233M 2s
 15300K .......... .......... .......... .......... .......... 23%  228M 2s
 15350K .......... .......... .......... .......... .......... 23%  195M 2s
 15400K .......... .......... .......... .......... .......... 23%  232M 2s
 15450K .......... .......... .......... .......... .......... 23%  229M 2s
 15500K .......... .......... .......... .......... .......... 23%  223M 2s
 15550K .......... .......... .......... .......... .......... 23% 5.89M 2s
 15600K .......... .......... .......... .......... .......... 23%  214M 2s
 15650K .......... .......... .......... .......... .......... 23%  227M 2s
 15700K .......... .......... .......... .......... .......... 23%  230M 2s
 15750K .......... .......... .......... .......... .......... 23%  190M 2s
 15800K .......... .......... .......... .......... .......... 23%  221M 2s
 15850K .......... .......... .......... .......... .......... 23%  232M 2s
 15900K .......... .......... .......... .......... .......... 23%  234M 2s
 15950K .......... .......... .......... .......... .......... 24%  202M 2s
 16000K .......... .......... .......... .......... .......... 24%  231M 2s
 16050K .......... .......... .......... .......... .......... 24%  232M 2s
 16100K .......... .......... .......... .......... .......... 24%  224M 2s
 16150K .......... .......... .......... .......... .......... 24%  193M 2s
 16200K .......... .......... .......... .......... .......... 24%  231M 2s
 16250K .......... .......... .......... .......... .......... 24%  214M 2s
 16300K .......... .......... .......... .......... .......... 24%  221M 2s
 16350K .......... .......... .......... .......... .......... 24%  201M 2s
 16400K .......... .......... .......... .......... .......... 24%  223M 2s
 16450K .......... .......... .......... .......... .......... 24%  216M 2s
 16500K .......... .......... .......... .......... .......... 24%  229M 2s
 16550K .......... .......... .......... .......... .......... 24%  191M 2s
 16600K .......... .......... .......... .......... .......... 24%  229M 2s
 16650K .......... .......... .......... .......... .......... 25% 12.2M 2s
 16700K .......... .......... .......... .......... .......... 25% 14.2M 2s
 16750K .......... .......... .......... .......... .......... 25%  223M 2s
 16800K .......... .......... .......... .......... .......... 25%  229M 2s
 16850K .......... .......... .......... .......... .......... 25%  228M 2s
 16900K .......... .......... .......... .......... .......... 25%  186M 2s
 16950K .......... .......... .......... .......... .......... 25%  209M 2s
 17000K .......... .......... .......... .......... .......... 25%  213M 2s
 17050K .......... .......... .......... .......... .......... 25%  202M 2s
 17100K .......... .......... .......... .......... .......... 25%  213M 2s
 17150K .......... .......... .......... .......... .......... 25%  200M 2s
 17200K .......... .......... .......... .......... .......... 25%  206M 2s
 17250K .......... .......... .......... .......... .......... 25%  217M 2s
 17300K .......... .......... .......... .......... .......... 26%  221M 2s
 17350K .......... .......... .......... .......... .......... 26%  177M 2s
 17400K .......... .......... .......... .......... .......... 26%  213M 2s
 17450K .......... .......... .......... .......... .......... 26%  217M 2s
 17500K .......... .......... .......... .......... .......... 26%  190M 2s
 17550K .......... .......... .......... .......... .......... 26%  191M 2s
 17600K .......... .......... .......... .......... .......... 26%  223M 2s
 17650K .......... .......... .......... .......... .......... 26%  200M 2s
 17700K .......... .......... .......... .......... .......... 26%  226M 2s
 17750K .......... .......... .......... .......... .......... 26% 19.9M 2s
 17800K .......... .......... .......... .......... .......... 26% 4.58M 2s
 17850K .......... .......... .......... .......... .......... 26% 13.5M 2s
 17900K .......... .......... .......... .......... .......... 26% 7.25M 2s
 17950K .......... .......... .......... .......... .......... 27% 16.9M 2s
 18000K .......... .......... .......... .......... .......... 27% 32.2M 2s
 18050K .......... .......... .......... .......... .......... 27%  205M 2s
 18100K .......... .......... .......... .......... .......... 27%  210M 2s
 18150K .......... .......... .......... .......... .......... 27%  181M 2s
 18200K .......... .......... .......... .......... .......... 27%  217M 2s
 18250K .......... .......... .......... .......... .......... 27%  214M 2s
 18300K .......... .......... .......... .......... .......... 27%  209M 2s
 18350K .......... .......... .......... .......... .......... 27%  191M 2s
 18400K .......... .......... .......... .......... .......... 27%  217M 2s
 18450K .......... .......... .......... .......... .......... 27%  212M 1s
 18500K .......... .......... .......... .......... .......... 27%  210M 1s
 18550K .......... .......... .......... .......... .......... 27%  175M 1s
 18600K .......... .......... .......... .......... .......... 27%  184M 1s
 18650K .......... .......... .......... .......... .......... 28% 5.48M 1s
 18700K .......... .......... .......... .......... .......... 28% 19.4M 2s
 18750K .......... .......... .......... .......... .......... 28% 15.6M 2s
 18800K .......... .......... .......... .......... .......... 28% 18.8M 2s
 18850K .......... .......... .......... .......... .......... 28% 18.2M 2s
 18900K .......... .......... .......... .......... .......... 28%  131M 2s
 18950K .......... .......... .......... .......... .......... 28%  170M 1s
 19000K .......... .......... .......... .......... .......... 28%  205M 1s
 19050K .......... .......... .......... .......... .......... 28%  172M 1s
 19100K .......... .......... .......... .......... .......... 28%  202M 1s
 19150K .......... .......... .......... .......... .......... 28%  194M 1s
 19200K .......... .......... .......... .......... .......... 28%  210M 1s
 19250K .......... .......... .......... .......... .......... 28%  204M 1s
 19300K .......... .......... .......... .......... .......... 29%  218M 1s
 19350K .......... .......... .......... .......... .......... 29%  159M 1s
 19400K .......... .......... .......... .......... .......... 29%  206M 1s
 19450K .......... .......... .......... .......... .......... 29%  213M 1s
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 19600K .......... .......... .......... .......... .......... 29%  224M 1s
 19650K .......... .......... .......... .......... .......... 29%  210M 1s
 19700K .......... .......... .......... .......... .......... 29%  107M 1s
 19750K .......... .......... .......... .......... .......... 29%  167M 1s
 19800K .......... .......... .......... .......... .......... 29%  172M 1s
 19850K .......... .......... .......... .......... .......... 29%  164M 1s
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 19950K .......... .......... .......... .......... .......... 30%  170M 1s
 20000K .......... .......... .......... .......... .......... 30%  134M 1s
 20050K .......... .......... .......... .......... .......... 30% 12.0M 1s
 20100K .......... .......... .......... .......... .......... 30% 20.4M 1s
 20150K .......... .......... .......... .......... .......... 30%  146M 1s
 20200K .......... .......... .......... .......... .......... 30%  149M 1s
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 20300K .......... .......... .......... .......... .......... 30%  152M 1s
 20350K .......... .......... .......... .......... .......... 30%  178M 1s
 20400K .......... .......... .......... .......... .......... 30% 9.99M 1s
 20450K .......... .......... .......... .......... .......... 30% 5.13M 1s
 20500K .......... .......... .......... .......... .......... 30%  239M 1s
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 20600K .......... .......... .......... .......... .......... 30%  184M 1s
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 20750K .......... .......... .......... .......... .......... 31%  224M 1s
 20800K .......... .......... .......... .......... .......... 31%  194M 1s
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 21300K .......... .......... .......... .......... .......... 32%  238M 1s
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 22150K .......... .......... .......... .......... .......... 33%  209M 1s
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 23000K .......... .......... .......... .......... .......... 34% 16.2M 1s
 23050K .......... .......... .......... .......... .......... 34% 19.2M 1s
 23100K .......... .......... .......... .......... .......... 34% 52.1M 1s
 23150K .......... .......... .......... .......... .......... 34%  221M 1s
 23200K .......... .......... .......... .......... .......... 34%  182M 1s
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 24350K .......... .......... .......... .......... .......... 36% 7.57M 1s
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 25300K .......... .......... .......... .......... .......... 38% 8.99M 1s
 25350K .......... .......... .......... .......... .......... 38%  198M 1s
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 25950K .......... .......... .......... .......... .......... 39% 86.5M 1s
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 26050K .......... .......... .......... .......... .......... 39% 80.9M 1s
 26100K .......... .......... .......... .......... .......... 39% 17.8M 1s
 26150K .......... .......... .......... .......... .......... 39% 87.5M 1s
 26200K .......... .......... .......... .......... .......... 39% 90.1M 1s
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 26750K .......... .......... .......... .......... .......... 40% 7.48M 1s
 26800K .......... .......... .......... .......... .......... 40% 24.2M 1s
 26850K .......... .......... .......... .......... .......... 40% 30.5M 1s
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 27250K .......... .......... .......... .......... .......... 40% 45.7M 1s
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 27350K .......... .......... .......... .......... .......... 41% 8.75M 1s
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INFO: Download finished! File saved here: scarf_datasets/tenx_8K_pbmc_citeseq/data.zarr.tar.gz
INFO: Download started...

.. 93% 33.7M 0s
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 66600K .......... .......... .......... ........             100%  184M=1.6s

2021-08-22 18:10:15 (41.6 MB/s) - ‘scarf_datasets/tenx_8K_pbmc_citeseq/data.zarr.tar.gz’ saved [68237599/68237599]

--2021-08-22 18:10:15--  https://files.de-1.osf.io/v1/resources/zeupv/providers/osfstorage/60936f2f19183d04175582b3
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Location: https://storage.googleapis.com/cos-osf-prod-files-de-1/6d69deb9c1868e6441996e1593fce74e5a3222bdee834bc90e1b77581991eb1e?response-content-disposition=attachment%3B%20filename%3D%22data.h5%22%3B%20filename%2A%3DUTF-8%27%27data.h5&GoogleAccessId=files-de-1%40cos-osf-prod.iam.gserviceaccount.com&Expires=1629655876&Signature=ig2p2bb42a4KAXE9I8qF1vtjjNrrMGzyudEyldFxxGjcOenv0z4BI9Bddku35bkghkwyBsph%2BL5jp3vHXRjOySLCOZxjb9qe%2BFlh9bE1EiO2WK6iY8tM4mI2nJuPF9BV8y1LvFiyQKiWcgnz5DIs9N97TOSnvy6rM86L7tpf9ig3IW7sgNF1S7FsU%2F8tu0fO4POmJp%2BMIPIGkBcFGwecVgi%2BCy4PXmkG23QCX50lQu4lKPqV1vNGNXUYVoxyM0FkKyyeJx%2BZRBzkckTtyitnuxVswu1KsYtKwRDO77lg%2ByJZLZPGHvnawohOBrF2JJAMCaqLBqOx0k28ELrIp3DG%2BQ%3D%3D [following]
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Length: 21788278 (21M) [application/octet-stream]
Saving to: ‘scarf_datasets/tenx_8K_pbmc_citeseq/data.h5’

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INFO: Download finished! File saved here: scarf_datasets/tenx_8K_pbmc_citeseq/data.h5
time: 14.3 s (started: 2021-08-22 18:10:04 +00:00)

....... 66%  213M 0s
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 16950K .......... .......... .......... .......... .......... 79%  219M 0s
 17000K .......... .......... .......... .......... .......... 80%  193M 0s
 17050K .......... .......... .......... .......... .......... 80%  216M 0s
 17100K .......... .......... .......... .......... .......... 80%  219M 0s
 17150K .......... .......... .......... .......... .......... 80%  214M 0s
 17200K .......... .......... .......... .......... .......... 81%  185M 0s
 17250K .......... .......... .......... .......... .......... 81%  209M 0s
 17300K .......... .......... .......... .......... .......... 81%  217M 0s
 17350K .......... .......... .......... .......... .......... 81%  194M 0s
 17400K .......... .......... .......... .......... .......... 82%  118M 0s
 17450K .......... .......... .......... .......... .......... 82%  209M 0s
 17500K .......... .......... .......... .......... .......... 82%  115M 0s
 17550K .......... .......... .......... .......... .......... 82%  154M 0s
 17600K .......... .......... .......... .......... .......... 82%  132M 0s
 17650K .......... .......... .......... .......... .......... 83%  171M 0s
 17700K .......... .......... .......... .......... .......... 83%  217M 0s
 17750K .......... .......... .......... .......... .......... 83%  121M 0s
 17800K .......... .......... .......... .......... .......... 83%  131M 0s
 17850K .......... .......... .......... .......... .......... 84%  216M 0s
 17900K .......... .......... .......... .......... .......... 84%  221M 0s
 17950K .......... .......... .......... .......... .......... 84%  153M 0s
 18000K .......... .......... .......... .......... .......... 84%  180M 0s
 18050K .......... .......... .......... .......... .......... 85%  117M 0s
 18100K .......... .......... .......... .......... .......... 85%  151M 0s
 18150K .......... .......... .......... .......... .......... 85%  221M 0s
 18200K .......... .......... .......... .......... .......... 85%  141M 0s
 18250K .......... .......... .......... .......... .......... 86%  212M 0s
 18300K .......... .......... .......... .......... .......... 86%  222M 0s
 18350K .......... .......... .......... .......... .......... 86%  205M 0s
 18400K .......... .......... .......... .......... .......... 86%  114M 0s
 18450K .......... .......... .......... .......... .......... 86% 7.88M 0s
 18500K .......... .......... .......... .......... .......... 87% 12.2M 0s
 18550K .......... .......... .......... .......... .......... 87% 29.9M 0s
 18600K .......... .......... .......... .......... .......... 87% 23.1M 0s
 18650K .......... .......... .......... .......... .......... 87% 27.6M 0s
 18700K .......... .......... .......... .......... .......... 88% 38.7M 0s
 18750K .......... .......... .......... .......... .......... 88%  196M 0s
 18800K .......... .......... .......... .......... .......... 88%  133M 0s
 18850K .......... .......... .......... .......... .......... 88%  221M 0s
 18900K .......... .......... .......... .......... .......... 89%  148M 0s
 18950K .......... .......... .......... .......... .......... 89%  211M 0s
 19000K .......... .......... .......... .......... .......... 89%  199M 0s
 19050K .......... .......... .......... .......... .......... 89%  113M 0s
 19100K .......... .......... .......... .......... .......... 90%  206M 0s
 19150K .......... .......... .......... .......... .......... 90%  150M 0s
 19200K .......... .......... .......... .......... .......... 90%  179M 0s
 19250K .......... .......... .......... .......... .......... 90%  222M 0s
 19300K .......... .......... .......... .......... .......... 90%  155M 0s
 19350K .......... .......... .......... .......... .......... 91%  209M 0s
 19400K .......... .......... .......... .......... .......... 91%  105M 0s
 19450K .......... .......... .......... .......... .......... 91%  209M 0s
 19500K .......... .......... .......... .......... .......... 91%  220M 0s
 19550K .......... .......... .......... .......... .......... 92% 5.82M 0s
 19600K .......... .......... .......... .......... .......... 92%  186M 0s
 19650K .......... .......... .......... .......... .......... 92%  114M 0s
 19700K .......... .......... .......... .......... .......... 92%  234M 0s
 19750K .......... .......... .......... .......... .......... 93%  211M 0s
 19800K .......... .......... .......... .......... .......... 93%  170M 0s
 19850K .......... .......... .......... .......... .......... 93%  216M 0s
 19900K .......... .......... .......... .......... .......... 93%  162M 0s
 19950K .......... .......... .......... .......... .......... 93%  197M 0s
 20000K .......... .......... .......... .......... .......... 94%  187M 0s
 20050K .......... .......... .......... .......... .......... 94%  118M 0s
 20100K .......... .......... .......... .......... .......... 94%  212M 0s
 20150K .......... .......... .......... .......... .......... 94%  146M 0s
 20200K .......... .......... .......... .......... .......... 95%  188M 0s
 20250K .......... .......... .......... .......... .......... 95%  199M 0s
 20300K .......... .......... .......... .......... .......... 95%  143M 0s
 20350K .......... .......... .......... .......... .......... 95%  218M 0s
 20400K .......... .......... .......... .......... .......... 96%  175M 0s
 20450K .......... .......... .......... .......... .......... 96% 8.59M 0s
 20500K .......... .......... .......... .......... .......... 96% 34.0M 0s
 20550K .......... .......... .......... .......... .......... 96% 34.1M 0s
 20600K .......... .......... .......... .......... .......... 97% 31.3M 0s
 20650K .......... .......... .......... .......... .......... 97% 34.3M 0s
 20700K .......... .......... .......... .......... .......... 97% 34.3M 0s
 20750K .......... .......... .......... .......... .......... 97% 34.3M 0s
 20800K .......... .......... .......... .......... .......... 97% 5.46M 0s
 20850K .......... .......... .......... .......... .......... 98%  208M 0s
 20900K .......... .......... .......... .......... .......... 98%  213M 0s
 20950K .......... .......... .......... .......... .......... 98%  212M 0s
 21000K .......... .......... .......... .......... .......... 98%  189M 0s
 21050K .......... .......... .......... .......... .......... 99%  211M 0s
 21100K .......... .......... .......... .......... .......... 99%  219M 0s
 21150K .......... .......... .......... .......... .......... 99%  209M 0s
 21200K .......... .......... .......... .......... .......... 99%  180M 0s
 21250K .......... .......... .......                         100%  247M=1.1s

2021-08-22 18:10:18 (18.6 MB/s) - ‘scarf_datasets/tenx_8K_pbmc_citeseq/data.h5’ saved [21788278/21788278]

reader = scarf.CrH5Reader('scarf_datasets/tenx_8K_pbmc_citeseq/data.h5', 'rna')

time: 56.2 ms (started: 2021-08-22 18:10:18 +00:00)

We can also quickly check the different kinds of assays present in the file and the number of features from each of them.

reader.assayFeats

	RNA	assay2
type	Gene Expression	Antibody Capture
start	0	33538
end	33538	33555
nFeatures	33538	17

time: 9.8 ms (started: 2021-08-22 18:10:18 +00:00)

The nFeatures column shows the number of features present in each assay. CrH5Reader will automatically pull this information from H5 file and rename the ‘Gene Expression’ assay to RNA. Here it also found another assay: ‘Antibody Capture’ and named it to assay2. We will rename this to ADT.

reader.rename_assays({'assay2': 'ADT'})
reader.assayFeats

	RNA	ADT
type	Gene Expression	Antibody Capture
start	0	33538
end	33538	33555
nFeatures	33538	17

time: 8.3 ms (started: 2021-08-22 18:10:18 +00:00)

Now the data is converted into Zarr format. Like single assay datasets all the data is saved under one Zarr file.

writer = scarf.CrToZarr(reader, zarr_fn='scarf_datasets/tenx_8K_pbmc_citeseq/data.zarr',
                        chunk_size=(2000, 1000))
writer.dump(batch_size=1000)

100%|██████████| 8/8 [00:08<00:00,  1.01s/it]

time: 8.38 s (started: 2021-08-22 18:10:18 +00:00)

---

2) Create a multimodal DataStore

The next step is to create a Scarf DataStore object. This object will be the primary way to interact with the data and all its constituent assays. The first time a Zarr file is loaded, we need to set the default assay. Here we set the ‘RNA’ assay as the default assay. When a Zarr file is loaded, Scarf checks if some per-cell statistics have been calculated. If not, then nFeatures (number of features per cell) and nCounts (total sum of feature counts per cell) are calculated. Scarf will also attempt to calculate the percent of mitochondrial and ribosomal content per cell.

ds = scarf.DataStore('scarf_datasets/tenx_8K_pbmc_citeseq/data.zarr',
                     default_assay='RNA',
                     nthreads=4)

INFO: Setting assay ADT to assay type: ADTassay
INFO: (ADT) Computing nCells and dropOuts
[########################################] | 100% Completed |  0.1s
INFO: Setting assay RNA to assay type: RNAassay
INFO: (RNA) Computing nCells and dropOuts
[########################################] | 100% Completed |  1.9s
INFO: (ADT) Computing nCounts
[########################################] | 100% Completed |  0.1s
INFO: (ADT) Computing nFeatures
[########################################] | 100% Completed |  0.1s
INFO: (RNA) Computing nCounts
[########################################] | 100% Completed |  1.8s
WARNING: Minimum cell count (501) is lower than size factor multiplier (1000)
INFO: (RNA) Computing nFeatures
[########################################] | 100% Completed |  1.9s
INFO: Computing percentage of RNA_percentMito
[########################################] | 100% Completed |  1.2s
INFO: Computing percentage of RNA_percentRibo
[########################################] | 100% Completed |  1.3s
time: 9.15 s (started: 2021-08-22 18:10:27 +00:00)

We can print out the DataStore object to get an overview of all the assays stored.

ds

DataStore has 7865 (7865) cells with 2 assays: ADT RNA
   Cell metadata:
            'I', 'ids', 'names', 'ADT_nCounts', 'ADT_nFeatures',
            'RNA_nCounts', 'RNA_nFeatures', 'RNA_percentMito', 'RNA_percentRibo'
   ADT assay has 17 (17) features and following metadata:
            'I', 'ids', 'names', 'dropOuts', 'nCells',

   RNA assay has 13832 (33538) features and following metadata:
            'I', 'ids', 'names', 'dropOuts', 'nCells',

time: 7.72 ms (started: 2021-08-22 18:10:36 +00:00)

Feature attribute tables for each of the assays can be accessed like this:

ds.RNA.feats.head()

	I	ids	names	dropOuts	nCells
0	False	ENSG00000243485	MIR1302-2HG	7865	0
1	False	ENSG00000237613	FAM138A	7865	0
2	False	ENSG00000186092	OR4F5	7865	0
3	False	ENSG00000238009	AL627309.1	7853	12
4	False	ENSG00000239945	AL627309.3	7865	0

time: 21.4 ms (started: 2021-08-22 18:10:36 +00:00)

ds.ADT.feats.head()

	I	ids	names	dropOuts	nCells
0	True	CD3	CD3_TotalSeqB	1	7864
1	True	CD4	CD4_TotalSeqB	1	7864
2	True	CD8a	CD8a_TotalSeqB	2	7863
3	True	CD14	CD14_TotalSeqB	1	7864
4	True	CD15	CD15_TotalSeqB	1	7864

time: 19.4 ms (started: 2021-08-22 18:10:36 +00:00)

Cell filtering is performed based on the default assay. Here we use the auto_filter_cells method of the DataStore to filter low quality cells.

ds.auto_filter_cells()

INFO: 154 cells flagged for filtering out using attribute RNA_nCounts
INFO: 326 cells flagged for filtering out using attribute RNA_nFeatures
INFO: 119 cells flagged for filtering out using attribute RNA_percentMito
INFO: 21 cells flagged for filtering out using attribute RNA_percentRibo

time: 3.72 s (started: 2021-08-22 18:10:36 +00:00)

---

3) Process gene expression modality

Now we process the RNA assay to perform feature selection, create KNN graph, run UMAP reduction and clustering. These steps are same as shown in the basic workflow for scRNA-Seq data.

ds.mark_hvgs(min_cells=20, top_n=500, min_mean=-3, max_mean=2, max_var=6)
ds.make_graph(feat_key='hvgs', k=11, dims=15, n_centroids=100)
ds.run_umap(fit_n_epochs=250, spread=5, min_dist=1, parallel=True)
ds.run_leiden_clustering(resolution=1)

INFO: (RNA) Computing nCells
[########################################] | 100% Completed |  3.0s
INFO: (RNA) Computing normed_tot
[########################################] | 100% Completed |  2.9s
INFO: (RNA) Computing sigmas
[########################################] | 100% Completed |  3.6s
INFO: 497 genes marked as HVGs

INFO: No value provided for parameter `log_transform`. Will use default value: True
INFO: No value provided for parameter `renormalize_subset`. Will use default value: True
INFO: No value provided for parameter `pca_cell_key`. Will use default value: I
INFO: Using PCA for dimension reduction
INFO: No value provided for parameter `ann_metric`. Will use default value: l2
INFO: No value provided for parameter `ann_efc`. Will use default value: min(100, max(k * 3, 50))
INFO: No value provided for parameter `ann_ef`. Will use default value: min(100, max(k * 3, 50))
INFO: No value provided for parameter `ann_m`. Will use default value: 48
INFO: No value provided for parameter `rand_state`. Will use default value: 4466
INFO: No value provided for parameter `local_connectivity`. Will use default value: 1.0
INFO: No value provided for parameter `bandwidth`. Will use default value: 1.5
INFO: Normalizing with feature subset
[########################################] | 100% Completed |  1.4s

Writing data to normed__I__hvgs/data: 100%|██████████| 4/4 [00:02<00:00,  1.61it/s]

INFO: Calculating mean of norm. data
[                                        ] | 0% Completed |  0.1s

[########################################] | 100% Completed |  0.2s
INFO: Calculating std. dev. of norm. data
[########################################] | 100% Completed |  0.3s

Fitting PCA: 100%|██████████| 4/4 [00:02<00:00,  1.79it/s]
Fitting ANN: 100%|██████████| 4/4 [00:00<00:00,  4.20it/s]
Fitting kmeans: 100%|██████████| 4/4 [00:01<00:00,  3.36it/s]
Estimating seed partitions: 100%|██████████| 4/4 [00:00<00:00,  5.28it/s]

INFO: Saving loadings to RNA/normed__I__hvgs/reduction__pca__15__I

INFO: Saving ANN index to RNA/normed__I__hvgs/reduction__pca__15__I/ann__l2__50__50__48__4466
INFO: Saving kmeans clusters to RNA/normed__I__hvgs/reduction__pca__15__I/kmeans__100__4466

Saving KNN graph: 100%|██████████| 4/4 [00:00<00:00,  4.72it/s]

INFO: ANN recall: 99.81%

Smoothening KNN distances: 100%|██████████| 1/1 [00:04<00:00,  4.09s/it]
/home/docs/checkouts/readthedocs.org/user_builds/scarf/envs/0.7.8/lib/python3.8/site-packages/umap/umap_.py:1330: RuntimeWarning: divide by zero encountered in power
  return 1.0 / (1.0 + a * x ** (2 * b))

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time: 1min 1s (started: 2021-08-22 18:10:40 +00:00)

ds.plot_layout(layout_key='RNA_UMAP', color_by='RNA_leiden_cluster')

time: 1.33 s (started: 2021-08-22 18:11:41 +00:00)

---

4) Process protein surface abundance modality

We will now perform similar steps as RNA for the ADT data. Since ADT panels are often custom designed, we will not perform any feature selection step. This particular data contains some control antibodies which we should filter out before downstream analysis.

ds.ADT.feats.head(n=ds.ADT.feats.N)

	I	ids	names	dropOuts	nCells
0	True	CD3	CD3_TotalSeqB	1	7864
1	True	CD4	CD4_TotalSeqB	1	7864
2	True	CD8a	CD8a_TotalSeqB	2	7863
3	True	CD14	CD14_TotalSeqB	1	7864
4	True	CD15	CD15_TotalSeqB	1	7864
5	True	CD16	CD16_TotalSeqB	1	7864
6	True	CD56	CD56_TotalSeqB	1	7864
7	True	CD19	CD19_TotalSeqB	163	7702
8	True	CD25	CD25_TotalSeqB	4	7861
9	True	CD45RA	CD45RA_TotalSeqB	1	7864
10	True	CD45RO	CD45RO_TotalSeqB	1	7864
11	True	PD-1	PD-1_TotalSeqB	2	7863
12	True	TIGIT	TIGIT_TotalSeqB	16	7849
13	True	CD127	CD127_TotalSeqB	3	7862
14	True	IgG2a	IgG2a_control_TotalSeqB	26	7839
15	True	IgG1	IgG1_control_TotalSeqB	9	7856
16	True	IgG2b	IgG2b_control_TotalSeqB	226	7639

time: 25.2 ms (started: 2021-08-22 18:11:43 +00:00)

We can manually filter out the control antibodies by updating I to be False for those features. To do so we first extract the names of all the ADT features like below:

adt_names = ds.ADT.feats.to_pandas_dataframe(['names'])['names']
adt_names

0               CD3_TotalSeqB
1               CD4_TotalSeqB
2              CD8a_TotalSeqB
3              CD14_TotalSeqB
4              CD15_TotalSeqB
5              CD16_TotalSeqB
6              CD56_TotalSeqB
7              CD19_TotalSeqB
8              CD25_TotalSeqB
9            CD45RA_TotalSeqB
10           CD45RO_TotalSeqB
11             PD-1_TotalSeqB
12            TIGIT_TotalSeqB
13            CD127_TotalSeqB
14    IgG2a_control_TotalSeqB
15     IgG1_control_TotalSeqB
16    IgG2b_control_TotalSeqB
Name: names, dtype: object

time: 11.1 ms (started: 2021-08-22 18:11:43 +00:00)

The ADT features with ‘control’ in name are designated as control antibodies. You can have your own selection criteria here. The aim here is to create a boolean array that has True value for features to be removed.

is_control = adt_names.str.contains('control').values
is_control

array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False,  True,  True,  True])

time: 4.55 ms (started: 2021-08-22 18:11:43 +00:00)

Now we update I to remove the control features. update_key method takes a boolean array and disables the features that have False value. So we invert the above created array (using ~) before providing it to update_key. The second parameter for update_key denotes which feature table boolean column to modify, I in this case.

ds.ADT.feats.update_key(~is_control, 'I')
ds.ADT.feats.head(n=ds.ADT.feats.N)

	I	ids	names	dropOuts	nCells
0	True	CD3	CD3_TotalSeqB	1	7864
1	True	CD4	CD4_TotalSeqB	1	7864
2	True	CD8a	CD8a_TotalSeqB	2	7863
3	True	CD14	CD14_TotalSeqB	1	7864
4	True	CD15	CD15_TotalSeqB	1	7864
5	True	CD16	CD16_TotalSeqB	1	7864
6	True	CD56	CD56_TotalSeqB	1	7864
7	True	CD19	CD19_TotalSeqB	163	7702
8	True	CD25	CD25_TotalSeqB	4	7861
9	True	CD45RA	CD45RA_TotalSeqB	1	7864
10	True	CD45RO	CD45RO_TotalSeqB	1	7864
11	True	PD-1	PD-1_TotalSeqB	2	7863
12	True	TIGIT	TIGIT_TotalSeqB	16	7849
13	True	CD127	CD127_TotalSeqB	3	7862
14	False	IgG2a	IgG2a_control_TotalSeqB	26	7839
15	False	IgG1	IgG1_control_TotalSeqB	9	7856
16	False	IgG2b	IgG2b_control_TotalSeqB	226	7639

time: 25.3 ms (started: 2021-08-22 18:11:43 +00:00)

Assays named ADT are automatically created as objects of the ADTassay class, which uses CLR (centred log ratio) normalization as the default normalization method.

print (ds.ADT)
print (ds.ADT.normMethod.__name__)

ADTassay ADT with 14(17) features
norm_clr
time: 2.05 ms (started: 2021-08-22 18:11:43 +00:00)

Now we are ready to create a KNN graph of cells using only ADT data. Here we will use all the features (except those that were filtered out) and that is why we use I as value for feat_key. It is important to note the value for from_assay parameter which has now been set to ADT. If no value is provided for from_assay then it is automatically set to the default assay.

ds.make_graph(from_assay='ADT', feat_key='I', k=11, dims=11, n_centroids=100)

INFO: No value provided for parameter `log_transform`. Will use default value: True
INFO: No value provided for parameter `renormalize_subset`. Will use default value: True
INFO: No value provided for parameter `pca_cell_key`. Will use default value: I
INFO: Using PCA for dimension reduction
INFO: No value provided for parameter `ann_metric`. Will use default value: l2
INFO: No value provided for parameter `ann_efc`. Will use default value: min(100, max(k * 3, 50))
INFO: No value provided for parameter `ann_ef`. Will use default value: min(100, max(k * 3, 50))
INFO: No value provided for parameter `ann_m`. Will use default value: 48
INFO: No value provided for parameter `rand_state`. Will use default value: 4466
INFO: No value provided for parameter `local_connectivity`. Will use default value: 1.0
INFO: No value provided for parameter `bandwidth`. Will use default value: 1.5

Writing data to normed__I__I/data: 100%|██████████| 4/4 [00:00<00:00,  5.48it/s]

INFO: Calculating mean of norm. data
[                                        ] | 0% Completed |  0.1s

[########################################] | 100% Completed |  0.2s
INFO: Calculating std. dev. of norm. data
[########################################] | 100% Completed |  0.2s

Fitting PCA: 100%|██████████| 4/4 [00:00<00:00, 14.31it/s]
Fitting ANN: 100%|██████████| 4/4 [00:00<00:00,  5.23it/s]
Fitting kmeans: 100%|██████████| 4/4 [00:01<00:00,  3.87it/s]
Estimating seed partitions: 100%|██████████| 4/4 [00:00<00:00,  6.16it/s]

INFO: Saving loadings to ADT/normed__I__I/reduction__pca__11__I

INFO: Saving ANN index to ADT/normed__I__I/reduction__pca__11__I/ann__l2__50__50__48__4466
INFO: Saving kmeans clusters to ADT/normed__I__I/reduction__pca__11__I/kmeans__100__4466

Saving KNN graph: 100%|██████████| 4/4 [00:00<00:00,  6.56it/s]

INFO: ANN recall: 99.97%

Smoothening KNN distances: 100%|██████████| 1/1 [00:00<00:00, 19.48it/s]

time: 4.86 s (started: 2021-08-22 18:11:43 +00:00)

UMAP and clustering can be run on ADT assay by simply providing setting from_assay parameter value to ‘ADT’

ds.run_umap(from_assay='ADT', fit_n_epochs=250, spread=5, min_dist=1, parallel=True)
ds.run_leiden_clustering(from_assay='ADT', resolution=1)

/home/docs/checkouts/readthedocs.org/user_builds/scarf/envs/0.7.8/lib/python3.8/site-packages/umap/umap_.py:1330: RuntimeWarning: divide by zero encountered in power
  return 1.0 / (1.0 + a * x ** (2 * b))

        completed  0  /  250 epochs
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        completed  30  /  100 epochs
        completed  40  /  100 epochs
        completed  50  /  100 epochs
        completed  60  /  100 epochs
        completed  70  /  100 epochs
        completed  80  /  100 epochs
        completed  90  /  100 epochs
time: 31.2 s (started: 2021-08-22 18:11:48 +00:00)

If we now check the cell attribute table, we will find the UMAP coordinates and clusters calculated using ADT assay

ds.cells.head()

	I	ids	names	ADT_UMAP1	ADT_UMAP2	ADT_leiden_cluster	ADT_nCounts	ADT_nFeatures	RNA_UMAP1	RNA_UMAP2	RNA_leiden_cluster	RNA_nCounts	RNA_nFeatures	RNA_percentMito	RNA_percentRibo
0	True	AAACCCAAGATTGTGA-1	AAACCCAAGATTGTGA-1	-24.323153	16.300386	16	981.0	17.0	-25.398972	25.251200	5	6160.0	2194.0	8.668831	15.259740
1	True	AAACCCACATCGGTTA-1	AAACCCACATCGGTTA-1	-9.889935	20.346741	15	1475.0	17.0	-23.984476	21.164082	5	6713.0	2093.0	6.316103	19.037688
2	True	AAACCCAGTACCGCGT-1	AAACCCAGTACCGCGT-1	-13.106612	22.806581	7	7149.0	17.0	-15.875791	21.863632	2	3637.0	1518.0	8.056090	16.002200
3	True	AAACCCAGTATCGAAA-1	AAACCCAGTATCGAAA-1	23.668877	18.399595	2	6831.0	17.0	-29.925396	-19.403278	4	1244.0	737.0	9.003215	18.729904
4	True	AAACCCAGTCGTCATA-1	AAACCCAGTCGTCATA-1	26.384117	18.577906	2	6839.0	17.0	-24.395090	-29.287636	4	2611.0	1240.0	6.204519	16.353887

time: 48.5 ms (started: 2021-08-22 18:12:19 +00:00)

Visualizing the UMAP and clustering calcualted using ADT only

ds.plot_layout(layout_key='ADT_UMAP', color_by='ADT_leiden_cluster')

time: 1.26 s (started: 2021-08-22 18:12:19 +00:00)

---

5) Cross modality comparison

It is generally of quite interest to see how different modalities coroborate each other.

# UMAP on RNA and coloured with clusters calculated on ADT
ds.plot_layout(layout_key='RNA_UMAP', color_by='ADT_leiden_cluster')

time: 1.26 s (started: 2021-08-22 18:12:20 +00:00)

# UMAP on ADT and coloured with clusters calculated on RNA
ds.plot_layout(layout_key='ADT_UMAP', color_by='RNA_leiden_cluster')

time: 1.32 s (started: 2021-08-22 18:12:22 +00:00)

We can quantify the overlap of cells between RNA and ADT clusters. The following table has ADT clusters on columns and RNA clusters on rows. This table shows a cross tabulation of cells across the clustering from the two modalities.

import pandas as pd

df = pd.crosstab(ds.cells.fetch('RNA_leiden_cluster'),
                 ds.cells.fetch('ADT_leiden_cluster'))
df

col_0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20
row_0
1	928	0	83	0	1	0	0	15	0	1	18	17	67	17	0	1	2	0	0	0
2	0	0	1	290	0	3	296	0	111	1	3	23	0	0	76	35	0	11	1	11
3	17	0	428	3	0	4	0	1	0	9	175	21	134	8	0	1	4	1	0	0
4	0	735	0	0	0	0	0	0	0	3	0	35	0	0	0	0	31	0	0	0
5	0	0	0	349	0	3	83	0	192	0	2	23	1	1	60	22	0	4	0	7
6	0	2	47	0	0	330	0	2	0	7	0	14	0	74	0	0	9	0	0	0
7	0	0	0	0	0	5	0	335	0	4	0	4	1	7	0	0	7	0	0	0
8	0	0	0	0	312	0	0	0	0	0	0	1	1	0	0	0	0	0	7	0
9	2	0	235	0	1	11	0	1	0	2	44	11	1	5	0	0	3	0	0	0
10	16	1	7	0	0	3	0	3	0	220	8	17	2	1	0	0	17	0	0	0
11	1	1	2	0	1	61	0	43	0	62	15	12	1	42	0	0	8	0	0	0
12	0	0	0	0	208	0	0	0	1	0	1	3	0	0	0	2	0	0	11	0
13	0	134	0	0	0	2	0	2	0	0	0	5	0	10	0	1	4	3	0	0
14	0	0	0	58	0	1	34	0	29	0	1	9	0	0	13	1	0	2	0	2
15	81	1	27	0	0	0	0	9	0	1	7	3	6	3	0	0	1	0	0	0
16	0	1	4	20	0	11	5	0	7	3	1	36	2	0	1	0	0	0	0	10
17	0	0	0	4	1	0	3	0	0	0	0	5	0	0	0	0	0	59	0	0
18	31	0	1	0	0	2	0	0	0	0	1	0	3	10	0	1	0	0	0	0
19	0	0	0	1	0	0	3	0	0	0	0	2	0	2	1	32	0	1	0	0
20	0	0	0	0	0	0	0	0	0	5	0	4	0	0	0	0	0	0	30	0
21	0	0	0	0	0	0	0	0	0	0	0	0	0	21	0	0	0	1	0	0

time: 52.4 ms (started: 2021-08-22 18:12:23 +00:00)

There are possibly many interesting strategies to analyze this further. One simple way to summarize the above table can be quantify the transcriptomics ‘purity’ of ADT clusters:

(100 * df.max()/df.sum()).sort_values(ascending=False)

col_0
1     86.245353
2     84.000000
8     81.508516
6     75.688073
18    71.951220
7     69.811321
10    69.182390
11    63.405797
19    61.224490
13    61.187215
5     59.541985
9     56.470588
3     51.257485
15    50.331126
4     48.137931
14    36.815920
20    36.666667
16    36.458333
17    36.046512
12    14.693878
dtype: float64

time: 7.44 ms (started: 2021-08-22 18:12:23 +00:00)

Individual ADT expression can be visualized in both UMAPs easily.

ds.plot_layout(from_assay='ADT', layout_key='ADT_UMAP', color_by='CD16_TotalSeqB')
ds.plot_layout(from_assay='ADT', layout_key='RNA_UMAP', color_by='CD16_TotalSeqB')

time: 3.46 s (started: 2021-08-22 18:12:23 +00:00)

We can also query gene expression and visualize it on both RNA and ADT UMAPs. Here we query gene FCGR3A which codes for CD16.

ds.plot_layout(from_assay='RNA', layout_key='RNA_UMAP', color_by='FCGR3A')
ds.plot_layout(from_assay='RNA', layout_key='ADT_UMAP', color_by='FCGR3A')

time: 3.71 s (started: 2021-08-22 18:12:27 +00:00)

---

That is all for this vignette.