Question

我在治疗后的不同时间点获得了RNAseq数据。在这里，您可以找到该表的一部分。

> View(cluster2)
> cluster2
       rownames  Sample  expression
21        gene1 Sample1 -0.71692047
95        gene2 Sample1 -1.60358087
112       gene3 Sample1  0.29476156
113       gene4 Sample1  0.52390367
136       gene5 Sample1 -0.47093500
148       gene6 Sample1 -0.99902406
151       gene7 Sample1 -0.77891900
229       gene8 Sample1 -1.03649513
252       gene9 Sample1 -1.06392805
260      gene10 Sample1 -1.04305028
14932     gene1 Sample2  0.11824518
15006     gene2 Sample2 -0.06375086
15023     gene3 Sample2 -0.15769900
15024     gene4 Sample2 -0.94928544
15047     gene5 Sample2 -0.41254223
15059     gene6 Sample2 -0.45855777
15062     gene7 Sample2 -0.36056022
15140     gene8 Sample2  0.45096154
15163     gene9 Sample2  0.67248080
15171    gene10 Sample2 -0.59566009
29843     gene1 Sample3  0.29759959
29917     gene2 Sample3  0.48258443
29934     gene3 Sample3 -0.40674145
29935     gene4 Sample3 -1.03206336
29958     gene5 Sample3 -0.37866722
29970     gene6 Sample3 -0.37689157
29973     gene7 Sample3 -0.35649119
30051     gene8 Sample3 -0.31226370
30074     gene9 Sample3 -0.49334391
30082    gene10 Sample3 -0.36080332
44754     gene1 Sample4  0.78247333
44828     gene2 Sample4  1.64665427
44845     gene3 Sample4  1.72461980
44846     gene4 Sample4  0.12393858
44869     gene5 Sample4  0.30088996
44881     gene6 Sample4  1.73211193
44884     gene7 Sample4  0.39511615
44962     gene8 Sample4  1.69006925
44985     gene9 Sample4  0.94181113
44993    gene10 Sample4 -0.34747890
59665     gene1 Sample5  1.93571973
59739     gene2 Sample5  0.91504315
59756     gene3 Sample5  1.17766958
59757     gene4 Sample5  1.99293585
59780     gene5 Sample5  2.38539543
59792     gene6 Sample5  1.21697049
59795     gene7 Sample5  2.33208184
59873     gene8 Sample5  1.15438869
59896     gene9 Sample5  1.22935604
59904    gene10 Sample5  1.85440229
74576     gene1 Sample6 -0.58694546
74650     gene2 Sample6 -0.54178347
74667     gene3 Sample6 -0.70252704
74668     gene4 Sample6  0.41926725
74691     gene5 Sample6 -0.40225920
74703     gene6 Sample6  0.33670711
74706     gene7 Sample6 -0.27067586
74784     gene8 Sample6 -0.84741340
74807     gene9 Sample6 -1.48216198
74815    gene10 Sample6  1.23328639
89487     gene1 Sample7 -0.86542373
89561     gene2 Sample7 -0.40143953
89578     gene3 Sample7 -1.01716492
89579     gene4 Sample7 -0.62448087
89602     gene5 Sample7 -0.50543855
89614     gene6 Sample7 -0.69509192
89617     gene7 Sample7 -0.53891822
89695     gene8 Sample7 -0.78792371
89718     gene9 Sample7 -0.43037957
89726    gene10 Sample7 -0.56034284
104398    gene1 Sample8 -0.96474816
104472    gene2 Sample8 -0.43372711
104489    gene3 Sample8 -0.91291852
104490    gene4 Sample8 -0.45421567
104513    gene5 Sample8 -0.51644320
104525    gene6 Sample8 -0.75622422
104528    gene7 Sample8 -0.42163350
104606    gene8 Sample8 -0.31132355
104629    gene9 Sample8  0.62616555
104637   gene10 Sample8 -0.18035324

我的想法是绘制具有相同表达模式的基因，所以我查看了文学，我在自然界中发现了这种伟大的表现https://www.researchgate.net/figure/Pseudotime-ordering-of-cells-reveals-genes-activated-or-repressed-early-in_fig2_261034077

我对这些基因表达进行了聚类，我得到了这些模式，但现在我想做出本文所代表的流畅表示。我用ggplot2尝试了很多东西它似乎不起作用！

所以，如果有人有想法：）

我尝试了什么：

library(ggplot2)
ti<-ggplot(cluster2) + aes(x=as.factor(cluster2$Sample), y=expression, group=rownames) +
  geom_line(size=0.7, aes(color=rownames), alpha=0.5) +
  theme(legend.position="none")
ti

给我曲线

ti<-ggplot(cluster2) + aes(x=as.factor(cluster2$Sample), y=expression, group=factor(rownames), colour="black") + geom_line(size=0.7, aes(color=rownames), alpha=0.5) + theme(legend.position="none") + geom_contour()
ti

失败，因为它需要一个“z”值

ti<-ggplot(cluster2) + aes(x=as.factor(cluster2$Sample), y=expression, group=rownames) +
  geom_line(size=0.7, aes(color=rownames), alpha=0.5) +
  geom_density2d() +
  theme(legend.position="none")
ti

似乎不起作用

Answer 1

如果这是一个沿着时间段进行采样的实验，那么我会使用geom_line将基因和geom_smooth作为趋势线。

# Extract time point from sample
cluster2$TimePoint <- as.numeric(sub("Sample", "", cluster2$Sample))

library(ggplot2)
ggplot(cluster2, aes(TimePoint, expression)) +
    geom_hline(yintercept = 0, linetype = 2, color = "red") +
    # Line for each gene
    geom_line(aes(group = rownames), size = 0.5, alpha = 0.3, color = "blue") + 
    # Trend line
    geom_smooth(size = 2, se = FALSE, color = "orange") +
    scale_x_continuous(breaks = cluster2$TimePoint) +
    theme_classic()

编辑：添加一种方法（类似）来绘制此类数据。表达水平（> 0）可视化为点颜色。

ggplot(cluster2, aes(TimePoint, expression)) +
    geom_hline(yintercept = 0, linetype = 2, color = "grey") +
    geom_line(aes(group = rownames), size = 0.5, alpha = 0.5, color = "grey90") + 
    geom_point(alpha = 0.3, aes(color = expression > 0)) + 
    geom_smooth(size = 2, se = FALSE, color = "orange") +
    scale_x_continuous(breaks = cluster2$TimePoint) +
    scale_y_continuous(limits = c(-3, 3)) +
    scale_color_manual(values = c("blue", "red"), guide = FALSE) +
    labs(title = "Expression change in cluster2",
         x = "Time point",
         y = "Expression") +
    theme_classic()

用ggplot2绘制基因表达谱

1 个答案: