Our proposal of unsupervised adapted single-cell consensus clustering (adaSC3) suggests to replace the linear PCA by diffusion maps, a non-linear method that takes the transition of single cells into account. We investigate the performance of adaSC3 in terms of accuracy on the data sets of the...
We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of ...
(1)基因过滤:过滤掉表达细胞比例较少的稀有基因和表达比例特别高的ubiquitous genes (2)计算距离矩阵:如图所示3种方法分别计算细胞的距离矩阵。(得到3个距离矩阵) (3)降维:分别进行PCA和laplacian转化(此处的laplacian具体原理不了解),得到3*2个转化矩阵,然后分别选取特征值最高的前d个特征向量(比如PCA中分别选取PC...
SC3s, orSingle Cell Consensus Clustering with speed, is a package for the unsupervised clustering of single cell datasets. It is an updated version ofSC3, now reimplemented in Python, and integrated into the popular Python single cell toolkitScanpy. The algorithm has also been reengineered to al...
This function accepts a single scRNA-seq expression matrix. The encode function is used to produce multiple encodings of the data. These are separately clustered using a clustering function optionally provided by the user and produces a set of consensus clusters from these individual clusterings using...
Finally, the consensus matrix is clustered using hierarchical clustering to produce final clustering results18. However, these traditional single-cell clustering methods are not ready to take the advantage of multi-omics data to improve clustering performance and are thus not applicable to multimodal ...
State-of-the-art clustering methods, such as the k-means algorithm, have also been applied to scRNA-seq datasets, and based on this application, the single-cell consensus clustering (SC3) approach was developed [133] (Fig.1). Another category of popularly used methods for cell clustering in...
CRGs consensus clustering analysis Our comparison of LI+ or LI− cells to all other cells in each cell subset yielded DEGs. We next conducted univariate Cox analysis on the newly identified DEGs to screen for prognostic-associated genes with p-value < 0.05. We discovered 73 prognosis-rela...
To reveal the potential molecular basis driving the consensus cancer cell states, we utilized single-cell regulatory network inference and clustering (SCENIC)31 to identify the underlying regulatory network (see Methods). Generally, hc0, hc1, hc4 and hc10, referring to most cells from ER+ pati...
通过将单个细胞或单个peak的counts进行聚合分组,可以得到一个“consensus”的计数矩阵,从而减少噪声并去除binary regime的影响。在此分组下,我们可以使用二项式分布,或对于足够大的组使用相应的高斯近似分布,来对可及性特定位置的细胞进行建模。 我们可以使用aggregate_nearby_peaks函数将相隔一定距离内的count数进行求和聚集...