隨著單細胞技術的成熟，測序成本的降低，單細胞的數據量和樣本量也日益增長。我們知道單細胞轉錄組的一個主要應用就是解釋細胞的異質性，那么，不同器官，不同測序平臺，不同物種之間的單細胞數據何如整合分析呢？特別是在單細胞的數據維度這么高的前提下，顯然傳統的基于回歸的方法已經不適用了。于是出現了一批單細胞整合分析的工具，它們大多數是在R生態條件下的。如：
在我們理解單細胞數據的時候一張cell X gene 的大表不能離開我們的腦海。
adata.to_df()
Out[24]:
RP11-34P13.3 FAM138A ... AC213203.1 FAM231B
AAACCCAAGCGTATGG-1 0.0 0.0 ... 0.0 0.0
AAACCCAGTCCTACAA-1 0.0 0.0 ... 0.0 0.0
AAACCCATCACCTCAC-1 0.0 0.0 ... 0.0 0.0
AAACGCTAGGGCATGT-1 0.0 0.0 ... 0.0 0.0
AAACGCTGTAGGTACG-1 0.0 0.0 ... 0.0 0.0
... ... ... ... ...
TTTGTTGCAGGTACGA-1 0.0 0.0 ... 0.0 0.0
TTTGTTGCAGTCTCTC-1 0.0 0.0 ... 0.0 0.0
TTTGTTGGTAATTAGG-1 0.0 0.0 ... 0.0 0.0
TTTGTTGTCCTTGGAA-1 0.0 0.0 ... 0.0 0.0
TTTGTTGTCGCACGAC-1 0.0 0.0 ... 0.0 0.0
當我們有多個樣本的時候就是有多張這樣的表，那讓我們自己手動來整合這兩張表的話，我們會怎么做呢？
肯定是行列分別對齊把它們拼在一起啊，就像拼積木一樣的，但是這樣的結果就是：
兩個樣本在圖譜上完全的分開來了。我們不同平臺的樣本，相同的細胞類型應該是在一起的啊。于是我們開始思考如何完成這樣的整合。
seurat提供了一套解決方案，就是在數據集中構建錨點，將不同數據集中相似的細胞錨在一起。
那么如何錨，選擇哪些特征來錨定，又開發出不同的算法。不管算法如何，首先我們看看這種錨定可以為我們帶來什么？相同的細胞類型mapping在一起，一個自然的作用就是用來mapping細胞類型未知的數據。
所以在scanpy中也如seurat一樣在多樣本分析中，分別給出reference的方法和整合的方法。目前在scanpy中分別是ingest和BBKNN（Batch balanced kNN），當然整合也是可以用來做reference的。scanpy.external.pp.mnn_correct應該也是可以用的。
先來看ingest，通過投射到參考數據上的PCA(或備用模型)上，將一個adata的嵌入和注釋與一個參考數據集adata_ref集成在一起。該函數使用knn分類器來映射標簽，使用UMAP來映射嵌入。
再來看看bbknn是一個快速和直觀的批處理效果去除工具，可以直接在scanpy工作流中使用。它是scanpy.api.pp.neighbors()的替代方法，這兩個函數都創建了一個鄰居圖，以便后續在集群、偽時間和UMAP可視化中使用。標準方法首先確定整個數據結構中每個單元的k個最近鄰，然后將候選單元轉換為指數相關的連接，然后作為進一步分析的基礎。
那么我們就來看一下在scanpy的實現吧。
import scanpy as sc
import pandas as pd
import seaborn as sns
import sklearn
import sys
import scipy
import bbknn
sc.settings.verbosity = 1 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_versions()
sc.settings.set_figure_params(dpi=80, frameon=False, figsize=(3, 3))
scanpy==1.4.5.1 anndata==0.7.1 umap==0.3.10 numpy==1.16.5 scipy==1.3.1 pandas==0.25.1 scikit-learn==0.21.3 statsmodels==0.10.1 python-igraph==0.8.0
ingest 注釋
adata_ref = sc.datasets.pbmc3k_processed() # this is an earlier version of the dataset from the pbmc3k tutorial
adata_ref
AnnData object with n_obs × n_vars = 2638 × 1838
obs: 'n_genes', 'percent_mito', 'n_counts', 'louvain'
var: 'n_cells'
uns: 'draw_graph', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups'
obsm: 'X_pca', 'X_tsne', 'X_umap', 'X_draw_graph_fr'
varm: 'PCs'
我們一次看看以下參考數據集都有哪些內容：
adata_ref.obs
Out[9]:
n_genes percent_mito n_counts louvain
index
AAACATACAACCAC-1 781 0.030178 2419.0 CD4 T cells
AAACATTGAGCTAC-1 1352 0.037936 4903.0 B cells
AAACATTGATCAGC-1 1131 0.008897 3147.0 CD4 T cells
AAACCGTGCTTCCG-1 960 0.017431 2639.0 CD14+ Monocytes
AAACCGTGTATGCG-1 522 0.012245 980.0 NK cells
... ... ... ...
TTTCGAACTCTCAT-1 1155 0.021104 3459.0 CD14+ Monocytes
TTTCTACTGAGGCA-1 1227 0.009294 3443.0 B cells
TTTCTACTTCCTCG-1 622 0.021971 1684.0 B cells
TTTGCATGAGAGGC-1 454 0.020548 1022.0 B cells
TTTGCATGCCTCAC-1 724 0.008065 1984.0 CD4 T cells
[2638 rows x 4 columns]
adata_ref.var
Out[10]:
n_cells
index
TNFRSF4 155
CPSF3L 202
ATAD3C 9
C1orf86 501
RER1 608
...
ICOSLG 34
SUMO3 570
SLC19A1 31
S100B 94
PRMT2 588
[1838 rows x 1 columns]
adata_ref.uns['louvain_colors']
Out[14]:
array(['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
'#e377c2', '#bcbd22'], dtype='<U7')
adata_ref.obsm
Out[16]: AxisArrays with keys: X_pca, X_tsne, X_umap, X_draw_graph_fr
adata_ref.obsm['X_umap']
Out[17]:
array([[ 1.35285574, 2.26612719],
[-0.47802448, 7.87730423],
[ 2.16588875, -0.24481226],
...,
[ 0.34670979, 8.34967798],
[ 0.19864146, 9.56698797],
[ 2.62803322, 0.36722543]])
有沒有再次理解AnnData 這個對象的數據結構呢？
可以看到在這個數據集中降維聚類都是做過的，我們可以畫個圖看看：
sc.pl.umap(adata_ref, color='louvain')
接下來我們看看要預測的數據集是怎樣的。
adata = sc.datasets.pbmc68k_reduced()
adata
AnnData object with n_obs × n_vars = 700 × 765
obs: 'bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase', 'louvain'
var: 'n_counts', 'means', 'dispersions', 'dispersions_norm', 'highly_variable'
uns: 'bulk_labels_colors', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
可見它也是降維聚類過的了。
sc.pl.umap(adata, color='louvain')
這個數據集并沒有得到細胞類型的定義。
構建注釋數據結構：
var_names = adata_ref.var_names.intersection(adata.var_names) # 取交集
adata_ref = adata_ref[:, var_names]
adata = adata[:, var_names]
sc.pp.pca(adata_ref)
sc.pp.neighbors(adata_ref)
sc.tl.umap(adata_ref)
sc.tl.leiden(adata_ref)# 新的聚類方法
sc.pl.umap(adata_ref, color=['louvain','leiden'])
adata_ref
AnnData object with n_obs × n_vars = 2638 × 208
obs: 'n_genes', 'percent_mito', 'n_counts', 'louvain', 'leiden'
var: 'n_cells'
uns: 'draw_graph', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups', 'umap', 'leiden', 'leiden_colors'
obsm: 'X_pca', 'X_tsne', 'X_umap', 'X_draw_graph_fr'
varm: 'PCs'
sc.pp.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)
sc.pl.umap(adata, color=['louvain','leiden'])
adata
AnnData object with n_obs × n_vars = 700 × 208
obs: 'bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase', 'louvain', 'leiden'
var: 'n_counts', 'means', 'dispersions', 'dispersions_norm', 'highly_variable'
uns: 'bulk_labels_colors', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups', 'umap', 'leiden', 'leiden_colors'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
用ingest來做細胞注釋吧。
sc.tl.ingest(adata, adata_ref, obs='louvain')
adata.uns['louvain_colors'] = adata_ref.uns['louvain_colors'] # fix colors
我們來看看sc.tl.ingest的幫助文檔：
Help on function ingest in module scanpy.tools._ingest:
ingest(adata: anndata._core.anndata.AnnData, adata_ref: anndata._core.anndata.AnnData, obs: Union[str, Iterable[str], NoneType] = None, embedding_method: Union[str, Iterable[str]] = ('umap', 'pca'), labeling_method: str = 'knn', inplace: bool = True, **kwargs)
Map labels and embeddings from reference data to new data.
:tutorial:`integrating-data-using-ingest`
Integrates embeddings and annotations of an `adata` with a reference dataset
`adata_ref` through projecting on a PCA (or alternate
model) that has been fitted on the reference data. The function uses a knn
classifier for mapping labels and the UMAP package [McInnes18]_ for mapping
the embeddings.
.. note::
We refer to this *asymmetric* dataset integration as *ingesting*
annotations from reference data to new data. This is different from
learning a joint representation that integrates both datasets in an
unbiased way, as CCA (e.g. in Seurat) or a conditional VAE (e.g. in
scVI) would do.
You need to run :func:`~scanpy.pp.neighbors` on `adata_ref` before
passing it.
Parameters
----------
adata
The annotated data matrix of shape `n_obs` × `n_vars`. Rows correspond
to cells and columns to genes. This is the dataset without labels and
embeddings.
adata_ref
The annotated data matrix of shape `n_obs` × `n_vars`. Rows correspond
to cells and columns to genes.
Variables (`n_vars` and `var_names`) of `adata_ref` should be the same
as in `adata`.
This is the dataset with labels and embeddings
which need to be mapped to `adata`.
obs
Labels' keys in `adata_ref.obs` which need to be mapped to `adata.obs`
(inferred for observation of `adata`).
embedding_method
Embeddings in `adata_ref` which need to be mapped to `adata`.
The only supported values are 'umap' and 'pca'.
labeling_method
The method to map labels in `adata_ref.obs` to `adata.obs`.
The only supported value is 'knn'.
inplace
Only works if `return_joint=False`.
Add labels and embeddings to the passed `adata` (if `True`)
or return a copy of `adata` with mapped embeddings and labels.
Returns
-------
* if `inplace=False` returns a copy of `adata`
with mapped embeddings and labels in `obsm` and `obs` correspondingly
* if `inplace=True` returns `None` and updates `adata.obsm` and `adata.obs`
with mapped embeddings and labels
Example
-------
Call sequence:
import scanpy as sc
sc.pp.neighbors(adata_ref)
sc.tl.umap(adata_ref)
sc.tl.ingest(adata, adata_ref, obs='cell_type')
.. _ingest PBMC tutorial: https://scanpy-tutorials.readthedocs.io/en/latest/integrating-pbmcs-using-ingest.html
.. _ingest Pancreas tutorial: https://scanpy-tutorials.readthedocs.io/en/latest/integrating-pancreas-using-ingest.html
通過比較‘bulk_label’注釋和‘louvain’注釋，我們發現數據被合理地映射，只有樹突細胞的注釋似乎是含糊不清的，在adata中可能已經是模糊的了。我們來對adata做進一步的處理。
adata_concat = adata_ref.concatenate(adata, batch_categories=['ref', 'new'])
adata_concat.obs.louvain = adata_concat.obs.louvain.astype('category')
adata_concat.obs.louvain.cat.reorder_categories(adata_ref.obs.louvain.cat.categories, inplace=True) # fix category ordering
adata_concat.uns['louvain_colors'] = adata_ref.uns['louvain_colors'] # fix category colors
adata_concat
sc.pl.umap(adata_concat, color=['batch', 'louvain'])
AnnData object with n_obs × n_vars = 3338 × 208
obs: 'G2M_score', 'S_score', 'batch', 'bulk_labels', 'leiden', 'louvain', 'n_counts', 'n_genes', 'percent_mito', 'phase'
var: 'n_cells-ref', 'n_counts-new', 'means-new', 'dispersions-new', 'dispersions_norm-new', 'highly_variable-new'
obsm: 'X_pca', 'X_umap'
雖然在單核細胞和樹突狀細胞簇中似乎存在一些批處理效應，但在其他方面，新數據被繪制得相對均勻。
巨核細胞只存在于adata_ref中，沒有來自adata映射的單元格。如果交換參考數據和查詢數據，巨核細胞不再作為單獨的集群出現。這是一個極端的情況，因為參考數據非常小;但是，人們應該始終質疑參考數據是否包含足夠的生物變異，以便有意義地容納查詢數據。
使用BBKNN整合
sc.tl.pca(adata_concat)
sc.external.pp.bbknn(adata_concat, batch_key='batch') # running bbknn 1.3.6
sc.tl.umap(adata_concat)
sc.pl.umap(adata_concat, color=['batch', 'louvain'])
adata_concat
Out[45]:
AnnData object with n_obs × n_vars = 3338 × 208
obs: 'G2M_score', 'S_score', 'batch', 'bulk_labels', 'leiden', 'louvain', 'n_counts', 'n_genes', 'percent_mito', 'phase'
var: 'n_cells-ref', 'n_counts-new', 'means-new', 'dispersions-new', 'dispersions_norm-new', 'highly_variable-new'
uns: 'batch_colors', 'louvain_colors', 'pca', 'neighbors', 'umap'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
BBKNN并不維持巨核細胞簇。然而，它似乎更均勻地混合細胞。
一個例子使用BBKNN整合數據的例子
以下數據已在scGen論文[Lotfollahi19]中使用。點擊pancreas下載數據。
它包含了來自4個不同研究(Segerstolpe16, Baron16, Wang16, Muraro16)的人類胰腺數據，這些數據在單細胞數據集集成的開創性論文(Butler18, Haghverdi18)中被使用過，并在此后多次被使用。
h5ad = 'E:\\learnscanpy\\data\\objects-pancreas\\pancreas.h5ad'
adata_all = sc.read_h5ad(h5ad)
adata_all
AnnData object with n_obs × n_vars = 14693 × 2448
obs: 'celltype', 'sample', 'n_genes', 'batch', 'n_counts', 'louvain'
var: 'n_cells-0', 'n_cells-1', 'n_cells-2', 'n_cells-3'
uns: 'celltype_colors', 'louvain', 'neighbors', 'pca', 'sample_colors'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
counts = adata_all.obs.celltype.value_counts()
counts
Out[173]:
alpha 4214
beta 3354
ductal 1804
acinar 1368
not applicable 1154
delta 917
gamma 571
endothelial 289
activated_stellate 284
dropped 178
quiescent_stellate 173
mesenchymal 80
macrophage 55
PSC 54
unclassified endocrine 41
co-expression 39
mast 32
epsilon 28
mesenchyme 27
schwann 13
t_cell 7
MHC class II 5
unclear 4
unclassified 2
Name: celltype, dtype: int64
adata_all.obs
Out[171]:
celltype sample ... n_counts louvain
index ...
human1_lib1.final_cell_0001-0 acinar Baron ... 2.241100e+04 2
human1_lib1.final_cell_0002-0 acinar Baron ... 2.794900e+04 2
human1_lib1.final_cell_0003-0 acinar Baron ... 1.689200e+04 2
human1_lib1.final_cell_0004-0 acinar Baron ... 1.929900e+04 2
human1_lib1.final_cell_0005-0 acinar Baron ... 1.506700e+04 2
... ... ... ... ...
reads.29499-3 ductal Wang ... 1.056558e+06 10
reads.29500-3 ductal Wang ... 9.926309e+05 10
reads.29501-3 beta Wang ... 1.751338e+06 10
reads.29502-3 dropped Wang ... 2.163764e+06 10
reads.29503-3 beta Wang ... 2.038979e+06 10
[14693 rows x 6 columns]
可以看出這個數據集已經降維聚類好了，所以我們可以可視化一下：
sc.pl.umap(adata_all,color=['sample', 'celltype','louvain'])
樣本之間的批次很嚴重啊。
去掉細胞數較小的小群，
minority_classes = counts.index[-5:].tolist() # get the minority classes
# ['schwann', 't_cell', 'MHC class II', 'unclear', 'unclassified']
adata_all = adata_all[ # actually subset
~adata_all.obs.celltype.isin(minority_classes)]
adata_all.obs.celltype.cat.reorder_categories( # reorder according to abundance
counts.index[:-5].tolist(), inplace=True)
adata_all.obs.celltype.value_counts()
Out[182]:
alpha 4214
beta 3354
ductal 1804
acinar 1368
not applicable 1154
delta 917
gamma 571
endothelial 289
activated_stellate 284
dropped 178
quiescent_stellate 173
mesenchymal 80
macrophage 55
PSC 54
unclassified endocrine 41
co-expression 39
mast 32
epsilon 28
mesenchyme 27
進行pca降維和umap降維：
sc.pp.pca(adata_all)
sc.pp.neighbors(adata_all)
sc.tl.umap(adata_all)
sc.pl.umap(adata_all, color=['batch', 'celltype'], palette=sc.pl.palettes.vega_20_scanpy)
下面我們使用BBKNN來整合數據：
sc.external.pp.bbknn(adata_all, batch_key='batch')
sc.tl.umap(adata_all)
adata_all
sc.pl.umap(adata_all, color=['sample','batch', 'celltype'])
果然要比原始的數據好多了。但是改變的是什么？
AnnData object with n_obs × n_vars = 14662 × 2448
obs: 'celltype', 'sample', 'n_genes', 'batch', 'n_counts', 'louvain'
var: 'n_cells-0', 'n_cells-1', 'n_cells-2', 'n_cells-3'
uns: 'celltype_colors', 'louvain', 'neighbors', 'pca', 'sample_colors', 'louvain_colors', 'umap', 'batch_colors'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
如果想對其中某個樣本進行單獨的注釋，可以用上面提到的ingest。選擇一個參考批次來訓練模型和建立鄰域圖(這里是一個PCA)，并分離出所有其他批次。
adata_ref = adata_all[adata_all.obs.batch == '0']
adata_ref
Out[191]:
View of AnnData object with n_obs × n_vars = 8549 × 2448
obs: 'celltype', 'sample', 'n_genes', 'batch', 'n_counts', 'louvain'
var: 'n_cells-0', 'n_cells-1', 'n_cells-2', 'n_cells-3'
uns: 'celltype_colors', 'louvain', 'neighbors', 'pca', 'sample_colors', 'louvain_colors', 'umap', 'batch_colors'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
sc.pp.pca(adata_ref)
sc.pp.neighbors(adata_ref)
sc.tl.umap(adata_ref)
adata_ref
Out[197]:
AnnData object with n_obs × n_vars = 8549 × 2448
obs: 'celltype', 'sample', 'n_genes', 'batch', 'n_counts', 'louvain'
var: 'n_cells-0', 'n_cells-1', 'n_cells-2', 'n_cells-3'
uns: 'celltype_colors', 'louvain', 'neighbors', 'pca', 'sample_colors', 'louvain_colors', 'umap', 'batch_colors'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
sc.pl.umap(adata_ref, color='celltype')
選取數據集用ingest于adata_ref進行mapping：
adatas = [adata_all[adata_all.obs.batch == i].copy() for i in ['1', '2', '3']]
sc.settings.verbosity = 2 # a bit more logging
for iadata, adata in enumerate(adatas):
print(f'... integrating batch {iadata+1}')
adata.obs['celltype_orig'] = adata.obs.celltype # save the original cell type
sc.tl.ingest(adata, adata_ref, obs='celltype')
integrating batch 1
running ingest
finished (0:00:08)
integrating batch 2
running ingest
finished (0:00:06)
integrating batch 3
running ingest
finished (0:00:03)
adata_concat = adata_ref.concatenate(adatas)
adata_concat
Out[200]:
AnnData object with n_obs × n_vars = 14662 × 2448
obs: 'batch', 'celltype', 'celltype_orig', 'louvain', 'n_counts', 'n_genes', 'sample'
var: 'n_cells-0', 'n_cells-1', 'n_cells-2', 'n_cells-3'
obsm: 'X_pca', 'X_umap'
adata_concat.obs.celltype = adata_concat.obs.celltype.astype('category')
adata_concat.obs.celltype.cat.reorder_categories(adata_ref.obs.celltype.cat.categories, inplace=True) # fix category ordering
adata_concat.uns['celltype_colors'] = adata_ref.uns['celltype_colors'] # fix category coloring
sc.pl.umap(adata_concat, color=['celltype_orig','batch', 'celltype'])
與BBKNN的結果相比，這是以一種更加明顯的方式保持分群。如果已經觀察到一個想要的連續結構(例如在造血數據集中)，攝取允許容易地維持這個結構。
一致性評估
adata_query = adata_concat[adata_concat.obs.batch.isin(['1', '2', '3'])]
View of AnnData object with n_obs × n_vars = 6113 × 2448
obs: 'batch', 'celltype', 'celltype_orig', 'louvain', 'n_counts', 'n_genes', 'sample'
var: 'n_cells-0', 'n_cells-1', 'n_cells-2', 'n_cells-3'
uns: 'celltype_colors', 'celltype_orig_colors', 'batch_colors'
obsm: 'X_pca', 'X_umap'
sc.pl.umap(
adata_query, color=['batch', 'celltype', 'celltype_orig'], wspace=0.4)
這個結果依然不能很好的反映一致性，讓我們首先關注與參考保守的細胞類型，以簡化混淆矩陣的reads。
obs_query = adata_query.obs
conserved_categories = obs_query.celltype.cat.categories.intersection(obs_query.celltype_orig.cat.categories) # intersected categories
obs_query_conserved = obs_query.loc[obs_query.celltype.isin(conserved_categories) & obs_query.celltype_orig.isin(conserved_categories)] # intersect categories
obs_query_conserved.celltype.cat.remove_unused_categories(inplace=True) # remove unused categoriyes
obs_query_conserved.celltype_orig.cat.remove_unused_categories(inplace=True) # remove unused categoriyes
obs_query_conserved.celltype_orig.cat.reorder_categories(obs_query_conserved.celltype.cat.categories, inplace=True) # fix category ordering
obs_query_conserved
Out[214]:
batch celltype celltype_orig ... n_counts n_genes sample
D28.1_1-1-1 1 alpha alpha ... 2.322583e+04 5448 Muraro
D28.1_13-1-1 1 ductal ductal ... 2.334263e+04 5911 Muraro
D28.1_15-1-1 1 alpha alpha ... 2.713471e+04 5918 Muraro
D28.1_17-1-1 1 alpha alpha ... 1.581207e+04 4522 Muraro
D28.1_2-1-1 1 endothelial endothelial ... 3.173151e+04 6464 Muraro
... ... ... ... ... ... ...
reads.29498-3-3 3 ductal ductal ... 1.362606e+06 19950 Wang
reads.29499-3-3 3 ductal ductal ... 1.056558e+06 19950 Wang
reads.29500-3-3 3 ductal ductal ... 9.926309e+05 19950 Wang
reads.29501-3-3 3 beta beta ... 1.751338e+06 19950 Wang
reads.29503-3-3 3 beta beta ... 2.038979e+06 19950 Wang
pd.crosstab(obs_query_conserved.celltype, obs_query_conserved.celltype_orig)
Out[215]:
celltype_orig alpha beta ductal acinar delta gamma endothelial mast
celltype
alpha 1819 3 7 0 1 20 0 5
beta 49 803 3 1 10 26 0 0
ductal 8 5 693 263 0 0 0 0
acinar 1 3 2 145 0 3 0 0
delta 5 4 0 0 305 73 0 0
gamma 1 5 0 0 0 194 0 0
endothelial 2 0 0 0 0 0 36 0
mast 0 0 1 0 0 0 0 2
總的來說，保守的細胞類型也如預期的那樣被映射。主要的例外是原始注釋中出現的一些腺泡細胞。然而，已經觀察到參考數據同時具有腺泡和導管細胞，這就解釋了差異，并指出了初始注釋中潛在的不一致性。
現在讓我們繼續看看所有的細胞類型。
pd.crosstab(adata_query.obs.celltype, adata_query.obs.celltype_orig)
Out[216]:
celltype_orig PSC acinar ... not applicable unclassified endocrine
celltype ...
alpha 0 0 ... 304 11
beta 0 1 ... 522 24
ductal 0 263 ... 106 1
acinar 0 145 ... 86 0
delta 0 0 ... 95 5
gamma 0 0 ... 14 0
endothelial 1 0 ... 7 0
activated_stellate 49 1 ... 17 0
quiescent_stellate 4 0 ... 1 0
macrophage 0 0 ... 1 0
mast 0 0 ... 1 0
[11 rows x 16 columns]
我們觀察到PSC(胰腺星狀細胞)細胞實際上只是不一致地注釋并正確地映射到“激活的星狀細胞”上。
此外，很高興看到“間充質”和“間充質”細胞都屬于同一類別。但是，這個類別又是“activated_stellate”，而且可能是錯誤的。這就是我們說的，算法只能接近真相，而不能定義真相。
可視化分布的批次
通常，批量對應的是想要比較的實驗。Scanpy提供了方便的可視化可能性，主要有
- a density plot
- a partial visualization of a subset of categories/groups in an emnbedding
sc.tl.embedding_density(adata_concat, groupby='batch')
sc.pl.embedding_density(adata_concat, groupby='batch')
for batch in ['1', '2', '3']:
sc.pl.umap(adata_concat, color='batch', groups=[batch])
BBKNN: fast batch alignment of single cell transcriptomes
integrating-data-using-ingest
