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公司做网站走什么费,360建筑网中级机械工程师招聘,儿童编程培训班,巢湖有没有专门做网站的公司Clustering介绍分组变量将样本的观测值划分为可以相互比较属性的组。例如#xff0c;观测值的分组可以是聚类算法的结果或手动空间分割的结果。本教程将展示如何在SPATA2中应用和添加聚类。# load required packages library(SPATA2) library(SPATAData) library(tidyverse)ob…Clustering介绍分组变量将样本的观测值划分为可以相互比较属性的组。例如观测值的分组可以是聚类算法的结果或手动空间分割的结果。本教程将展示如何在SPATA2中应用和添加聚类。# load required packages library(SPATA2) library(SPATAData) library(tidyverse)object_t269- readRDS(object_t269.rds) object_t269 - updateSpataObject(object_t269)# plot histology plotSurface(object_t269, color_by histology, pt_clrp npg)plotImage(object_t269)2. SPATA2 内的聚类有许多算法可以将您的样本分成子组。SPATA2 提供了多种聚类算法的封装。那些会立即将结果添加到 SPATA2 对象中的聚类算法其名称以 run-* 开头并以 *-Clustering() 结尾。例如runBayesSpaceClustering()、runKmeansClustering()、runSeuratClustering()。参数名称或命名指定了输出分组变量的名称。可通过getGroupingOptions()获取结果分组变量名称。# current grouping options getGroupingOptions(object_t269)## factor factor factor factor ## tissue_section seurat_clusters histology bayes_space# run the pipeline object_t269 - runBayesSpaceClustering( object object_t269, name bayes_spacev2, # the name of the output grouping variable qs 5 )# run PCA based on which clustering is conducted object_t269 - runPCA(object_t269, n_pcs 20) object_t269 - runKmeansClustering( object object_t269, ks c(7, 8), methods_kmeans Lloyd )# results are immediately stored in the objects feature data getGroupingOptions(object_t269)## factor factor factor factor ## tissue_section seurat_clusters histology bayes_space ## factor factor factor ## bayes_spacev2 Lloyd_k7 Lloyd_k8# left plot plotSurface( object object_t269, color_by bayes_spacev2, pt_clrp uc )# right plot plotSurface( object object_t269, color_by Lloyd_k7, pt_clrp jco )3. SPATA2以外的聚类聚类可能由多种聚类算法产生。如果这些算法未在SPATA2函数中实现可以使用addFeatures()函数将它们添加进去。唯一的要求是有一个名为barcodes的变量用于将组映射到观测值。请注意变量必须为因子类factor class才能被识别为分组变量。# uses kmeans outside of SPATA2 kmeans_res - stats::kmeans( x getPcaMtr(object_t269), centers 7, algorithm Hartigan-Wong ) head(kmeans_res[[cluster]])## GTAGCGCTGTTGTAGT-1 TTGTTTGTGTAAATTC-1 CGTAGCGCCGACGTTG-1 GTAGACAACCGATGAA-1 ## 2 4 4 4 ## ACAGATTAGGTTAGTG-1 TGAGATCAAATACTCA-1 ## 2 2cluster_df - as.data.frame(kmeans_res[[cluster]]) %% tibble::rownames_to_column(var barcodes) %% magrittr::set_colnames(value c(barcodes, kmeans_4_HW)) %% tibble::as_tibble() cluster_df[[kmeans_4_HW]] - as.factor(cluster_df[[kmeans_4_HW]]) cluster_df## # A tibble: 3,213 × 2 ## barcodes kmeans_4_HW ## chr fct ## 1 GTAGCGCTGTTGTAGT-1 2 ## 2 TTGTTTGTGTAAATTC-1 4 ## 3 CGTAGCGCCGACGTTG-1 4 ## 4 GTAGACAACCGATGAA-1 4 ## 5 ACAGATTAGGTTAGTG-1 2 ## 6 TGAGATCAAATACTCA-1 2 ## 7 CTGGTCCTAACTTGGC-1 2 ## 8 TGCACGAGTCGGCAGC-1 3 ## 9 ATAGTCTTTGACGTGC-1 2 ## 10 GGGTGGTCCAGCCTGT-1 3 ## # ℹ 3,203 more rows# grouping options before adding getGroupingOptions(object_t269)## factor factor factor factor ## tissue_section seurat_clusters histology bayes_space ## factor factor factor ## bayes_spacev2 Lloyd_k7 Lloyd_k8# add the cluster results to the meta features object_t269 - addFeatures( object object_t269, feature_df cluster_df ) # grouping options names afterwards getGroupingOptions(object_t269)## factor factor factor factor ## tissue_section seurat_clusters histology bayes_space ## factor factor factor factor ## bayes_spacev2 Lloyd_k7 Lloyd_k8 kmeans_4_HW继续通过可视化结果或使用差异表达分析DEA来研究其转录特征。plotSurface( object object_t269, color_by kmeans_4_HW, pt_clrp jama ) labs(color Kmeans HW)