edgeR 中的彩色 MDS 图
Colorful MDS plot in edgeR
我在 edgeR 中创建 MDS 图以可视化实验(白血病)和对照(健康供体)组的颜色时遇到问题。
我使用 htseq 文件作为 edgeR 的输入。每个文件包含两列 - gene_ID 和读取计数。 "A"代表白血病患者,"H"代表健康捐献者。
这是我的代码:
创建 table:
samples <- matrix(c("A18.txt","experiment","blood_exp",
"A19.txt","experiment","blood_exp",
"A20.txt","experiment","blood_exp",
"A23.txt","experiment","blood_exp",
"A24.txt","experiment","blood_exp",
"A26.txt","experiment","blood_exp",
"A30.txt","experiment","blood_exp",
"A37.txt","experiment","blood_exp",
"H11.txt","control","blood_control",
"H12.txt","control","blood_control",
"H13.txt","control","blood_control",
"H15.txt","control","blood_control",
"H16.txt","control","blood_control",
"H17.txt","control","blood_control",
"H18.txt","control","blood_control",
"H19.txt","control","blood_control"),
nrow = 16, ncol = 3, byrow = TRUE, dimnames = list(c(1:16), c("library_name","condition","group_ALL_vs_control")))
samples <- as.data.frame (samples, row.names = NULL, optional = FALSE, stringAsFactors = default.stringAsFactors())
使用 edgeR 函数 readDGE 读取通过 htseq-count 创建的 READS COUNT 个文件:
counts <- readDGE(samples$library_name, path = 'C:/Users/okbm4/Desktop/htseq_files', columns=c(1,2), group = samples$group_ALL_vs_control, header = FALSE)
colnames(counts) <- samples$library_name
过滤弱表达和无信息(i.e.amibigous)特征:
noint <- rownames(counts) %in% c('__no_feature','__ambiguous','__too_low_aQual','__not_aligned','__alignment_not_unique')
cpms <- cpm(counts)
keep <- rowSums (cpms > 1) >= 4 & !noint
counts <- counts[keep,]
创建 DGElist 对象
counts <- DGEList(counts=counts,group = samples$group_ALL_vs_control)
估计归一化因子,这是文库大小的归一化
counts <- calcNormFactors(counts)
使用 MDS 图检查样本之间的关系。
pdf(file = 'HCB_ALL.pdf', width = 9, height = 6)
plotMDS(counts, labels = c('A18.txt','A19.txt','A20.txt','A23.txt','A24.txt','A26.txt','A30.txt','A37.txt','H11.txt','H12.txt','H13.txt','H15.txt','H16.txt','H17.txt','H18.txt','H19.txt'),
xlab = 'Dimension 1',
ylab = 'Dimension 2',
asp = 6/9,
cex = 0.8,
main = 'Multidimentional scaling plot')
par(cex.axis =0.6, cex.lab = 0.6, cex.main = 1)
我附加了之前生成的文件。
我很乐意听到任何建议。
plotMDS()
生成一个可以传递给 plot()
的对象
是,
这样您就可以选择自己的绘图符号以及 x 和 y 轴
标签:
mds <- plotMDS(yourdata)
plot(mds)
您可以向 plot()
添加任何参数来选择绘图符号、颜色
等等
我在 edgeR 中创建 MDS 图以可视化实验(白血病)和对照(健康供体)组的颜色时遇到问题。
我使用 htseq 文件作为 edgeR 的输入。每个文件包含两列 - gene_ID 和读取计数。 "A"代表白血病患者,"H"代表健康捐献者。
这是我的代码:
创建 table:
samples <- matrix(c("A18.txt","experiment","blood_exp",
"A19.txt","experiment","blood_exp",
"A20.txt","experiment","blood_exp",
"A23.txt","experiment","blood_exp",
"A24.txt","experiment","blood_exp",
"A26.txt","experiment","blood_exp",
"A30.txt","experiment","blood_exp",
"A37.txt","experiment","blood_exp",
"H11.txt","control","blood_control",
"H12.txt","control","blood_control",
"H13.txt","control","blood_control",
"H15.txt","control","blood_control",
"H16.txt","control","blood_control",
"H17.txt","control","blood_control",
"H18.txt","control","blood_control",
"H19.txt","control","blood_control"),
nrow = 16, ncol = 3, byrow = TRUE, dimnames = list(c(1:16), c("library_name","condition","group_ALL_vs_control")))
samples <- as.data.frame (samples, row.names = NULL, optional = FALSE, stringAsFactors = default.stringAsFactors())
使用 edgeR 函数 readDGE 读取通过 htseq-count 创建的 READS COUNT 个文件:
counts <- readDGE(samples$library_name, path = 'C:/Users/okbm4/Desktop/htseq_files', columns=c(1,2), group = samples$group_ALL_vs_control, header = FALSE)
colnames(counts) <- samples$library_name
过滤弱表达和无信息(i.e.amibigous)特征:
noint <- rownames(counts) %in% c('__no_feature','__ambiguous','__too_low_aQual','__not_aligned','__alignment_not_unique')
cpms <- cpm(counts)
keep <- rowSums (cpms > 1) >= 4 & !noint
counts <- counts[keep,]
创建 DGElist 对象
counts <- DGEList(counts=counts,group = samples$group_ALL_vs_control)
估计归一化因子,这是文库大小的归一化
counts <- calcNormFactors(counts)
使用 MDS 图检查样本之间的关系。
pdf(file = 'HCB_ALL.pdf', width = 9, height = 6)
plotMDS(counts, labels = c('A18.txt','A19.txt','A20.txt','A23.txt','A24.txt','A26.txt','A30.txt','A37.txt','H11.txt','H12.txt','H13.txt','H15.txt','H16.txt','H17.txt','H18.txt','H19.txt'),
xlab = 'Dimension 1',
ylab = 'Dimension 2',
asp = 6/9,
cex = 0.8,
main = 'Multidimentional scaling plot')
par(cex.axis =0.6, cex.lab = 0.6, cex.main = 1)
我附加了之前生成的文件。
我很乐意听到任何建议。
plotMDS()
生成一个可以传递给 plot()
的对象
是,
这样您就可以选择自己的绘图符号以及 x 和 y 轴
标签:
mds <- plotMDS(yourdata)
plot(mds)
您可以向 plot()
添加任何参数来选择绘图符号、颜色
等等