:func:`~scanpy.pl.rank_genes_groups_dotplot`: to plot marker genes identified using the :func:`~scanpy.tl.rank_genes_groups` function. Examples --- Create a dot plot using the given markers and the PBMC example dataset grouped by the category 'bulk_labels'. .. plot:: :context: ...
# 载入数据# Load datadf <- read.table(file = "data/data_practice3.txt", sep = "\t", header = TRUE, check.names = FALSE)# 基本堆叠柱状图# Basic stack bar plotp31 <- ggplot(df, aes(x = time, y = values, fill = genes)) + geom_col(width = 0.45, position = position_stack(...
corrected by the Gene Set Enrichment Analysis [58] approach (GSEA), which is also referred to as functional class scoring and is a rank-based threshold-free method that does not rely on differentially expressed genes to perform pathway analyses but uses all available gene expression information. ...
Finally, a user can choose not to rank the regions and just use the input order (called “none”). This is particularly useful if a user has already ranked the regions. For example, a user can rank genes by expression levels and then plot the enrichment for histone marks to see if the...
This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average ...
The maximally selected Log-Rank statistics analysis was applied to the Ki67 continuous variable in order to estimate the most appropriate cut-off values able to split patients into groups with different DFS probabilities [16]. Associations between variables and groups according to Ki67 were analyzed ...
(data)," genes")}ncol=ncol(groups)codeg<-as.character(colnames(groups))#repvect代表生物学重复的设计矩阵reps<-i.rank(repvect)y<-vector(mode="numeric",length=length(unique(reps)))x<-vector(mode="numeric",length=length(unique(reps)))g<-matrix(nrow=length(unique(reps)),ncol=ncol)#yy...
# 基本堆叠柱状图# Basic stack bar plotp31 <- ggplot(df, aes(x = time, y = values, fill = genes)) +geom_col(width =0.45, position = position_stack(vjust =1)) +scale_fill_manual(values = c("#d36e70", "#64b7d4", "#f9a648", "#72c8b4", "#f05a2c", "#4f81bd")) +sca...