1. Paper
Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender
2. GitHub
3. 官网
remove ambient (contaminative) RNA
使用:
https://cellbender.readthedocs.io/en/latest/usage/index.html
cellbender remove-background \ --input \ --output output.h5 \ --expected-cells \ --total-droplets-included \ --cuda
--input: the input.h5文件；通常是 Cellranger 输出的raw_feature_bc_matrix.h5
--output: the name for the output
--expected-cells: 预期捕获的细胞数量；大多数时间使用Cellranger report中的数值；示例中①位置对应的数值
--total-droplets-included:超过这个数值便为surely empty；示例中②位置对应的数值
--cuda: with this flag, GPU will be used for calculation
结果解读:
https://cellbender.readthedocs.io/en/latest/tutorial/index.html
https://cellbender.readthedocs.io/en/latest/reference/index.html#loading-outputs
output.h5: 去除ambient RNAs，保留全部的barcode后的结果
output_filtered.h5: 去除ambient RNAs；The word “filtered” means that this file contains only the droplets which were determined to have a > 50% posterior probability of containing cells.
anndata_from_h5()import cellbender as cbd scanpy_object_output = cbd.remove_background.downstream.anndata_from_h5('/path/output.h5')
output.h5文件会被加载
- 只有
--total-droplets-included这个数量的细胞会被保留（根据UMI number进行排序）
- 在
.obs中会有cell_probability一列，根据默认，只有这一列>0.5的细胞会被最终保留并作为最终分析的高质量细胞，这一结果保存在output_filtered.h5中
- 降维以后的数据存放在
.obs['gene_expression_encoding']，后续计算KNN和tSNE可以
