Scarf’s basic workflow for scRNA-Seq¶
This workflow is meant to familiarize users with the Scarf API and how data is internally handled in Scarf. Please checkout the quick start guide if you are interested in the minimal steps required to run the analysis.
[1]:
%load_ext autotime
%config InlineBackend.figure_format = 'retina'
import scarf
scarf.__version__
[1]:
'0.7.8'
time: 1.53 s (started: 2021-08-22 17:55:15 +00:00)
Download the count matrix from 10x’s website using the fetch_dataset function. This is a convenience function that stores URLs of datasets that can be downloaded. The save_path parameter allows the data to be saved to a location of choice.
[2]:
scarf.fetch_dataset('tenx_5K_pbmc_rnaseq', save_path='scarf_datasets')
INFO: Download started...
--2021-08-22 17:55:22-- https://files.de-1.osf.io/v1/resources/zeupv/providers/osfstorage/609678235533b405a6e22fad
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-1/17a47c2d3b323858ae9183e332fa31a01bf130eaa7ca6199ca9d4efe5217374f?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=1629654983&Signature=vMt234NEyL%2FmfMeLXSwPuZ1BVp4XeFEY4nkrYBCHhJ8Rama%2Bb51V%2FYvSnxcNdbZUMNnbYZcPEKJZpm3%2Bu5IRJoeWPEFmme%2FG9ffqu519osfnAq6o7veSWWxsxCrMLgpMpCCnchx0FWKeDAmGHqw%2Fj663E%2B2dFgwxEKYJ7HSRYc1Mk3JhATZgof1USrCdpoc6hHXvO04ouPzjUvCOdxAYOezy%2BPmEdDbe2kADmhIFgGHaT78FUtUNMEBOidRfELI0kN8czr9MKuSI6fDdjW%2FniGLyba5R0d52094jQa8v68VF0SA3%2BEXdpLVrcic356W3koC5pQ1rj9aDOZB66HzUaw%3D%3D [following]
--2021-08-22 17:55:23-- https://storage.googleapis.com/cos-osf-prod-files-de-1/17a47c2d3b323858ae9183e332fa31a01bf130eaa7ca6199ca9d4efe5217374f?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=1629654983&Signature=vMt234NEyL%2FmfMeLXSwPuZ1BVp4XeFEY4nkrYBCHhJ8Rama%2Bb51V%2FYvSnxcNdbZUMNnbYZcPEKJZpm3%2Bu5IRJoeWPEFmme%2FG9ffqu519osfnAq6o7veSWWxsxCrMLgpMpCCnchx0FWKeDAmGHqw%2Fj663E%2B2dFgwxEKYJ7HSRYc1Mk3JhATZgof1USrCdpoc6hHXvO04ouPzjUvCOdxAYOezy%2BPmEdDbe2kADmhIFgGHaT78FUtUNMEBOidRfELI0kN8czr9MKuSI6fDdjW%2FniGLyba5R0d52094jQa8v68VF0SA3%2BEXdpLVrcic356W3koC5pQ1rj9aDOZB66HzUaw%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: 18098007 (17M) [application/octet-stream]
Saving to: ‘scarf_datasets/tenx_5K_pbmc_rnaseq/data.h5’
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INFO: Download finished! File saved here: scarf_datasets/tenx_5K_pbmc_rnaseq/data.h5
INFO: Download started...
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12850K .......... .......... .......... .......... .......... 72% 228M 0s
12900K .......... .......... .......... .......... .......... 73% 232M 0s
12950K .......... .......... .......... .......... .......... 73% 204M 0s
13000K .......... .......... .......... .......... .......... 73% 230M 0s
13050K .......... .......... .......... .......... .......... 74% 227M 0s
13100K .......... .......... .......... .......... .......... 74% 224M 0s
13150K .......... .......... .......... .......... .......... 74% 191M 0s
13200K .......... .......... .......... .......... .......... 74% 225M 0s
13250K .......... .......... .......... .......... .......... 75% 216M 0s
13300K .......... .......... .......... .......... .......... 75% 221M 0s
13350K .......... .......... .......... .......... .......... 75% 203M 0s
13400K .......... .......... .......... .......... .......... 76% 229M 0s
13450K .......... .......... .......... .......... .......... 76% 227M 0s
13500K .......... .......... .......... .......... .......... 76% 229M 0s
13550K .......... .......... .......... .......... .......... 76% 191M 0s
13600K .......... .......... .......... .......... .......... 77% 226M 0s
13650K .......... .......... .......... .......... .......... 77% 222M 0s
13700K .......... .......... .......... .......... .......... 77% 221M 0s
13750K .......... .......... .......... .......... .......... 78% 7.92M 0s
13800K .......... .......... .......... .......... .......... 78% 203M 0s
13850K .......... .......... .......... .......... .......... 78% 90.0M 0s
13900K .......... .......... .......... .......... .......... 78% 214M 0s
13950K .......... .......... .......... .......... .......... 79% 131M 0s
14000K .......... .......... .......... .......... .......... 79% 217M 0s
14050K .......... .......... .......... .......... .......... 79% 207M 0s
14100K .......... .......... .......... .......... .......... 80% 156M 0s
14150K .......... .......... .......... .......... .......... 80% 12.3M 0s
14200K .......... .......... .......... .......... .......... 80% 14.1M 0s
14250K .......... .......... .......... .......... .......... 80% 34.5M 0s
14300K .......... .......... .......... .......... .......... 81% 34.0M 0s
14350K .......... .......... .......... .......... .......... 81% 30.5M 0s
14400K .......... .......... .......... .......... .......... 81% 4.89M 0s
14450K .......... .......... .......... .......... .......... 82% 139M 0s
14500K .......... .......... .......... .......... .......... 82% 218M 0s
14550K .......... .......... .......... .......... .......... 82% 103M 0s
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14700K .......... .......... .......... .......... .......... 83% 217M 0s
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14900K .......... .......... .......... .......... .......... 84% 150M 0s
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15000K .......... .......... .......... .......... .......... 85% 145M 0s
15050K .......... .......... .......... .......... .......... 85% 203M 0s
15100K .......... .......... .......... .......... .......... 85% 115M 0s
15150K .......... .......... .......... .......... .......... 86% 134M 0s
15200K .......... .......... .......... .......... .......... 86% 189M 0s
15250K .......... .......... .......... .......... .......... 86% 139M 0s
15300K .......... .......... .......... .......... .......... 86% 215M 0s
15350K .......... .......... .......... .......... .......... 87% 107M 0s
15400K .......... .......... .......... .......... .......... 87% 219M 0s
15450K .......... .......... .......... .......... .......... 87% 140M 0s
15500K .......... .......... .......... .......... .......... 87% 224M 0s
15550K .......... .......... .......... .......... .......... 88% 126M 0s
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15700K .......... .......... .......... .......... .......... 89% 203M 0s
15750K .......... .......... .......... .......... .......... 89% 197M 0s
15800K .......... .......... .......... .......... .......... 89% 201M 0s
15850K .......... .......... .......... .......... .......... 89% 222M 0s
15900K .......... .......... .......... .......... .......... 90% 222M 0s
15950K .......... .......... .......... .......... .......... 90% 173M 0s
16000K .......... .......... .......... .......... .......... 90% 226M 0s
16050K .......... .......... .......... .......... .......... 91% 11.5M 0s
16100K .......... .......... .......... .......... .......... 91% 22.0M 0s
16150K .......... .......... .......... .......... .......... 91% 20.6M 0s
16200K .......... .......... .......... .......... .......... 91% 23.4M 0s
16250K .......... .......... .......... .......... .......... 92% 76.1M 0s
16300K .......... .......... .......... .......... .......... 92% 145M 0s
16350K .......... .......... .......... .......... .......... 92% 134M 0s
16400K .......... .......... .......... .......... .......... 93% 210M 0s
16450K .......... .......... .......... .......... .......... 93% 197M 0s
16500K .......... .......... .......... .......... .......... 93% 118M 0s
16550K .......... .......... .......... .......... .......... 93% 137M 0s
16600K .......... .......... .......... .......... .......... 94% 208M 0s
16650K .......... .......... .......... .......... .......... 94% 152M 0s
16700K .......... .......... .......... .......... .......... 94% 208M 0s
16750K .......... .......... .......... .......... .......... 95% 5.86M 0s
16800K .......... .......... .......... .......... .......... 95% 218M 0s
16850K .......... .......... .......... .......... .......... 95% 213M 0s
16900K .......... .......... .......... .......... .......... 95% 216M 0s
16950K .......... .......... .......... .......... .......... 96% 197M 0s
17000K .......... .......... .......... .......... .......... 96% 220M 0s
17050K .......... .......... .......... .......... .......... 96% 211M 0s
17100K .......... .......... .......... .......... .......... 97% 180M 0s
17150K .......... .......... .......... .......... .......... 97% 205M 0s
17200K .......... .......... .......... .......... .......... 97% 213M 0s
17250K .......... .......... .......... .......... .......... 97% 215M 0s
17300K .......... .......... .......... .......... .......... 98% 195M 0s
17350K .......... .......... .......... .......... .......... 98% 209M 0s
17400K .......... .......... .......... .......... .......... 98% 220M 0s
17450K .......... .......... .......... .......... .......... 99% 202M 0s
17500K .......... .......... .......... .......... .......... 99% 176M 0s
17550K .......... .......... .......... .......... .......... 99% 204M 0s
17600K .......... .......... .......... .......... .......... 99% 212M 0s
17650K .......... .......... ... 100% 216M=0.4s
2021-08-22 17:55:24 (41.8 MB/s) - ‘scarf_datasets/tenx_5K_pbmc_rnaseq/data.h5’ saved [18098007/18098007]
--2021-08-22 17:55:24-- https://files.de-1.osf.io/v1/resources/zeupv/providers/osfstorage/6096d5685533b405bfe1eeb3
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-1/1ef8b465325d04f0b87ed06fe7813ec6698d1a9a45e3511e885ae45e732f9bb8?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=1629654985&Signature=TkUkKS6PQ0P%2F4HtCdmeTrFwOqFvom6yWak2CCSAxUThLY%2FKSnIwDSfxuGAMuzSh44%2BJkn4DmjttJoQKzHuhR%2Bsm%2B9q9Sbi%2BVCn55LBdgZtkHZeK58%2BHJFVv25xdxiaSFDuZpr4MZPSrawke846r%2Bgl8CBTZdr%2BXc8NCiYmIQh%2FgVnfFX%2BxxeEHirGHnqBxwqzPrlXDEnzxd6HRv31c6ICvbnAjWe%2Fa1vNFPPx68jzl%2BwAhcPIGJcEBzFHDX1JWaS%2FpaTxCSKOeFRxxbJQulw58DHcZD4dH3Qb%2BfZqz%2Bmiyvudy2slkOWFDRmEPuqe2JxdO186gtUkXfzV5h5Q9bU8w%3D%3D [following]
--2021-08-22 17:55:25-- https://storage.googleapis.com/cos-osf-prod-files-de-1/1ef8b465325d04f0b87ed06fe7813ec6698d1a9a45e3511e885ae45e732f9bb8?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=1629654985&Signature=TkUkKS6PQ0P%2F4HtCdmeTrFwOqFvom6yWak2CCSAxUThLY%2FKSnIwDSfxuGAMuzSh44%2BJkn4DmjttJoQKzHuhR%2Bsm%2B9q9Sbi%2BVCn55LBdgZtkHZeK58%2BHJFVv25xdxiaSFDuZpr4MZPSrawke846r%2Bgl8CBTZdr%2BXc8NCiYmIQh%2FgVnfFX%2BxxeEHirGHnqBxwqzPrlXDEnzxd6HRv31c6ICvbnAjWe%2Fa1vNFPPx68jzl%2BwAhcPIGJcEBzFHDX1JWaS%2FpaTxCSKOeFRxxbJQulw58DHcZD4dH3Qb%2BfZqz%2Bmiyvudy2slkOWFDRmEPuqe2JxdO186gtUkXfzV5h5Q9bU8w%3D%3D
Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.4.208, 142.250.191.112, 142.250.191.144, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.4.208|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 31491651 (30M) [application/octet-stream]
Saving to: ‘scarf_datasets/tenx_5K_pbmc_rnaseq/data.zarr.tar.gz’
0K .......... .......... .......... .......... .......... 0% 1.39M 22s
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250K .......... .......... .......... .......... .......... 0% 11.0M 8s
300K .......... .......... .......... .......... .......... 1% 10.9M 7s
350K .......... .......... .......... .......... .......... 1% 16.6M 7s
400K .......... .......... .......... .......... .......... 1% 15.6M 6s
450K .......... .......... .......... .......... .......... 1% 18.2M 6s
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900K .......... .......... .......... .......... .......... 3% 44.7M 3s
950K .......... .......... .......... .......... .......... 3% 31.8M 3s
1000K .......... .......... .......... .......... .......... 3% 26.9M 3s
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1100K .......... .......... .......... .......... .......... 3% 87.5M 3s
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1200K .......... .......... .......... .......... .......... 4% 39.8M 3s
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1350K .......... .......... .......... .......... .......... 4% 115M 3s
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2150K .......... .......... .......... .......... .......... 7% 59.3M 2s
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2350K .......... .......... .......... .......... .......... 7% 144M 2s
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2450K .......... .......... .......... .......... .......... 8% 183M 2s
2500K .......... .......... .......... .......... .......... 8% 136M 1s
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3000K .......... .......... .......... .......... .......... 9% 62.8M 1s
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5150K .......... .......... .......... .......... .......... 16% 225M 1s
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5450K .......... .......... .......... .......... .......... 17% 223M 1s
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5600K .......... .......... .......... .......... .......... 18% 171M 1s
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7200K .......... .......... .......... .......... .......... 23% 3.97M 1s
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8000K .......... .......... .......... .......... .......... 26% 190M 1s
8050K .......... .......... .......... .......... .......... 26% 171M 1s
8100K .......... .......... .......... .......... .......... 26% 176M 1s
8150K .......... .......... .......... .......... .......... 26% 414K 1s
8200K .......... .......... .......... .......... .......... 26% 6.24M 1s
8250K .......... .......... .......... .......... .......... 26% 7.19M 1s
8300K .......... .......... .......... .......... .......... 27% 34.2M 1s
8350K .......... .......... .......... .......... .......... 27% 35.3M 1s
8400K .......... .......... .......... .......... .......... 27% 71.2M 1s
8450K .......... .......... .......... .......... .......... 27% 222M 1s
8500K .......... .......... .......... .......... .......... 27% 61.5M 1s
8550K .......... .......... .......... .......... .......... 27% 37.0M 1s
8600K .......... .......... .......... .......... .......... 28% 128M 1s
8650K .......... .......... .......... .......... .......... 28% 86.2M 1s
8700K .......... .......... .......... .......... .......... 28% 169M 1s
8750K .......... .......... .......... .......... .......... 28% 104M 1s
8800K .......... .......... .......... .......... .......... 28% 117M 1s
8850K .......... .......... .......... .......... .......... 28% 46.4M 1s
8900K .......... .......... .......... .......... .......... 29% 6.46M 1s
8950K .......... .......... .......... .......... .......... 29% 236M 1s
9000K .......... .......... .......... .......... .......... 29% 212M 1s
9050K .......... .......... .......... .......... .......... 29% 245M 1s
9100K .......... .......... .......... .......... .......... 29% 238M 1s
9150K .......... .......... .......... .......... .......... 29% 211M 1s
9200K .......... .......... .......... .......... .......... 30% 201M 1s
9250K .......... .......... .......... .......... .......... 30% 234M 1s
9300K .......... .......... .......... .......... .......... 30% 214M 1s
9350K .......... .......... .......... .......... .......... 30% 231M 1s
9400K .......... .......... .......... .......... .......... 30% 214M 1s
9450K .......... .......... .......... .......... .......... 30% 227M 1s
9500K .......... .......... .......... .......... .......... 31% 240M 1s
9550K .......... .......... .......... .......... .......... 31% 227M 1s
9600K .......... .......... .......... .......... .......... 31% 193M 1s
9650K .......... .......... .......... .......... .......... 31% 196M 1s
9700K .......... .......... .......... .......... .......... 31% 227M 1s
9750K .......... .......... .......... .......... .......... 31% 221M 1s
9800K .......... .......... .......... .......... .......... 32% 195M 1s
9850K .......... .......... .......... .......... .......... 32% 236M 1s
9900K .......... .......... .......... .......... .......... 32% 227M 1s
9950K .......... .......... .......... .......... .......... 32% 221M 1s
10000K .......... .......... .......... .......... .......... 32% 132M 1s
10050K .......... .......... .......... .......... .......... 32% 107M 1s
10100K .......... .......... .......... .......... .......... 33% 215M 1s
10150K .......... .......... .......... .......... .......... 33% 238M 1s
10200K .......... .......... .......... .......... .......... 33% 141M 1s
10250K .......... .......... .......... .......... .......... 33% 219M 1s
10300K .......... .......... .......... .......... .......... 33% 244M 1s
10350K .......... .......... .......... .......... .......... 33% 233M 1s
10400K .......... .......... .......... .......... .......... 33% 191M 1s
10450K .......... .......... .......... .......... .......... 34% 123M 1s
10500K .......... .......... .......... .......... .......... 34% 221M 1s
10550K .......... .......... .......... .......... .......... 34% 235M 1s
10600K .......... .......... .......... .......... .......... 34% 219M 1s
10650K .......... .......... .......... .......... .......... 34% 224M 1s
10700K .......... .......... .......... .......... .......... 34% 238M 1s
10750K .......... .......... .......... .......... .......... 35% 218M 1s
10800K .......... .......... .......... .......... .......... 35% 130M 1s
10850K .......... .......... .......... .......... .......... 35% 229M 1s
10900K .......... .......... .......... .......... .......... 35% 221M 1s
10950K .......... .......... .......... .......... .......... 35% 243M 1s
11000K .......... .......... .......... .......... .......... 35% 8.81M 1s
11050K .......... .......... .......... .......... .......... 36% 10.1M 1s
11100K .......... .......... .......... .......... .......... 36% 15.7M 1s
11150K .......... .......... .......... .......... .......... 36% 21.3M 1s
11200K .......... .......... .......... .......... .......... 36% 165M 1s
11250K .......... .......... .......... .......... .......... 36% 133M 1s
11300K .......... .......... .......... .......... .......... 36% 229M 1s
11350K .......... .......... .......... .......... .......... 37% 159M 1s
11400K .......... .......... .......... .......... .......... 37% 192M 1s
11450K .......... .......... .......... .......... .......... 37% 236M 1s
11500K .......... .......... .......... .......... .......... 37% 230M 1s
11550K .......... .......... .......... .......... .......... 37% 7.18M 1s
11600K .......... .......... .......... .......... .......... 37% 25.0M 1s
11650K .......... .......... .......... .......... .......... 38% 18.2M 1s
11700K .......... .......... .......... .......... .......... 38% 112M 1s
11750K .......... .......... .......... .......... .......... 38% 235M 1s
11800K .......... .......... .......... .......... .......... 38% 149M 1s
11850K .......... .......... .......... .......... .......... 38% 213M 1s
11900K .......... .......... .......... .......... .......... 38% 235M 1s
11950K .......... .......... .......... .......... .......... 39% 165M 1s
12000K .......... .......... .......... .......... .......... 39% 183M 1s
12050K .......... .......... .......... .......... .......... 39% 226M 1s
12100K .......... .......... .......... .......... .......... 39% 109M 1s
12150K .......... .......... .......... .......... .......... 39% 207M 1s
12200K .......... .......... .......... .......... .......... 39% 142M 1s
12250K .......... .......... .......... .......... .......... 39% 217M 1s
12300K .......... .......... .......... .......... .......... 40% 181M 1s
12350K .......... .......... .......... .......... .......... 40% 143M 1s
12400K .......... .......... .......... .......... .......... 40% 213M 1s
12450K .......... .......... .......... .......... .......... 40% 210M 1s
12500K .......... .......... .......... .......... .......... 40% 5.69M 1s
12550K .......... .......... .......... .......... .......... 40% 202M 1s
12600K .......... .......... .......... .......... .......... 41% 121M 1s
12650K .......... .......... .......... .......... .......... 41% 222M 1s
12700K .......... .......... .......... .......... .......... 41% 144M 1s
12750K .......... .......... .......... .......... .......... 41% 182M 1s
12800K .......... .......... .......... .......... .......... 41% 225M 1s
12850K .......... .......... .......... .......... .......... 41% 227M 1s
12900K .......... .......... .......... .......... .......... 42% 212M 1s
12950K .......... .......... .......... .......... .......... 42% 182M 1s
13000K .......... .......... .......... .......... .......... 42% 222M 1s
13050K .......... .......... .......... .......... .......... 42% 209M 1s
13100K .......... .......... .......... .......... .......... 42% 111M 1s
13150K .......... .......... .......... .......... .......... 42% 119M 1s
13200K .......... .......... .......... .......... .......... 43% 216M 1s
13250K .......... .......... .......... .......... .......... 43% 220M 1s
13300K .......... .......... .......... .......... .......... 43% 216M 1s
13350K .......... .......... .......... .......... .......... 43% 191M 1s
13400K .......... .......... .......... .......... .......... 43% 227M 1s
13450K .......... .......... .......... .......... .......... 43% 182M 1s
13500K .......... .......... .......... .......... .......... 44% 168M 1s
13550K .......... .......... .......... .......... .......... 44% 175M 1s
13600K .......... .......... .......... .......... .......... 44% 91.3M 1s
13650K .......... .......... .......... .......... .......... 44% 208M 1s
13700K .......... .......... .......... .......... .......... 44% 213M 1s
13750K .......... .......... .......... .......... .......... 44% 140M 1s
13800K .......... .......... .......... .......... .......... 45% 221M 1s
13850K .......... .......... .......... .......... .......... 45% 149M 1s
13900K .......... .......... .......... .......... .......... 45% 199M 1s
13950K .......... .......... .......... .......... .......... 45% 181M 1s
14000K .......... .......... .......... .......... .......... 45% 115M 1s
14050K .......... .......... .......... .......... .......... 45% 221M 1s
14100K .......... .......... .......... .......... .......... 46% 144M 1s
14150K .......... .......... .......... .......... .......... 46% 189M 1s
14200K .......... .......... .......... .......... .......... 46% 208M 1s
14250K .......... .......... .......... .......... .......... 46% 149M 1s
14300K .......... .......... .......... .......... .......... 46% 7.51M 1s
14350K .......... .......... .......... .......... .......... 46% 10.7M 1s
14400K .......... .......... .......... .......... .......... 46% 17.2M 1s
14450K .......... .......... .......... .......... .......... 47% 201M 1s
14500K .......... .......... .......... .......... .......... 47% 222M 1s
14550K .......... .......... .......... .......... .......... 47% 112M 1s
14600K .......... .......... .......... .......... .......... 47% 218M 1s
14650K .......... .......... .......... .......... .......... 47% 150M 1s
14700K .......... .......... .......... .......... .......... 47% 216M 1s
14750K .......... .......... .......... .......... .......... 48% 182M 1s
14800K .......... .......... .......... .......... .......... 48% 111M 1s
14850K .......... .......... .......... .......... .......... 48% 209M 1s
14900K .......... .......... .......... .......... .......... 48% 139M 1s
14950K .......... .......... .......... .......... .......... 48% 191M 1s
15000K .......... .......... .......... .......... .......... 48% 224M 1s
15050K .......... .......... .......... .......... .......... 49% 154M 1s
15100K .......... .......... .......... .......... .......... 49% 202M 1s
15150K .......... .......... .......... .......... .......... 49% 180M 1s
15200K .......... .......... .......... .......... .......... 49% 103M 1s
15250K .......... .......... .......... .......... .......... 49% 13.0M 1s
15300K .......... .......... .......... .......... .......... 49% 10.9M 1s
15350K .......... .......... .......... .......... .......... 50% 9.89M 1s
15400K .......... .......... .......... .......... .......... 50% 34.7M 1s
15450K .......... .......... .......... .......... .......... 50% 34.8M 1s
15500K .......... .......... .......... .......... .......... 50% 34.7M 1s
15550K .......... .......... .......... .......... .......... 50% 30.7M 1s
15600K .......... .......... .......... .......... .......... 50% 198M 1s
15650K .......... .......... .......... .......... .......... 51% 232M 1s
15700K .......... .......... .......... .......... .......... 51% 204M 1s
15750K .......... .......... .......... .......... .......... 51% 191M 1s
15800K .......... .......... .......... .......... .......... 51% 226M 1s
15850K .......... .......... .......... .......... .......... 51% 219M 1s
15900K .......... .......... .......... .......... .......... 51% 203M 1s
15950K .......... .......... .......... .......... .......... 52% 189M 1s
16000K .......... .......... .......... .......... .......... 52% 200M 1s
16050K .......... .......... .......... .......... .......... 52% 213M 0s
16100K .......... .......... .......... .......... .......... 52% 218M 0s
16150K .......... .......... .......... .......... .......... 52% 189M 0s
16200K .......... .......... .......... .......... .......... 52% 214M 0s
16250K .......... .......... .......... .......... .......... 53% 213M 0s
16300K .......... .......... .......... .......... .......... 53% 211M 0s
16350K .......... .......... .......... .......... .......... 53% 820K 1s
16400K .......... .......... .......... .......... .......... 53% 40.7M 1s
16450K .......... .......... .......... .......... .......... 53% 28.7M 1s
16500K .......... .......... .......... .......... .......... 53% 53.3M 1s
16550K .......... .......... .......... .......... .......... 53% 40.3M 1s
16600K .......... .......... .......... .......... .......... 54% 28.3M 1s
16650K .......... .......... .......... .......... .......... 54% 37.9M 1s
16700K .......... .......... .......... .......... .......... 54% 62.4M 1s
16750K .......... .......... .......... .......... .......... 54% 31.0M 1s
16800K .......... .......... .......... .......... .......... 54% 32.9M 1s
16850K .......... .......... .......... .......... .......... 54% 32.1M 1s
16900K .......... .......... .......... .......... .......... 55% 66.4M 1s
16950K .......... .......... .......... .......... .......... 55% 211M 1s
17000K .......... .......... .......... .......... .......... 55% 170M 1s
17050K .......... .......... .......... .......... .......... 55% 203M 0s
17100K .......... .......... .......... .......... .......... 55% 220M 0s
17150K .......... .......... .......... .......... .......... 55% 216M 0s
17200K .......... .......... .......... .......... .......... 56% 192M 0s
17250K .......... .......... .......... .......... .......... 56% 208M 0s
17300K .......... .......... .......... .......... .......... 56% 240M 0s
17350K .......... .......... .......... .......... .......... 56% 222M 0s
17400K .......... .......... .......... .......... .......... 56% 197M 0s
17450K .......... .......... .......... .......... .......... 56% 237M 0s
17500K .......... .......... .......... .......... .......... 57% 221M 0s
17550K .......... .......... .......... .......... .......... 57% 239M 0s
17600K .......... .......... .......... .......... .......... 57% 193M 0s
17650K .......... .......... .......... .......... .......... 57% 208M 0s
17700K .......... .......... .......... .......... .......... 57% 246M 0s
17750K .......... .......... .......... .......... .......... 57% 227M 0s
17800K .......... .......... .......... .......... .......... 58% 191M 0s
17850K .......... .......... .......... .......... .......... 58% 221M 0s
17900K .......... .......... .......... .......... .......... 58% 235M 0s
17950K .......... .......... .......... .......... .......... 58% 196M 0s
18000K .......... .......... .......... .......... .......... 58% 10.2M 0s
18050K .......... .......... .......... .......... .......... 58% 78.1M 0s
18100K .......... .......... .......... .......... .......... 59% 20.0M 0s
18150K .......... .......... .......... .......... .......... 59% 33.4M 0s
18200K .......... .......... .......... .......... .......... 59% 31.4M 0s
18250K .......... .......... .......... .......... .......... 59% 52.4M 0s
18300K .......... .......... .......... .......... .......... 59% 48.0M 0s
18350K .......... .......... .......... .......... .......... 59% 19.0M 0s
18400K .......... .......... .......... .......... .......... 59% 26.0M 0s
18450K .......... .......... .......... .......... .......... 60% 27.4M 0s
18500K .......... .......... .......... .......... .......... 60% 30.0M 0s
18550K .......... .......... .......... .......... .......... 60% 55.3M 0s
18600K .......... .......... .......... .......... .......... 60% 175M 0s
18650K .......... .......... .......... .......... .......... 60% 213M 0s
18700K .......... .......... .......... .......... .......... 60% 233M 0s
18750K .......... .......... .......... .......... .......... 61% 227M 0s
18800K .......... .......... .......... .......... .......... 61% 178M 0s
18850K .......... .......... .......... .......... .......... 61% 225M 0s
18900K .......... .......... .......... .......... .......... 61% 225M 0s
18950K .......... .......... .......... .......... .......... 61% 229M 0s
19000K .......... .......... .......... .......... .......... 61% 195M 0s
19050K .......... .......... .......... .......... .......... 62% 222M 0s
19100K .......... .......... .......... .......... .......... 62% 228M 0s
19150K .......... .......... .......... .......... .......... 62% 229M 0s
19200K .......... .......... .......... .......... .......... 62% 187M 0s
19250K .......... .......... .......... .......... .......... 62% 16.8M 0s
19300K .......... .......... .......... .......... .......... 62% 53.5M 0s
19350K .......... .......... .......... .......... .......... 63% 29.5M 0s
19400K .......... .......... .......... .......... .......... 63% 25.5M 0s
19450K .......... .......... .......... .......... .......... 63% 38.6M 0s
19500K .......... .......... .......... .......... .......... 63% 24.8M 0s
19550K .......... .......... .......... .......... .......... 63% 166M 0s
19600K .......... .......... .......... .......... .......... 63% 53.9M 0s
19650K .......... .......... .......... .......... .......... 64% 223M 0s
19700K .......... .......... .......... .......... .......... 64% 210M 0s
19750K .......... .......... .......... .......... .......... 64% 219M 0s
19800K .......... .......... .......... .......... .......... 64% 201M 0s
19850K .......... .......... .......... .......... .......... 64% 231M 0s
19900K .......... .......... .......... .......... .......... 64% 244M 0s
19950K .......... .......... .......... .......... .......... 65% 231M 0s
20000K .......... .......... .......... .......... .......... 65% 186M 0s
20050K .......... .......... .......... .......... .......... 65% 247M 0s
20100K .......... .......... .......... .......... .......... 65% 227M 0s
20150K .......... .......... .......... .......... .......... 65% 220M 0s
20200K .......... .......... .......... .......... .......... 65% 213M 0s
20250K .......... .......... .......... .......... .......... 66% 228M 0s
20300K .......... .......... .......... .......... .......... 66% 227M 0s
20350K .......... .......... .......... .......... .......... 66% 237M 0s
20400K .......... .......... .......... .......... .......... 66% 187M 0s
20450K .......... .......... .......... .......... .......... 66% 224M 0s
20500K .......... .......... .......... .......... .......... 66% 238M 0s
20550K .......... .......... .......... .......... .......... 66% 230M 0s
20600K .......... .......... .......... .......... .......... 67% 79.3M 0s
20650K .......... .......... .......... .......... .......... 67% 32.0M 0s
20700K .......... .......... .......... .......... .......... 67% 84.4M 0s
20750K .......... .......... .......... .......... .......... 67% 185M 0s
20800K .......... .......... .......... .......... .......... 67% 194M 0s
20850K .......... .......... .......... .......... .......... 67% 242M 0s
20900K .......... .......... .......... .......... .......... 68% 192M 0s
20950K .......... .......... .......... .......... .......... 68% 194M 0s
21000K .......... .......... .......... .......... .......... 68% 80.1M 0s
21050K .......... .......... .......... .......... .......... 68% 23.1M 0s
21100K .......... .......... .......... .......... .......... 68% 32.5M 0s
21150K .......... .......... .......... .......... .......... 68% 15.6M 0s
21200K .......... .......... .......... .......... .......... 69% 26.0M 0s
21250K .......... .......... .......... .......... .......... 69% 40.9M 0s
21300K .......... .......... .......... .......... .......... 69% 201M 0s
21350K .......... .......... .......... .......... .......... 69% 190M 0s
21400K .......... .......... .......... .......... .......... 69% 206M 0s
21450K .......... .......... .......... .......... .......... 69% 214M 0s
21500K .......... .......... .......... .......... .......... 70% 213M 0s
21550K .......... .......... .......... .......... .......... 70% 173M 0s
21600K .......... .......... .......... .......... .......... 70% 203M 0s
21650K .......... .......... .......... .......... .......... 70% 16.6M 0s
21700K .......... .......... .......... .......... .......... 70% 24.4M 0s
21750K .......... .......... .......... .......... .......... 70% 29.8M 0s
21800K .......... .......... .......... .......... .......... 71% 89.7M 0s
21850K .......... .......... .......... .......... .......... 71% 191M 0s
21900K .......... .......... .......... .......... .......... 71% 214M 0s
21950K .......... .......... .......... .......... .......... 71% 182M 0s
22000K .......... .......... .......... .......... .......... 71% 181M 0s
22050K .......... .......... .......... .......... .......... 71% 200M 0s
22100K .......... .......... .......... .......... .......... 72% 216M 0s
22150K .......... .......... .......... .......... .......... 72% 181M 0s
22200K .......... .......... .......... .......... .......... 72% 218M 0s
22250K .......... .......... .......... .......... .......... 72% 212M 0s
22300K .......... .......... .......... .......... .......... 72% 218M 0s
22350K .......... .......... .......... .......... .......... 72% 179M 0s
22400K .......... .......... .......... .......... .......... 72% 210M 0s
22450K .......... .......... .......... .......... .......... 73% 204M 0s
22500K .......... .......... .......... .......... .......... 73% 210M 0s
22550K .......... .......... .......... .......... .......... 73% 194M 0s
22600K .......... .......... .......... .......... .......... 73% 219M 0s
22650K .......... .......... .......... .......... .......... 73% 199M 0s
22700K .......... .......... .......... .......... .......... 73% 219M 0s
22750K .......... .......... .......... .......... .......... 74% 182M 0s
22800K .......... .......... .......... .......... .......... 74% 187M 0s
22850K .......... .......... .......... .......... .......... 74% 84.2M 0s
22900K .......... .......... .......... .......... .......... 74% 89.7M 0s
22950K .......... .......... .......... .......... .......... 74% 185M 0s
23000K .......... .......... .......... .......... .......... 74% 213M 0s
23050K .......... .......... .......... .......... .......... 75% 221M 0s
23100K .......... .......... .......... .......... .......... 75% 197M 0s
23150K .......... .......... .......... .......... .......... 75% 181M 0s
23200K .......... .......... .......... .......... .......... 75% 220M 0s
23250K .......... .......... .......... .......... .......... 75% 188M 0s
23300K .......... .......... .......... .......... .......... 75% 216M 0s
23350K .......... .......... .......... .......... .......... 76% 196M 0s
23400K .......... .......... .......... .......... .......... 76% 209M 0s
23450K .......... .......... .......... .......... .......... 76% 209M 0s
23500K .......... .......... .......... .......... .......... 76% 51.3M 0s
23550K .......... .......... .......... .......... .......... 76% 162M 0s
23600K .......... .......... .......... .......... .......... 76% 83.8M 0s
23650K .......... .......... .......... .......... .......... 77% 203M 0s
23700K .......... .......... .......... .......... .......... 77% 32.6M 0s
23750K .......... .......... .......... .......... .......... 77% 72.6M 0s
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23850K .......... .......... .......... .......... .......... 77% 213M 0s
23900K .......... .......... .......... .......... .......... 77% 76.3M 0s
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24050K .......... .......... .......... .......... .......... 78% 79.2M 0s
24100K .......... .......... .......... .......... .......... 78% 203M 0s
24150K .......... .......... .......... .......... .......... 78% 53.8M 0s
24200K .......... .......... .......... .......... .......... 78% 107M 0s
24250K .......... .......... .......... .......... .......... 79% 75.6M 0s
24300K .......... .......... .......... .......... .......... 79% 216M 0s
24350K .......... .......... .......... .......... .......... 79% 55.1M 0s
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24500K .......... .......... .......... .......... .......... 79% 62.5M 0s
24550K .......... .......... ......
INFO: Download finished! File saved here: scarf_datasets/tenx_5K_pbmc_rnaseq/data.zarr.tar.gz
time: 10.2 s (started: 2021-08-22 17:55:17 +00:00)
.... .......... .......... 79% 222K 0s
24600K .......... .......... .......... .......... .......... 80% 75.4M 0s
24650K .......... .......... .......... .......... .......... 80% 93.0M 0s
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25050K .......... .......... .......... .......... .......... 81% 70.3M 0s
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25250K .......... .......... .......... .......... .......... 82% 189M 0s
25300K .......... .......... .......... .......... .......... 82% 75.9M 0s
25350K .......... .......... .......... .......... .......... 82% 47.4M 0s
25400K .......... .......... .......... .......... .......... 82% 85.8M 0s
25450K .......... .......... .......... .......... .......... 82% 230M 0s
25500K .......... .......... .......... .......... .......... 83% 93.6M 0s
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25650K .......... .......... .......... .......... .......... 83% 95.3M 0s
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25750K .......... .......... .......... .......... .......... 83% 70.6M 0s
25800K .......... .......... .......... .......... .......... 84% 58.3M 0s
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30700K .......... .......... .......... .......... .......... 99% 235M 0s
30750K ... 100% 64.2M=1.1s
2021-08-22 17:55:27 (28.2 MB/s) - ‘scarf_datasets/tenx_5K_pbmc_rnaseq/data.zarr.tar.gz’ saved [31491651/31491651]
1) Format conversion¶
The first step of the analysis workflow is to convert the file into the Zarr format that is supported by Scarf. We read in the data using CrH5Reader (stands for cellranger H5 reader). The reader object allows quick investigation of the file before the format is converted.
[3]:
reader = scarf.CrH5Reader('scarf_datasets/tenx_5K_pbmc_rnaseq/data.h5', 'rna')
time: 53.9 ms (started: 2021-08-22 17:55:27 +00:00)
We can quickly check the number of cells and features (genes as well as ADT features in this case) present in the file.
[4]:
reader.nCells, reader.nFeatures
[4]:
(5025, 33538)
time: 2.51 ms (started: 2021-08-22 17:55:27 +00:00)
Next we convert the data to the Zarr format that will later on be used by Scarf. For this we use Scarf’s CrToZarr class. This class will first quickly ascertain the type of data to be written and then create a Zarr format file for the data to be written into. CrToZarr takes two mandatory arguments. The first is the cellranger reader, and the other is the name of the output file.
NOTE: When we say zarr file, we actually mean zarr directory because, unlike HDF5, Zarr hierarchy is organized as a directory structure.
[5]:
writer = scarf.CrToZarr(reader, zarr_fn='scarf_datasets/tenx_5K_pbmc_rnaseq/data.zarr',
chunk_size=(2000, 1000))
writer.dump(batch_size=1000)
100%|██████████| 6/6 [00:06<00:00, 1.08s/it]
time: 6.68 s (started: 2021-08-22 17:55:27 +00:00)
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. 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. When we create a DataStore
instance, we can decide to filter out low abundance genes with parameter min_features_per_cell. For example the value of 10 for min_features_per_cell below means that those genes that are present in less than 10 cells will be filtered out.
[6]:
ds = scarf.DataStore('scarf_datasets/tenx_5K_pbmc_rnaseq/data.zarr',
nthreads=4, min_features_per_cell=10)
INFO: Setting assay RNA to assay type: RNAassay
INFO: (RNA) Computing nCells and dropOuts
[########################################] | 100% Completed | 1.3s
INFO: (RNA) Computing nCounts
[########################################] | 100% Completed | 1.2s
WARNING: Minimum cell count (502) is lower than size factor multiplier (1000)
INFO: (RNA) Computing nFeatures
[########################################] | 100% Completed | 1.3s
INFO: Computing percentage of RNA_percentMito
[########################################] | 100% Completed | 1.0s
INFO: Computing percentage of RNA_percentRibo
[########################################] | 100% Completed | 1.0s
time: 6.55 s (started: 2021-08-22 17:55:34 +00:00)
2) Cell filtering¶
We can visualize the per-cell statistics in violin plots before we start filtering cells out.
[7]:
ds.plot_cells_dists()
time: 2.06 s (started: 2021-08-22 17:55:40 +00:00)
We can filter cells based on these cell attributes by providing upper and lower threshold values.
[8]:
ds.filter_cells(attrs=['RNA_nCounts', 'RNA_nFeatures', 'RNA_percentMito'],
highs=[15000, 4000, 15],
lows=[1000, 500, 0])
INFO: 597 cells flagged for filtering out using attribute RNA_nCounts
INFO: 461 cells flagged for filtering out using attribute RNA_nFeatures
INFO: 612 cells flagged for filtering out using attribute RNA_percentMito
time: 14.3 ms (started: 2021-08-22 17:55:42 +00:00)
Now we visualize the attributes again after filtering the values.
Note: the ‘I’ value given as the ``cell_key`` attribute signifies the column of the table that is set to ``False`` for cells that were filtered out or ``True`` for cells that are kept.
[9]:
ds.plot_cells_dists(cell_key='I', color='coral')
time: 1.2 s (started: 2021-08-22 17:55:42 +00:00)
The data stored under the ‘cellData’ level can easily be accessed using the cells attribute of the DataStore object.
[10]:
ds.cells.head()
[10]:
| I | ids | names | RNA_nCounts | RNA_nFeatures | RNA_percentMito | RNA_percentRibo | |
|---|---|---|---|---|---|---|---|
| 0 | True | AAACCCAAGCGTATGG-1 | AAACCCAAGCGTATGG-1 | 13537.0 | 3503.0 | 10.844353 | 16.783630 |
| 1 | True | AAACCCAGTCCTACAA-1 | AAACCCAGTCCTACAA-1 | 12668.0 | 3381.0 | 5.975687 | 20.034733 |
| 2 | False | AAACCCATCACCTCAC-1 | AAACCCATCACCTCAC-1 | 962.0 | 346.0 | 53.430353 | 2.494802 |
| 3 | True | AAACGCTAGGGCATGT-1 | AAACGCTAGGGCATGT-1 | 5788.0 | 1799.0 | 10.919143 | 28.783690 |
| 4 | True | AAACGCTGTAGGTACG-1 | AAACGCTGTAGGTACG-1 | 13186.0 | 2887.0 | 7.955407 | 35.750038 |
time: 34.3 ms (started: 2021-08-22 17:55:44 +00:00)
NOTE: We strongly discourage directly adding or removing the data from this table as Scarf will not be able to synchronize the changes to the disk. Instead use the methods of the cells attribute. Please refer to the insert, fetch, fetch_all, drop and update_key methods.
3) Feature selection¶
Similar to the cell table, Scarf also saves the feature level data that can be accessed as below:
[11]:
ds.RNA.feats.head()
[11]:
| I | ids | names | dropOuts | nCells | |
|---|---|---|---|---|---|
| 0 | False | ENSG00000243485 | MIR1302-2HG | 5025 | 0 |
| 1 | False | ENSG00000237613 | FAM138A | 5025 | 0 |
| 2 | False | ENSG00000186092 | OR4F5 | 5025 | 0 |
| 3 | True | ENSG00000238009 | AL627309.1 | 4976 | 49 |
| 4 | False | ENSG00000239945 | AL627309.3 | 5022 | 3 |
time: 23.1 ms (started: 2021-08-22 17:55:44 +00:00)
The feature selection step is performed on the normalized data. The default normalization method for RNAassay-type data is library-size normalization, wherein the count values are divided by the sum of total values for a cell. These values are then multiplied by a scalar factor. The default value of this scalar factor is 1000. However, if the total counts in a cell are less than this value, then on multiplication with this scalar factor the values will be ‘scaled up’ (which is not a desired
behaviour). In the filtering step above, we set the low threshold for RNA_nCounts at 1000, and hence it is safe to use 1000 as a scalar factor. The scalar factor can be set by modifying the sf attribute of the assay. Let’s print the default value of sf
[12]:
ds.RNA.sf
[12]:
1000
time: 3.22 ms (started: 2021-08-22 17:55:44 +00:00)
Now the next step is to identify the highly variable genes in the dataset (for the RNA assay). This can be done using the mark_hvgs method of the assay. The parameters govern the min/max variance (corrected) and mean expression threshold for calling genes highly variable.
The variance is corrected by first dividing genes into bins based on their mean expression values. Genes with minimum variance is selected from each bin and a Lowess curve is fitted to the mean-variance trend of these genes. mark_hvgs will by default run on the default assay.
A plot is produced, that for each gene shows the corrected variance on the y-axis and the non-zero mean (means from cells where the gene had a non-zero value) on the x-axis. The genes are colored in two gradients which indicate the number of cells where the gene was expressed. The colors are yellow to dark red for HVGs, and blue to green for non-HVGs.
The mark_hvgs function has a parameter cell_key that dictates which cells to use to identify the HVGs. The default value of this parameter is I, which means it will use all the cells that were not filtered out.
[13]:
ds.mark_hvgs(min_cells=20, top_n=500, min_mean=-3, max_mean=2, max_var=6)
INFO: (RNA) Computing nCells
[########################################] | 100% Completed | 2.0s
INFO: (RNA) Computing normed_tot
[########################################] | 100% Completed | 2.1s
INFO: (RNA) Computing sigmas
[########################################] | 100% Completed | 2.2s
INFO: 497 genes marked as HVGs
time: 9.84 s (started: 2021-08-22 17:55:44 +00:00)
As a result of running mark_hvgs, the feature table now has an extra column **I__hvgs** which contains a True value for genes marked HVGs. The naming rule in Scarf dictates that cells used to identify HVGs are prepended to the column name (with a double underscore delimiter). Since we did not provide any cell_key parameter the default value was used, i.e. the filtered cells. This resulted in I becoming the prefix.
[14]:
ds.RNA.feats.head()
[14]:
| I | ids | names | I__hvgs | dropOuts | nCells | stats_I_avg | stats_I_c_var__200__0.1 | stats_I_normed_n | stats_I_normed_tot | stats_I_nz_mean | stats_I_sigmas | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | False | ENSG00000243485 | MIR1302-2HG | False | 5025 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | False | ENSG00000237613 | FAM138A | False | 5025 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | False | ENSG00000186092 | OR4F5 | False | 5025 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | True | ENSG00000238009 | AL627309.1 | False | 4976 | 49 | 0.000914 | 1.555434 | 33.0 | 4.593283 | 0.13919 | 0.000196 |
| 4 | False | ENSG00000239945 | AL627309.3 | False | 5022 | 3 | NaN | NaN | NaN | NaN | NaN | NaN |
time: 42.1 ms (started: 2021-08-22 17:55:54 +00:00)
4) Graph creation¶
Creating a neighbourhood graph of cells is the most critical step in any Scarf workflow. This step internally involves multiple substeps:
data normalization for selected features
linear dimensionality reduction using PCA
creating an approximate nearest neighbour graph index (using the HNSWlib library)
querying cells against this index to identify nearest neighbours for each cell
edge weight computation using the
compute_membership_strengthsfunction from the UMAP packagefitting MiniBatch Kmeans (The kmeans centers are used later, for UMAP initialization)
make_graph method is responsible for graph construction. It method takes a mandatory parameter: feat_key. This should be a column in the feature metadata table that indicates which genes to use to create the graph. Since, we have already identified the hvgs in the step above, we use those genes. Note that we do not need to write *I__hvgs* but just hvgs as the value of the parameter. We also supply values for two very important parameters here: k (number of nearest
neighbours to be queried for each cell) and dims (number of PCA dimensions to use for graph construction). n_centroids parameter controls number of clusters to create for the data using Kmeans algorithm. We perform a more accurate clustering of data in the later steps.
[15]:
ds.make_graph(feat_key='hvgs', k=11, dims=15, 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
INFO: Normalizing with feature subset
[########################################] | 100% Completed | 1.0s
Writing data to normed__I__hvgs/data: 100%|██████████| 3/3 [00:01<00:00, 1.73it/s]
INFO: Calculating mean of norm. data
[########################################] | 100% Completed | 0.1s
INFO: Calculating std. dev. of norm. data
[ ] | 0% Completed | 0.0s
[########################################] | 100% Completed | 0.1s
Fitting PCA: 100%|██████████| 2/2 [00:00<00:00, 2.45it/s]
Fitting ANN: 100%|██████████| 2/2 [00:00<00:00, 4.10it/s]
Fitting kmeans: 100%|██████████| 2/2 [00:00<00:00, 3.10it/s]
Estimating seed partitions: 100%|██████████| 2/2 [00:00<00:00, 5.26it/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%|██████████| 2/2 [00:00<00:00, 4.87it/s]
INFO: ANN recall: 99.92%
Smoothening KNN distances: 100%|██████████| 1/1 [00:04<00:00, 4.04s/it]
time: 12.2 s (started: 2021-08-22 17:55:54 +00:00)
5) Low dimensional embedding and clustering¶
Next we run UMAP on the graph calculated above. Here we will not provide which cell key or feature key to be used, because we want the UMAP to run on all the cells that were not filtered out and with the feature key used to calculate the latest graph. We can provide the parameter values for the UMAP algorithm here.
[16]:
ds.run_umap(fit_n_epochs=250, spread=5, min_dist=1, parallel=True)
/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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time: 21.9 s (started: 2021-08-22 17:56:06 +00:00)
The UMAP results are saved in the cell metadata table as seen below in columns: RNA_UMAP1 and RNA_UMAP2
[17]:
ds.cells.head()
[17]:
| I | ids | names | RNA_UMAP1 | RNA_UMAP2 | RNA_nCounts | RNA_nFeatures | RNA_percentMito | RNA_percentRibo | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | True | AAACCCAAGCGTATGG-1 | AAACCCAAGCGTATGG-1 | 8.368184 | 18.407541 | 13537.0 | 3503.0 | 10.844353 | 16.783630 |
| 1 | True | AAACCCAGTCCTACAA-1 | AAACCCAGTCCTACAA-1 | 20.305178 | 30.461788 | 12668.0 | 3381.0 | 5.975687 | 20.034733 |
| 2 | False | AAACCCATCACCTCAC-1 | AAACCCATCACCTCAC-1 | NaN | NaN | 962.0 | 346.0 | 53.430353 | 2.494802 |
| 3 | True | AAACGCTAGGGCATGT-1 | AAACGCTAGGGCATGT-1 | -16.047424 | 32.967999 | 5788.0 | 1799.0 | 10.919143 | 28.783690 |
| 4 | True | AAACGCTGTAGGTACG-1 | AAACGCTGTAGGTACG-1 | -9.697846 | 10.873372 | 13186.0 | 2887.0 | 7.955407 | 35.750038 |
time: 32.7 ms (started: 2021-08-22 17:56:28 +00:00)
plot_layout is a versatile method to create a scatter plot using Scarf. Here we can plot the UMAP coordinates of all the non-filtered out cells.
[18]:
ds.plot_layout(layout_key='RNA_UMAP')
time: 390 ms (started: 2021-08-22 17:56:28 +00:00)
plot_layout can be used to easily visualize data from any column of the cell metadata table. Next, we visualize the number of genes expressed in each cell.
[19]:
ds.plot_layout(layout_key='RNA_UMAP', color_by='RNA_nCounts', cmap='coolwarm')
time: 1.21 s (started: 2021-08-22 17:56:28 +00:00)
There has been a lot of discussion over the choice of non-linear dimensionality reduction for single-cell data. tSNE was initially considered an excellent solution, but has gradually lost out to UMAP because the magnitude of relations between the clusters cannot easily be discerned in a tSNE plot. Scarf contains an implementation of tSNE that runs directly on the graph structure of cells. So, essentially the same data that was used to create the UMAP and clustering is used.
[20]:
ds.run_tsne(alpha=10, box_h=1, early_iter=250, max_iter=500, parallel=True)
Saving KNN matrix in MTX format: 100%|██████████| 4/4 [00:00<00:00, 20.30it/s]
ERROR: SG-tSNE failed, possibly due to missing libraries or file permissions. SG-tSNE currently fails on readthedocs
time: 276 ms (started: 2021-08-22 17:56:29 +00:00)
sgtsne: error while loading shared libraries: libmetis.so.5: cannot open shared object file: No such file or directory
NOTE: The tSNE implementation is currently not supported on Windows.
[21]:
# Here we run plot_layout under exception catching because if you are not on Linux then the `run_tnse` would have failed.
try:
ds.plot_layout(layout_key='RNA_tSNE')
except KeyError:
print ("'RNA_tSNE1' not found in MetaData")
'RNA_tSNE1' not found in MetaData
time: 3.93 ms (started: 2021-08-22 17:56:30 +00:00)
6) Cell clustering¶
Identifying clusters of cells is one of the central tenets of single cell approaches. Scarf includes two graph clustering methods and any (or even both) can be used on the dataset. The methods start with the same graph as the UMAP algorithm above to minimize the disparity between the UMAP and clustering results. The two clustering methods are:
Paris: This is the default clustering algorithm.
Leiden: Leiden is a widely used graph clustering algorithm in single-cell genomics.
Paris is the default algorithm in Scarf due to its ability to highlight cluster relationships. Paris is a hierarchical graph clustering algorithm that is based on node pair sampling. Paris creates a dendrogram of cells which can then be cut to obtain desired number of clusters. The advantage of using Paris, especially in the larger datasets, is that once the dendrogram has been created one can change the desired number of clusters with minimal computation overhead.
[22]:
# We start with Leiden clustering
ds.run_leiden_clustering(resolution=0.5)
time: 159 ms (started: 2021-08-22 17:56:30 +00:00)
We can visualize the results using the plot_layout method again. Here we plot both UMAP and colour cells based on the their cluster identity, as obtained using Leiden clustering.
[23]:
ds.plot_layout(layout_key='RNA_UMAP', color_by='RNA_leiden_cluster')
time: 812 ms (started: 2021-08-22 17:56:30 +00:00)
The results of the clustering algorithm are saved in the cell metadata table. In this case, they have been saved under the column name RNA_leiden_cluster.
[24]:
ds.cells.head()
[24]:
| I | ids | names | RNA_UMAP1 | RNA_UMAP2 | RNA_leiden_cluster | RNA_nCounts | RNA_nFeatures | RNA_percentMito | RNA_percentRibo | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | True | AAACCCAAGCGTATGG-1 | AAACCCAAGCGTATGG-1 | 8.368184 | 18.407541 | 2 | 13537.0 | 3503.0 | 10.844353 | 16.783630 |
| 1 | True | AAACCCAGTCCTACAA-1 | AAACCCAGTCCTACAA-1 | 20.305178 | 30.461788 | 2 | 12668.0 | 3381.0 | 5.975687 | 20.034733 |
| 2 | False | AAACCCATCACCTCAC-1 | AAACCCATCACCTCAC-1 | NaN | NaN | -1 | 962.0 | 346.0 | 53.430353 | 2.494802 |
| 3 | True | AAACGCTAGGGCATGT-1 | AAACGCTAGGGCATGT-1 | -16.047424 | 32.967999 | 7 | 5788.0 | 1799.0 | 10.919143 | 28.783690 |
| 4 | True | AAACGCTGTAGGTACG-1 | AAACGCTGTAGGTACG-1 | -9.697846 | 10.873372 | 1 | 13186.0 | 2887.0 | 7.955407 | 35.750038 |
time: 38 ms (started: 2021-08-22 17:56:31 +00:00)
We can export the Leiden cluster information into a pandas dataframe. Setting key to I makes sure that we do not include the data for cells that were filtered out.
[25]:
leiden_clusters = ds.cells.to_pandas_dataframe(['RNA_leiden_cluster'], key='I')
leiden_clusters.head()
[25]:
| RNA_leiden_cluster | |
|---|---|
| 0 | 2 |
| 1 | 2 |
| 3 | 7 |
| 4 | 1 |
| 6 | 5 |
time: 10.5 ms (started: 2021-08-22 17:56:31 +00:00)
Now we run Paris clustering algorithm using run_clustering method. Paris clustering requires only one parameter: n_clusters, which determines the number of clusters to create. Here we set the number of clusters same as that were obtained using Leiden clustering.leiden_clusters.nunique()
[26]:
ds.run_clustering(n_clusters=leiden_clusters.nunique()[0])
time: 505 ms (started: 2021-08-22 17:56:31 +00:00)
Visualizing Paris clusters
[27]:
ds.plot_layout(layout_key='RNA_UMAP', color_by='RNA_cluster')
time: 868 ms (started: 2021-08-22 17:56:31 +00:00)
Discerning similarity between clusters can be difficult from visual inspection alone, especially for tSNE plots. plot_cluster_tree function plots the relationship between clusters as a binary tree. This tree is simply a condensation of the dendrogram obtained using Paris clustering.
[28]:
ds.plot_cluster_tree(cluster_key='RNA_cluster', width=1)
Constructing graph from dendrogram: 100%|██████████| 3939/3939 [00:00<00:00, 58036.08it/s]
Identifying the top node for cluster: 100%|██████████| 13/13 [00:00<00:00, 365.01it/s]
time: 993 ms (started: 2021-08-22 17:56:32 +00:00)
The tree is free form (i.e the position of clusters doesn’t convey any meaning) but allows inspection of cluster similarity based on branching pattern. The sizes of clusters indicate the number of cells present in each cluster. The tree starts from the root node (black dot with no incoming edges).
7) Marker gene identification¶
Now we can identify the genes that are differentially expressed between the clusters using the run_marker_search method. The method to identify the differentially expressed genes in Scarf is optimized to obtain quick results. We have not compared the sensitivity of our method to other differential expression detecting methods. We expect specialized methods to be more sensitive and accurate to varying degrees. Our method is designed to quickly obtain key marker genes for populations from a
large dataset. For each gene individually, following steps are carried out:
Expression values are converted to ranks (dense format) across cells.
A mean of ranks is calculated for each group of cells
The mean value for each group is divided by the sum of mean values to obtain the ‘specificity score’
The gene is saved as a marker gene if it’s specificity score is higher than a given threshold.
This method does not perform any statistical test of significance and uses ‘specificity score’ as a measure of importance of each gene for a cluster.
[29]:
ds.run_marker_search(group_key='RNA_cluster', threshold=0.25)
Finding markers: 100%|██████████| 272/272 [00:26<00:00, 10.24it/s]
time: 26.9 s (started: 2021-08-22 17:56:33 +00:00)
Using the plot_marker_heatmap method, we can also plot a heatmap with the top marker genes from each cluster. The method will calculate the mean expression value for each gene from each cluster.
[30]:
ds.plot_marker_heatmap(group_key='RNA_cluster', topn=5, figsize=(5, 9))
time: 3.11 s (started: 2021-08-22 17:57:00 +00:00)
The markers list for specific clusters can be obtained like this:
[31]:
ds.get_markers(group_key='RNA_cluster', group_id='1')
[31]:
| score | names | |
|---|---|---|
| ids | ||
| ENSG00000121552 | 0.949142 | CSTA |
| ENSG00000170458 | 0.947154 | CD14 |
| ENSG00000106565 | 0.945043 | TMEM176B |
| ENSG00000197249 | 0.945023 | SERPINA1 |
| ENSG00000116701 | 0.943234 | NCF2 |
| ... | ... | ... |
| ENSG00000144802 | 0.251001 | NFKBIZ |
| ENSG00000163661 | 0.250964 | PTX3 |
| ENSG00000100605 | 0.250697 | ITPK1 |
| ENSG00000182095 | 0.250591 | TNRC18 |
| ENSG00000163131 | 0.250493 | CTSS |
655 rows × 2 columns
time: 116 ms (started: 2021-08-22 17:57:03 +00:00)
We can directly visualize the expression values for a gene of interest. It is usually a good idea to visually confirm the gene expression pattern across the cells atleast this way.
[32]:
ds.plot_layout(layout_key='RNA_UMAP', color_by='CD14')
time: 1.41 s (started: 2021-08-22 17:57:03 +00:00)
That is all for this vignette.