Comments (5)
ChuanXu1
commented on May 23, 2024
1
@ersgupta, in an AnnData, clusters are usually assumed to be string ("1" rather than 1). Your seurat_clusters is type of int. You can change type of your seurat_clusters by adata.obs['seurat_clusters'] = adata.obs['seurat_clusters'].astype(str) or adata.obs['seurat_clusters'] = adata.obs['seurat_clusters'].astype('category') before using celltypist.annotate to get the predictions.
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ChuanXu1
commented on May 23, 2024
1
I have committed a change relating to this dbfc3d1.
Now such int-type plot should be possible in (next version of) CellTypist.
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ChuanXu1
commented on May 23, 2024
@ersgupta, I have no idea of this.
If you could try df1, df2 = celltypist.plot._get_fraction_prob_df(predictions, 'seurat_clusters', 'majority_voting'), and show me how df1 and df2 look like, that will help me spot the error source.
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ersgupta
commented on May 23, 2024
@ChuanXu1 , thanks. I ran the command with 'majority_voting' and 'predicted_labels'. df1 and df2 are panda dataframes with identical dimensions (23 * 49 (majority_voting) and 29 * 49 (predicted_labels)). 29 being the # clusters in ref and 49 in query datasets.
df1
refer 32 5 28 23 2 0 8 \
pred
0 0.658318 0.606526 0.568102 0.556452 0.539310 0.535056 0.526742
1 0.016100 0.026104 0.022119 0.030242 0.035672 0.033028 0.041329
10 0.000000 0.002687 0.002328 0.002016 0.002582 0.002434 0.008914
11 0.000000 0.000768 0.001164 0.000000 0.000469 0.001043 0.004457
12 0.059034 0.027639 0.013970 0.025202 0.015724 0.043806 0.066045
13 0.000000 0.000000 0.000000 0.000000 0.000000 0.000116 0.000000
14 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
15 0.000000 0.000384 0.039581 0.000000 0.000000 0.000116 0.000000
16 0.000000 0.000000 0.000000 0.000000 0.000235 0.000116 0.000000
17 0.000000 0.000000 0.000000 0.001008 0.000000 0.000116 0.000810
18 0.001789 0.000384 0.003492 0.000000 0.000000 0.000116 0.000000
19 0.007156 0.002303 0.005821 0.001008 0.002112 0.001738 0.009319
2 0.005367 0.004607 0.005821 0.008065 0.008683 0.004404 0.003647
20 0.000000 0.000384 0.000000 0.000000 0.000000 0.000116 0.001621
21 0.000000 0.000384 0.002328 0.002016 0.000469 0.000811 0.029173
22 0.007156 0.001919 0.003492 0.002016 0.002816 0.004520 0.006483
23 0.000000 0.000384 0.001164 0.001008 0.001173 0.001275 0.003241
24 0.000000 0.000000 0.000000 0.000000 0.000000 0.000232 0.000405
25 0.000000 0.000000 0.000000 0.000000 0.000000 0.000695 0.001621
26 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
27 0.000000 0.000000 0.000000 0.000000 0.000000 0.000116 0.000000
28 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000405
3 0.028623 0.063340 0.050058 0.050403 0.075334 0.054815 0.047407
4 0.042934 0.112092 0.144354 0.117944 0.106548 0.178932 0.136953
5 0.033989 0.059885 0.065192 0.052419 0.046233 0.061073 0.043760
6 0.000000 0.001536 0.000000 0.001008 0.000704 0.000348 0.001621
7 0.000000 0.000000 0.000000 0.000000 0.000469 0.000116 0.000000
8 0.001789 0.000384 0.003492 0.002016 0.004928 0.002086 0.008104
9 0.137746 0.088292 0.067520 0.147177 0.156536 0.072778 0.057942
refer 3 16 15 ... 42 30 22 38 \
pred ...
0 0.455960 0.446793 0.404225 ... 0.0 0.002503 0.051456 0.010101
1 0.062247 0.029883 0.046479 ... 0.0 0.000000 0.012621 0.000000
10 0.014317 0.001458 0.000704 ... 0.0 0.002503 0.025243 0.015152
11 0.002801 0.001458 0.000000 ... 0.0 0.000000 0.000000 0.000000
12 0.039216 0.034257 0.013380 ... 0.0 0.002503 0.001942 0.000000
13 0.001245 0.000000 0.000000 ... 0.0 0.001252 0.000971 0.000000
14 0.000000 0.000000 0.000000 ... 0.0 0.000000 0.000000 0.000000
15 0.000622 0.000729 0.000000 ... 0.0 0.000000 0.000000 0.000000
16 0.000622 0.000000 0.000000 ... 0.0 0.000000 0.000000 0.000000
17 0.000000 0.000000 0.000000 ... 0.0 0.000000 0.054369 0.000000
18 0.000000 0.000000 0.000000 ... 0.0 0.000000 0.000000 0.000000
19 0.005291 0.006560 0.100704 ... 0.0 0.001252 0.000971 0.005051
2 0.009337 0.007289 0.007042 ... 0.0 0.002503 0.308738 0.000000
20 0.001556 0.000000 0.000000 ... 0.0 0.001252 0.001942 0.000000
21 0.017429 0.002187 0.001408 ... 0.0 0.007509 0.001942 0.000000
22 0.006536 0.024052 0.004225 ... 0.0 0.000000 0.003883 0.000000
23 0.002490 0.000729 0.000000 ... 0.0 0.000000 0.000971 0.000000
24 0.000311 0.000000 0.000000 ... 1.0 0.947434 0.000000 0.000000
25 0.000311 0.000000 0.000000 ... 0.0 0.000000 0.000000 0.000000
26 0.000000 0.000000 0.000000 ... 0.0 0.000000 0.484466 0.000000
27 0.000000 0.000000 0.000000 ... 0.0 0.000000 0.000000 0.000000
28 0.000000 0.000000 0.000000 ... 0.0 0.000000 0.000000 0.964646
3 0.066293 0.042274 0.059859 ... 0.0 0.005006 0.010680 0.005051
4 0.114535 0.214286 0.201408 ... 0.0 0.001252 0.012621 0.000000
5 0.050731 0.062682 0.074648 ... 0.0 0.002503 0.005825 0.000000
6 0.001245 0.000000 0.000000 ... 0.0 0.001252 0.000971 0.000000
7 0.000622 0.000000 0.000000 ... 0.0 0.013767 0.000000 0.000000
8 0.010271 0.003644 0.000000 ... 0.0 0.003755 0.007767 0.000000
9 0.136010 0.121720 0.085915 ... 0.0 0.003755 0.012621 0.000000
refer 6 36 21 34 9 20
pred
0 0.132435 0.000000 0.037847 0.006494 0.010412 0.0192
1 0.025377 0.000000 0.009251 0.000000 0.003037 0.0008
10 0.010309 0.000000 0.015980 0.025974 0.035575 0.0304
11 0.000793 0.000000 0.000841 0.087662 0.000000 0.0000
12 0.097938 0.000000 0.004205 0.006494 0.000000 0.0008
13 0.000000 0.000000 0.010934 0.003247 0.000000 0.0000
14 0.000000 0.000000 0.000000 0.003247 0.000000 0.0000
15 0.000397 0.000000 0.000000 0.000000 0.000434 0.0000
16 0.000397 0.000000 0.001682 0.055195 0.000000 0.0000
17 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000
18 0.011102 0.000000 0.000000 0.103896 0.000000 0.0000
19 0.000793 0.000000 0.000841 0.009740 0.001735 0.0016
2 0.010706 0.003846 0.014298 0.003247 0.001735 0.0016
20 0.000000 0.000000 0.013457 0.012987 0.000000 0.0000
21 0.001983 0.000000 0.025231 0.074675 0.000000 0.0000
22 0.020222 0.000000 0.000841 0.003247 0.000000 0.0000
23 0.000397 0.000000 0.000000 0.000000 0.000000 0.0000
24 0.000000 0.003846 0.127839 0.000000 0.000000 0.0000
25 0.000000 0.000000 0.000000 0.009740 0.000000 0.0000
26 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000
27 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000
28 0.000397 0.000000 0.000000 0.000000 0.000000 0.0000
3 0.052736 0.000000 0.013457 0.003247 0.003037 0.0024
4 0.519429 0.000000 0.005887 0.012987 0.003905 0.0064
5 0.082474 0.000000 0.003364 0.000000 0.000434 0.0008
6 0.000397 0.992308 0.000841 0.000000 0.000434 0.0000
7 0.000397 0.000000 0.708158 0.577922 0.000000 0.0000
8 0.004362 0.000000 0.002523 0.000000 0.937093 0.9312
9 0.026963 0.000000 0.002523 0.000000 0.002169 0.0048
df2
refer 32 5 28 23 2 0 8 \
pred
0 0.965809 0.948292 0.944483 0.947334 0.922767 0.948084 0.898341
1 0.886550 0.860238 0.845232 0.849214 0.831936 0.880587 0.829372
10 0.000000 0.786719 0.839367 0.359204 0.843823 0.691223 0.861046
11 0.000000 0.968777 0.969953 0.000000 0.766236 0.899411 0.683387
12 0.986399 0.928306 0.892552 0.871506 0.899762 0.936753 0.925805
13 0.000000 0.000000 0.000000 0.000000 0.000000 0.987605 0.000000
14 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
15 0.000000 0.232390 0.876529 0.000000 0.000000 0.897506 0.000000
16 0.000000 0.000000 0.000000 0.000000 0.355944 0.124632 0.000000
17 0.000000 0.000000 0.000000 0.960677 0.000000 0.985546 0.774046
18 0.820819 0.961904 0.728207 0.000000 0.000000 0.500547 0.000000
19 0.932710 0.860029 0.924264 0.614199 0.778727 0.885887 0.784612
2 0.848432 0.601666 0.631763 0.765619 0.650762 0.776042 0.546454
20 0.000000 0.198825 0.000000 0.000000 0.000000 0.382079 0.917699
21 0.000000 0.982235 0.892606 0.760580 0.960442 0.830323 0.829415
22 0.946244 0.806765 0.742423 0.956148 0.883609 0.898583 0.869285
23 0.000000 0.982128 0.750404 0.996796 0.875415 0.843705 0.798186
24 0.000000 0.000000 0.000000 0.000000 0.000000 0.977217 0.940842
25 0.000000 0.000000 0.000000 0.000000 0.000000 0.783579 0.802243
26 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
27 0.000000 0.000000 0.000000 0.000000 0.000000 0.863327 0.000000
28 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.993291
3 0.910973 0.826976 0.818633 0.747797 0.764451 0.835207 0.763416
4 0.923150 0.914572 0.937952 0.901560 0.871839 0.915423 0.879200
5 0.949060 0.884110 0.850637 0.885552 0.837089 0.885396 0.812224
6 0.000000 0.959709 0.000000 0.991092 0.559784 0.916381 0.572463
7 0.000000 0.000000 0.000000 0.000000 0.525894 0.842679 0.000000
8 0.921094 0.822523 0.954555 0.992354 0.794097 0.793627 0.894249
9 0.958550 0.941142 0.923955 0.947293 0.948092 0.941160 0.930160
refer 3 16 15 ... 42 30 22 \
pred ...
0 0.902354 0.933878 0.941323 ... 0.00000 0.999953 0.903707
1 0.866204 0.882260 0.834244 ... 0.00000 0.000000 0.893350
10 0.863571 0.709540 0.468696 ... 0.00000 0.980372 0.954141
11 0.728036 0.944513 0.000000 ... 0.00000 0.000000 0.000000
12 0.920262 0.931447 0.852006 ... 0.00000 0.999853 0.545751
13 0.746761 0.000000 0.000000 ... 0.00000 1.000000 0.705615
14 0.000000 0.000000 0.000000 ... 0.00000 0.000000 0.000000
15 0.810965 0.992943 0.000000 ... 0.00000 0.000000 0.000000
16 0.509520 0.000000 0.000000 ... 0.00000 0.000000 0.000000
17 0.000000 0.000000 0.000000 ... 0.00000 0.000000 0.959697
18 0.000000 0.000000 0.000000 ... 0.00000 0.000000 0.000000
19 0.718166 0.848843 0.810799 ... 0.00000 1.000000 0.999774
2 0.653567 0.541287 0.797159 ... 0.00000 0.649194 0.876038
20 0.692546 0.000000 0.000000 ... 0.00000 0.981132 0.907484
21 0.753680 0.926417 0.703139 ... 0.00000 0.647112 0.860861
22 0.922892 0.956881 0.864125 ... 0.00000 0.000000 0.962811
23 0.689861 0.773163 0.000000 ... 0.00000 0.000000 0.801867
24 0.945073 0.000000 0.000000 ... 0.99946 0.970967 0.000000
25 0.999784 0.000000 0.000000 ... 0.00000 0.000000 0.000000
26 0.000000 0.000000 0.000000 ... 0.00000 0.000000 0.951527
27 0.000000 0.000000 0.000000 ... 0.00000 0.000000 0.000000
28 0.000000 0.000000 0.000000 ... 0.00000 0.000000 0.000000
3 0.791107 0.849579 0.794963 ... 0.00000 0.763061 0.703529
4 0.854674 0.925256 0.900698 ... 0.00000 0.592713 0.801051
5 0.808756 0.878435 0.886966 ... 0.00000 0.682470 0.933008
6 0.673831 0.000000 0.000000 ... 0.00000 0.976354 0.997900
7 0.532620 0.000000 0.000000 ... 0.00000 0.922624 0.000000
8 0.835278 0.882204 0.000000 ... 0.00000 0.995094 0.932741
9 0.941952 0.970117 0.915104 ... 0.00000 0.782747 0.985773
refer 38 6 36 21 34 9 20
pred
0 0.978322 0.913048 0.000000 0.648222 0.471485 0.791584 0.936011
1 0.000000 0.927497 0.000000 0.603467 0.000000 0.761626 0.994911
10 0.986820 0.937392 0.000000 0.525245 0.692694 0.895098 0.921507
11 0.000000 0.926083 0.000000 0.099093 0.856720 0.000000 0.000000
12 0.000000 0.966130 0.000000 0.971357 0.850548 0.000000 0.292844
13 0.000000 0.000000 0.000000 0.837263 1.000000 0.000000 0.000000
14 0.000000 0.000000 0.000000 0.000000 0.819100 0.000000 0.000000
15 0.000000 0.999997 0.000000 0.000000 0.000000 0.076491 0.000000
16 0.000000 0.224512 0.000000 0.974763 0.998662 0.000000 0.000000
17 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
18 0.000000 0.867637 0.000000 0.000000 0.999613 0.000000 0.000000
19 1.000000 0.904442 0.000000 0.997380 0.948926 0.984928 0.701797
2 0.000000 0.755984 0.741267 0.507369 0.515607 0.633443 0.446354
20 0.000000 0.000000 0.000000 0.934429 0.996754 0.000000 0.000000
21 0.000000 0.862564 0.000000 0.484563 0.811229 0.000000 0.000000
22 0.000000 0.897069 0.000000 0.161369 0.876750 0.000000 0.000000
23 0.000000 0.899411 0.000000 0.000000 0.000000 0.000000 0.000000
24 0.000000 0.000000 0.867081 0.840746 0.000000 0.000000 0.000000
25 0.000000 0.000000 0.000000 0.000000 0.963699 0.000000 0.000000
26 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
27 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
28 0.968427 0.557817 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.545563 0.857892 0.000000 0.739810 0.624453 0.595503 0.698857
4 0.000000 0.961181 0.000000 0.756561 0.974402 0.740595 0.778212
5 0.000000 0.919500 0.000000 0.952483 0.000000 0.134070 0.749154
6 0.000000 0.429840 0.957648 0.998860 0.000000 0.196596 0.000000
7 0.000000 0.956970 0.000000 0.913621 0.960916 0.000000 0.000000
8 0.000000 0.897939 0.000000 0.388946 0.000000 0.986432 0.990842
9 0.000000 0.929880 0.000000 0.740042 0.000000 0.904171 0.919951
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ersgupta
commented on May 23, 2024
Thanks a lot. It worked just fine.
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Related Issues (20)
- " have completed preprocessing and cell clustering. " HOT 2
- codes for harmonizing the cell labels HOT 2
- the question about conf_score HOT 3
- CellTypist_Immune_Reference_v2 question HOT 2
- Immune_All_Low - model training question HOT 4
- Cluster-level label prediction HOT 1
- Running celltypist with concatenated dataset HOT 3
- Running the CellTypist training function celltypist.train on a subset of genes HOT 1
- ValueError in celltypist.annotate HOT 3
- is it available to generate model.pkl file from a marker gene list HOT 1
- Feature: Support Rapids-singlecell HOT 1
- Can not detect a neighborhood graph, will construct one before the over-clustering HOT 1
- multiple models HOT 3
- Question about over_clustering and conf_score? HOT 1
- Using model trained by scRNA-seq datasets to predict Spatial transcriptmoic dataset HOT 2
- Unable to allocate xxx GiB for an array with shape and data type float64 HOT 2
- Running `celltypist.annotate` with `min_prop` can't create "Heterogeneous" category HOT 2
- Use of metacells? HOT 4
- unable to download models HOT 3
- Feature Request: Store freqs in annotation when majority_vote set to True HOT 2
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