Tutorial:Single-cell RNA-seq: Annotation: Celltype auto annotation with SCSA-3
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3 months ago
Julia Ma ▴ 120

Part-1 here: Single-cell RNA-seq: Annotation: Celltype auto annotation with SCSA

Part-2 here: Single-cell RNA-seq: Annotation: Celltype auto annotation with SCSA-2

Full article lifted from: https://omicverse.readthedocs.io/en/latest/Tutorials-single/t_cellanno/


Cell annotate manually

In order to compare the accuracy of our automatic annotations, we will here use marker genes to manually annotate the cluster and compare the accuracy of the pySCSA and manual.

We need to prepare a marker's dict at first

res_marker_dict={
    'Megakaryocyte':['ITGA2B','ITGB3'],
    'Dendritic cell':['CLEC10A','IDO1'],
    'Monocyte' :['S100A8','S100A9','LST1',],
    'Macrophage':['CSF1R','CD68'],
    'B cell':['MS4A1','CD79A','MZB1',],
    'NK/NKT cell':['GNLY','KLRD1'],
    'CD8+T cell':['CD8A','CD8B'],
    'Treg':['CD4','CD40LG','IL7R','FOXP3','IL2RA'],
    'CD4+T cell':['PTPRC','CD3D','CD3E'],
}

We then calculated the expression of marker genes in each cluster and the fraction

sc.tl.dendrogram(adata,'leiden')
sc.pl.dotplot(adata, res_marker_dict, 'leiden',
              dendrogram=True,standard_scale='var')

WARNING: You’re trying to run this on 2000 dimensions of `.X`, if you really want this, set `use_rep='X'`.
         Falling back to preprocessing with `sc.pp.pca` and default params.
computing PCA
    with n_comps=50
    finished (0:00:00)
Storing dendrogram info using `.uns['dendrogram_leiden']`
WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: 0, 1, 2, etc.
var_group_labels: Megakaryocyte, Dendritic cell, Monocyte, etc.

enter image description here

Based on the dotplot, we name each cluster according ov.single.scanpy_cellanno_from_dict

# create a dictionary to map cluster to annotation label
cluster2annotation = {
     '0': 'T cell',
     '1': 'T cell',
     '2': 'Monocyte',#Germ-cell(Oid)
     '3': 'B cell',#Germ-cell(Oid)
     '4': 'T cell',
     '5': 'Macrophage',
     '6': 'NKT cells',
     '7': 'T cell',
    '8':'Monocyte',
    '9':'Dendritic cell',
    '10':'Megakaryocyte',

}
ov.single.scanpy_cellanno_from_dict(adata,anno_dict=cluster2annotation,
                                       clustertype='leiden')

...cell type added to major_celltype on obs of anndata

Compare the pySCSA and Manual

We can see that the auto-annotation results are almost identical to the manual annotation, the only difference is between monocyte and macrophages, but in the previous auto-annotation results, pySCSA gives the option of monocyte|macrophage, so it can be assumed that pySCSA performs better on the pbmc3k data

ov.utils.embedding(adata,
                   basis='X_mde',
                   color=['major_celltype','scsa_celltype_cellmarker'],
                   legend_loc='on data', frameon='small',legend_fontoutline=2,
                   palette=ov.utils.palette()[14:],
                  )

enter image description here

We can use get_celltype_marker to obtain the marker of each celltype

marker_dict=ov.single.get_celltype_marker(adata,clustertype='scsa_celltype_cellmarker')
marker_dict.keys()

...get cell type marker
ranking genes
    finished (0:00:01)

dict_keys(['B cell', 'Dendritic cell', 'Megakaryocyte', 'Monocyte', 'Natural killer cell', 'T cell'])

marker_dict['B cell']

array(['CD74', 'CD79A', 'HLA-DRA', 'CD79B', 'HLA-DPB1', 'HLA-DQA1',
       'MS4A1', 'HLA-DQB1', 'HLA-DRB1', 'CD37', 'HLA-DPA1', 'HLA-DRB5',
       'TCL1A'], dtype=object)

The tissue name in database

For annotation of cell types in specific tissues, we can query the tissues available in the database using get_model_tissue.

scsa.get_model_tissue()

Version V2.1 [2023/06/27]
DB load: GO_items:47347,Human_GO:3,Mouse_GO:3,
CellMarkers:82887,CancerSEA:1574,PanglaoDB:24223
Ensembl_HGNC:61541,Ensembl_Mouse:55414
    ########################################################################################################################
    ------------------------------------------------------------------------------------------------------------------------
    Species:Human      Num:298
    ------------------------------------------------------------------------------------------------------------------------
      1: Abdomen                              2: Abdominal adipose tissue             3: Abdominal fat pad
      4: Acinus                               5: Adipose tissue                       6: Adrenal gland
      7: Adventitia                           8: Airway                               9: Airway epithelium
     10: Allocortex                          11: Alveolus                            12: Amniotic fluid
     13: Amniotic membrane                   14: Ampullary                           15: Anogenital tract
     16: Antecubital vein                    17: Anterior cruciate ligament          18: Anterior presomitic mesoderm
     19: Aorta                               20: Aortic valve                        21: Artery
     22: Arthrosis                           23: Articular Cartilage                 24: Ascites
     25: Ascitic fluid                       26: Atrium                              27: Basal airway
     28: Basilar membrane                    29: Beige Fat                           30: Bile duct
     31: Biliary tract                       32: Bladder                             33: Blood
     34: Blood vessel                        35: Bone                                36: Bone marrow
     37: Brain                               38: Breast                              39: Bronchial vessel
     40: Bronchiole                          41: Bronchoalveolar lavage              42: Bronchoalveolar system
     43: Bronchus                            44: Brown adipose tissue                45: Calvaria
     46: Capillary                           47: Cardiac atrium                      48: Cardiovascular system
     49: Carotid artery                      50: Carotid plaque                      51: Cartilage
     52: Caudal cortex                       53: Caudal forebrain                    54: Caudal ganglionic eminence
     55: Cavernosum                          56: Central amygdala                    57: Central nervous system
     58: Cerebellum                          59: Cerebral organoid                   60: Cerebrospinal fluid
     61: Cervix                              62: Choriocapillaris                    63: Chorionic villi
     64: Chorionic villus                    65: Choroid                             66: Choroid plexus
     67: Colon                               68: Colon epithelium                    69: Colorectum
     70: Cornea                              71: Corneal endothelium                 72: Corneal epithelium
     73: Coronary artery                     74: Corpus callosum                     75: Corpus luteum
     76: Cortex                              77: Cortical layer                      78: Cortical thymus
     79: Decidua                             80: Deciduous tooth                     81: Dental pulp
     82: Dermis                              83: Diencephalon                        84: Distal airway
     85: Dorsal forebrain                    86: Dorsal root ganglion                87: Dorsolateral prefrontal cortex
     88: Ductal tissue                       89: Duodenum                            90: Ectocervix
     91: Ectoderm                            92: Embryo                              93: Embryoid body
     94: Embryonic Kidney                    95: Embryonic brain                     96: Embryonic heart
     97: Embryonic prefrontal cortex         98: Embryonic stem cell                 99: Endocardium
    100: Endocrine                          101: Endoderm                           102: Endometrium
    103: Endometrium stroma                 104: Entorhinal cortex                  105: Epidermis
    106: Epithelium                         107: Esophageal                         108: Esophagus
    109: Eye                                110: Fat pad                            111: Fetal brain
...
scRNA-seq SCSA • 241 views
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