plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="") title(main="B. Filtered data", xlab="Log-cpm") abline(v=lcpm.cutoff, lty=3)for(iin2:nsamples){ den<-density(lcpm[,i]) lines(den$x, den$y, col=col[i], lwd=2) } legend("top...
par(mfrow=c(1,2)) # par(mfrow=c(1,2))实现一页多图的功能,其中,通过设定函数par()的各个参数来调整我们的图形,mfrow=c(2,2) 就是画4幅图,mfrow=c(3,5),是画15幅图, plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="") title(main="A. ...
Density plots correspond to the pie charts in Fig. 5e. Extended Data Fig 8 Activation and isolation of the EMT-II program. a, Average expression log-ratio for the gene-sets representing each RHP (each row represents an RHP), upon treatment of SCC47 or JHU006 with multiple perturbations (...
(B) Density plots of intron rate for lncRNA and protein-coding genes. (C) Relationship between intron rate and intron length. (D) Relationship between intron rate and the number of introns in a gene. The intron rate was calculated as follows. First, the number of reads mapped to intronic...
对象数据中变量之间的相互关系可以通过DensityScatter()函数来直观展示。此外,设置quantiles=TRUE选项,可以帮助我们迅速确定不同质量控制指标的适宜阈值。 DensityScatter(pbmc, x = 'nCount_ATAC', y = 'TSS.enrichment', log_x = TRUE, quantiles = TRUE) ...
#Regenerate these plots using a density scatter plot plotCor2 = function(lib1, lib2, name, color){ x=gene_expression[,lib1] y=gene_expression[,lib2] zero_count = length(which(x==0)) + length(which(y==0)) colors = colorRampPalette(c("white", "blue", "#007FFF", "cyan","#...
(D) Density plots of the likelihoods (solid lines, scaled to integrate to 1) and the posteriors (dashed lines) for the green and purple genes and of the prior (solid black line): due to the higher dispersion of the purple gene, its likelihood is wider and less peaked (indicating less...
对象数据中变量之间的相互关系可以通过 DensityScatter() 函数来直观展示。此外,设置 quantiles=TRUE 选项,可以帮助我们迅速确定不同质量控制指标的适宜阈值。 代码语言:javascript 复制 DensityScatter(pbmc, x = 'nCount_ATAC', y = 'TSS.enrichment', log_x = TRUE, quantiles = TRUE) 代码语言:javascript 复...
Similarly, density plots for features commonly measured in all approaches have been plotted against their respective reference per cell type (Fig. S1). 2.8. Bioinformatics For classification of proteins or genes, full id sets for each of the three techniques (RNA, PROT, SOMA) were submitted to...
# dittoPlot() 是主函数,dittoRidgePlot() 和 dittoBoxPlot() 本质上只是将输入图的默认值从 c(“jitter”, “vlnplot”) 调整为c(“ridgeplot”) 或 or c(“boxplot”,“jitter”)dittoPlot(sce,"ENO1",group.by="label",plots=c("jitter","vlnplot","boxplot"),#<-order matters# change the ...