1adata_ref= sc.datasets.pbmc3k_processed()# this is an earlier version of the dataset from the pbmc3k tutorial 2 3adata_ref 4AnnData object with n_obs × n_vars =2638×1838 5obs:'n_genes','percent_mito','n_counts','louvain' 6var:'n_cells' 7uns:'draw_graph','louvain','louvain...
#Cell type annotation is laborous and repetitive task, one which typically requires multiple rounds of subclustering and re-annotation.#以下展示Scanpy教程对于细胞类型注释过程中提供的帮助函数#It’s difficult to show the entirety of the process in this tutorial, but we aim to show how the tools ...
scanpy.external.pp.mnn_correct(https://scanpy.readthedocs.io/en/latest/external/scanpy.external.pp.mnn_correct.html#scanpy-external-pp-mnn-correct)应该也是可以用的。 先来看ingest,通过投射到参考数据上的PCA(或备用模型)上,将一个adata的嵌入和注释与一个参考数据集adata_ref集成在一起。该函数使用knn...
plt.savefig(dir + "02-UMAP_integration.png") 使用VAE 模型整合后的结果:整合后,可以看到下面右图中相同细胞类型都聚在了一起。 准确性: accuracy = (cvae.classifier(means).argmax(dim=-1)==full_data.obs['cell_type']).sum().item()/adata.n_obsaccuracy# 0.9195204387475289 下次见~ 参考资料 [1...
classCVAE(nn.Module):# The code is based on the scarches trVAE model# https://github.com/theislab/scarches/blob/v0.3.5/scarches/models/trvae/trvae.py# and on the pyro.ai Variational Autoencoders tutorial# http://pyro.ai/examples/vae.htmldef__init__(self,input_dim,n_conds,n_cla...
We refer to thisasymmetricdataset integration asingestingannotations from an annotated referenceadata_refinto anadatathat still lacks this annotation(跟上面介绍的一致). It is different from learning a joint representation that integrates datasets in a symmetric way asBBKNN, Scanorma, Conos, CCA(CCA是...
: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 ...
1adata_ref= sc.datasets.pbmc3k_processed()# this is an earlier version of the dataset from the pbmc3k tutorial 2 3adata_ref 4AnnData object with n_obs × n_vars =2638×1838 5obs:'n_genes','percent_mito','n_counts','louvain' ...
# and on the Variational Autoencoders tutorial # http:///examples/vae.html def __init__(self, input_dim, n_conds, n_classes, hidden_dims, latent_dim): super().__init__() self.encoder = MLP(input_dim+n_conds, hidden_dims, 2*latent_dim) # output - mean and logvar of z self...