Implement SOMA-level dimension-slicing

[johnkerl]

Context: #95

Examples (see also test cases on this PR):

$ ./tools/ingestor anndata/pbmc-small.h5ad ./tiledb-data/pbmc-small
...

>>> import tiledbsc

>>> soma = tiledbsc.SOMA('./tiledb-data/pbmc-small')

>>> soma.obs.ids()
[b'AAATTCGAATCACG', b'AAGCAAGAGCTTAG', b'AAGCGACTTTGACG', b'AATGCGTGGACGGA', [snip] b'TTGCATTGAGCTAC', b'TTGGTACTGAATCC', b'TTTAGCTGTACTCT']

>>> soma.var.ids()
[b'AKR1C3', b'CA2', b'CD1C', b'GNLY', b'HLA-DPB1', b'HLA-DQA1', b'IGLL5', b'MYL9', b'PARVB', b'PF4', b'PGRMC1', b'PPBP', b'RP11-290F20.3', b'RUFY1', b'S100A8', b'S100A9', b'SDPR', b'TREML1', b'TUBB1', b'VDAC3']

>>> soma.obs.dim_select([b'AAGCGACTTTGACG', b'AATGCGTGGACGGA'])
                orig.ident  nCount_RNA  nFeature_RNA  RNA_snn_res.0.8  letter.idents groups  RNA_snn_res.1
obs_id
AAGCGACTTTGACG           0       443.0            77                1              1     g1              1
AATGCGTGGACGGA           0       389.0            73                1              1     g1              1

>>> soma.var.dim_select([b'AKR1C3', b'MYL9'])
        vst.mean  vst.variance  vst.variance.expected  vst.variance.standardized  vst.variable
var_id
AKR1C3    0.2625      1.132753               0.553424                   2.021191             1
MYL9      0.2875      1.321361               0.614125                   1.938228             1

>>> soma.obsm.keys()
['X_tsne', 'X_pca']

>>> soma.obsm['X_tsne'].dim_select([b'AAGCGACTTTGACG', b'AATGCGTGGACGGA'])
           obs_id   X_tsne_1   X_tsne_2
0  AAGCGACTTTGACG -31.919623  -0.864304
1  AATGCGTGGACGGA  -1.839384  22.204006

>>> soma.obsm['X_pca'].dim_select([b'AAGCGACTTTGACG', b'AATGCGTGGACGGA'])
           obs_id   X_pca_1   X_pca_2   X_pca_3   X_pca_4  ...  X_pca_15  X_pca_16  X_pca_17  X_pca_18  X_pca_19
0  AAGCGACTTTGACG -1.380380  1.284101  1.918055  1.247647  ...  0.117602  0.169661  0.130810 -0.041855  0.027550
1  AATGCGTGGACGGA -1.494413  1.783583  0.661433 -0.584200  ... -0.163601 -0.082049 -0.145453 -0.045108  0.055206

[2 rows x 20 columns]

>>> soma.X.data.dim_select([b'AAGCGACTTTGACG'], [b'AKR1C3'])
           obs_id  var_id     value
0  AAGCGACTTTGACG  AKR1C3 -0.325888

>>> soma.X.data.dim_select([b'AAGCGACTTTGACG'], None)
            obs_id         var_id     value
0   AAGCGACTTTGACG         AKR1C3 -0.325888
1   AAGCGACTTTGACG            CA2 -0.346938
2   AAGCGACTTTGACG           CD1C -0.253005
3   AAGCGACTTTGACG           GNLY -0.528670
4   AAGCGACTTTGACG       HLA-DPB1  0.412720
5   AAGCGACTTTGACG       HLA-DQA1 -0.671707
6   AAGCGACTTTGACG          IGLL5 -0.193495
7   AAGCGACTTTGACG           MYL9 -0.301435
8   AAGCGACTTTGACG          PARVB -0.346179
9   AAGCGACTTTGACG            PF4 -0.370707
10  AAGCGACTTTGACG         PGRMC1 -0.329147
11  AAGCGACTTTGACG           PPBP -0.422769
12  AAGCGACTTTGACG  RP11-290F20.3  2.782381
13  AAGCGACTTTGACG          RUFY1 -0.409833
14  AAGCGACTTTGACG         S100A8  1.007046
15  AAGCGACTTTGACG         S100A9  1.127458
16  AAGCGACTTTGACG           SDPR -0.388176
17  AAGCGACTTTGACG         TREML1 -0.328625
18  AAGCGACTTTGACG          TUBB1 -0.350375
19  AAGCGACTTTGACG          VDAC3 -0.524551

>>> soma.X.data.dim_select(None, [b'AKR1C3'])
            obs_id  var_id     value
0   AAATTCGAATCACG  AKR1C3 -0.325888
1   AAGCAAGAGCTTAG  AKR1C3 -0.325888
2   AAGCGACTTTGACG  AKR1C3 -0.325888
3   AATGCGTGGACGGA  AKR1C3 -0.325888
4   AATGTTGACAGTCA  AKR1C3 -0.325888
..             ...     ...       ...
75  TTACGTACGTTCAG  AKR1C3 -0.325888
76  TTGAGGACTACGCA  AKR1C3 -0.325888
77  TTGCATTGAGCTAC  AKR1C3 -0.325888
78  TTGGTACTGAATCC  AKR1C3 -0.325888
79  TTTAGCTGTACTCT  AKR1C3 -0.325888

[80 rows x 3 columns]