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前言
cfDNA（无细胞DNA，游离DNA，Circulating free DNA or Cell free DNA）是指在血液循环中存在的DNA片段。这些DNA片段不属于任何细胞，因此被称为“无细胞”或“游离”的。cfDNA来源广泛，可以来自正常细胞和病变细胞（如肿瘤细胞）的死亡和分解过程。cfDNA的长度通常在160-180碱基对左右，这与核小体保护的DNA片段长度相符。
cfDNA的研究对于非侵入性诊断、疾病监测、早期检测以及了解生理和病理状态具有重要意义。特别是在肿瘤学领域，通过分析循环肿瘤DNA（ctDNA），即来源于肿瘤细胞的cfDNA，可以获取肿瘤的遗传信息，从而指导癌症的诊断、治疗选择和治疗效果监测。
cfDNAPro
主要功能：
- 数据表征： 计算片段大小分布的整体、中位数和众数，以及片段大小轮廓中的峰和谷，还有振荡周期性。
- 数据可视化： 提供了多种函数来可视化这些数据，包括整体到单个片段的可视化、度量可视化、模式和摘要可视化等。
demo
1.片段长度可视化
- 上图：横轴表示片段长度，范围为30bp至500bp。纵轴表示具有特定读取长度的读取比例。这里的线并不是平滑曲线，而是连接不同数据点的直线。
- 下图：首先统计长度小于或等于30bp的读取数量（例如N），然后将其归一化为比例。重复这一过程，直至处理完所有片段长度（即30bp, 31bp, …, 500bp），然后以线图的形式呈现。与非累积图一样，这里的线也是连接各个数据点，而不是平滑曲线。
library(scales) library(ggpubr) library(ggplot2) library(dplyr) # Define a list for the groups/cohorts. grp_list<-list("cohort_1"="cohort_1", "cohort_2"="cohort_2", "cohort_3"="cohort_3", "cohort_4"="cohort_4") # Generating the plots and store them in a list. result<-sapply(grp_list, function(x){ result <-callSize(path = data_path) %>% dplyr::filter(group==as.character(x)) %>% plotSingleGroup() }, simplify = FALSE) #> setting default outfmt to df. #> setting default input_type to picard. #> setting default outfmt to df. #> setting default input_type to picard. #> setting default outfmt to df. #> setting default input_type to picard. #> setting default outfmt to df. #> setting default input_type to picard. # Multiplexing the plots in one figure suppressWarnings( multiplex <- ggarrange(result$cohort_1$prop_plot + theme(axis.title.x = element_blank()), result$cohort_4$prop_plot + theme(axis.title = element_blank()), result$cohort_1$cdf_plot, result$cohort_4$cdf_plot + theme(axis.title.y = element_blank()), labels = c("Cohort 1 (n=5)", "Cohort 4 (n=4)"), label.x = 0.2, ncol = 2, nrow = 2)) multiplex
2.片段长度分布比较
- callMetrics：计算了每个组的中位片段大小分布
- 上图：每个队列中位数片段大小分布的比例。y轴显示读取比例，x轴显示片段大小。图中显示的线不是平滑的曲线，而是连接不同数据点的线
- 下图：中位数累积分布函数(CDF)的图形。y轴显示累积比例，x轴仍然显示片段大小。这是一个逐步上升的图形，反映了不同片段大小下读取的累积分布情况。
# Set an order for those groups (i.e. the levels of factors). order <- c("cohort_1", "cohort_2", "cohort_3", "cohort_4") # Generate plots. compare_grps<-callMetrics(data_path) %>% plotMetrics(order=order) #> setting default input_type to picard. # Modify plots. p1<-compare_grps$median_prop_plot + ylim(c(0, 0.028)) + theme(axis.title.x = element_blank(), axis.title.y = element_text(size=12,face="bold")) + theme(legend.position = c(0.7, 0.5), legend.text = element_text( size = 11), legend.title = element_blank()) p2<-compare_grps$median_cdf_plot + scale_y_continuous(labels = scales::number_format(accuracy = 0.001)) + theme(axis.title=element_text(size=12,face="bold")) + theme(legend.position = c(0.7, 0.5), legend.text = element_text( size = 11), legend.title = element_blank()) # Finalize plots. suppressWarnings( median_grps<-ggpubr::ggarrange(p1, p2, label.x = 0.3, ncol = 1, nrow = 2 )) median_grps
3.可视化DNA片段模态长度
- 柱状图：这里的模态片段大小是指在样本中出现次数最多的DNA片段长度
# Set an order for your groups, it will affect the group order along x axis! order <- c("cohort_1", "cohort_2", "cohort_3", "cohort_4") # Generate mode bin chart. mode_bin <- callMode(data_path) %>% plotMode(order=order,hline = c(167,111,81)) #> setting default mincount as 0. #> setting default input_type to picard. # Show the plot. suppressWarnings(print(mode_bin))
- 堆叠柱状图：可以看到每个组中不同长度片段的分布
# Set an order for your groups, it will affect the group order along x axis. order <- c("cohort_1", "cohort_2", "cohort_3", "cohort_4") # Generate mode stacked bar chart. You could specify how to stratify the modes # using 'mode_partition' arguments. If other modes exist other than you # specified, an 'other' group will be added to the plot. mode_stacked <- callMode(data_path) %>% plotModeSummary(order=order, mode_partition = list(c(166,167))) #> setting default input_type to picard. # Modify the plot using ggplot syntax. mode_stacked <- mode_stacked + theme(legend.position = "top") # Show the plot. suppressWarnings(print(mode_stacked))
4.片段化振荡模式比较
- 间峰距离：通过测量和比较间距距离（峰值之间的距离），比较不同队列中的10bp周期性振荡模式
# Set an order for your groups, it will affect the group order. order <- c("cohort_1", "cohort_2", "cohort_4", "cohort_3") # Plot and modify inter-peak distances. inter_peak_dist<-callPeakDistance(path = data_path, limit = c(50, 135)) %>% plotPeakDistance(order = order) + labs(y="Fraction") + theme(axis.title = element_text(size=12,face="bold"), legend.title = element_blank(), legend.position = c(0.91, 0.5), legend.text = element_text(size = 11)) #> setting the mincount to 0. #> setting the xlim to c(7,13). #> setting default outfmt to df. #> Setting default mincount to 0. #> setting default input_type to picard. # Show the plot. suppressWarnings(print(inter_peak_dist))
- 间谷距离：与之前介绍的间峰距离可视化相比，间谷距离的可视化重点在于表示读取次数下降的区域，而不是上升的区域。这两个图表的区别在于它们关注的是碎片大小谱的不同特点，一个是峰点（即频率的局部最高点），另一个是谷点（即频率的局部最低点）。
# Set an order for your groups, it will affect the group order. order <- c("cohort_1", "cohort_2", "cohort_4", "cohort_3") # Plot and modify inter-peak distances. inter_valley_dist<-callValleyDistance(path = data_path, limit = c(50, 135)) %>% plotValleyDistance(order = order) + labs(y="Fraction") + theme(axis.title = element_text(size=12,face="bold"), legend.title = element_blank(), legend.position = c(0.91, 0.5), legend.text = element_text(size = 11)) #> setting the mincount to 0. #> setting the xlim to c(7,13). #> setting default outfmt to df. #> setting the mincount to 0. #> setting default input_type to picard. # Show the plot. suppressWarnings(print(inter_valley_dist))
5. ggplot2美化
library(ggplot2) library(cfDNAPro) # Set the path to the example sample. exam_path <- examplePath("step6") # Calculate peaks and valleys. peaks <- callPeakDistance(path = exam_path) #> setting default limit to c(35,135). #> setting default outfmt to df. #> Setting default mincount to 0. #> setting default input_type to picard. valleys <- callValleyDistance(path = exam_path) #> setting default limit to c(35,135). #> setting default outfmt to df. #> setting the mincount to 0. #> setting default input_type to picard. # A line plot showing the fragmentation pattern of the example sample. exam_plot_all <- callSize(path=exam_path) %>% plotSingleGroup(vline = NULL) #> setting default outfmt to df. #> setting default input_type to picard. # Label peaks and valleys with dashed and solid lines. exam_plot_prop <- exam_plot_all$prop + coord_cartesian(xlim = c(90,135),ylim = c(0,0.0065)) + geom_vline(xintercept=peaks$insert_size, colour="red",linetype="dashed") + geom_vline(xintercept = valleys$insert_size,colour="blue") # Show the plot. suppressWarnings(print(exam_plot_prop))
# Label peaks and valleys with dots. exam_plot_prop_dot<- exam_plot_all$prop + coord_cartesian(xlim = c(90,135),ylim = c(0,0.0065)) + geom_point(data= peaks, mapping = aes(x= insert_size, y= prop), color="blue",alpha=0.5,size=3) + geom_point(data= valleys, mapping = aes(x= insert_size, y= prop), color="red",alpha=0.5,size=3) # Show the plot. suppressWarnings(print(exam_plot_prop_dot))
想做cfDNA，迈出分析的第一步，数据表征。
