如何将基因探针ID与R中数据框中的基因符号匹配

Question

我有一个包含基因和样本（癌症与正常）的数据框，并且我已经进行了 LASSO 和交叉验证以选择最佳 lambda，以及找到具有非零系数的基因（下面代码中的 x 是我的数据框包含这些）。我接下来要做的是向 x 添加另一列，其中包含与 x 中具有非零系数的那些基因相对应的基因符号（来自原始数据帧 daf 的符号）。我已经尝试了一个多小时来让它发挥作用，但没有成功。关于什么是最好的方法的任何建议？以下是我的代码：

probeID<-c("213456_at", "217428_s_at", "219230_at", "226228_at","230030_at")
symbol<-c("SOSTDC1","COL10A1", "TMEM100", "AQP4", "HS6ST2")

BCR1<-c(28.005966, 30.806433, 17.341375, 17.40666, 30.039436)
BCR2<-c(30.973469, 29.236025, 30.41161, 20.914383, 20.904331)
BCR3<-c(26.322796, 25.542833, 22.460772, 19.972183, 30.409641)
BCR4<-c(26.441898, 25.837685, 23.158352, 20.379173, 33.81327)
BCR5<-c(39.750206, 19.901133, 28.180124, 22.668673, 25.748884)
CTL6<-c(23.004385, 28.472675, 23.81621, 26.433413, 28.851719)
CTL7<-c(22.239546, 28.741674, 23.754929, 26.015385, 28.16368)
CTL8<-c(29.590443, 30.041988, 21.323061, 24.272501, 18.099016)
CTL9<-c(15.856442, 22.64224, 29.629637, 25.374926, 22.356894)
CTL10<-c(38.137985, 24.753338, 26.986668, 24.578161, 19.223558)
daf<-data.frame(probeID, symbol, BCR1, BCR2, BCR3, BCR4, BCR5, CTL6, CTL7, CTL8, CTL9, CTL10)

daf1<-t(daf[,3:12])
colnames(daf1)<-daf$probeID
View(daf1)

Type<-c("cancer", "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal", "normal")
Sample<-c("BCR1", "BCR2", "BCR3", "BCR4", "BCR5", "CTL6", "CTL7", "CTL8", "CTL9", "CTL10")
type.df<-data.frame(Sample, Type)

daf2<-data.frame(daf1, type.df$Type)
names(daf2)[names(daf2) == "type.df.Type"] <- "Type"
View(daf2)
daf2$Type<-as.factor(daf2$Type)

lassoModel <- glmnet(
  x=data.matrix(daf2[,-6]),
  y=daf2$Type,
  alpha=1,
  family="binomial")
plot(lassoModel, xvar="lambda")
coef(lassoModel)[,5][coef(lassoModel)[,5]!=0]

#Cross Validation
cv.lassoModel<- cv.glmnet(
  x=data.matrix(daf2[,-6]),
  y=daf2$Type,
  alpha=1, family="binomial")

# plot variable deviances vs. shrinkage parameter, λ (lambda)
plot(cv.lassoModel)

#Chose best lambda
idealLambda <- cv.lassoModel$lambda.min
idealLambda1se <- cv.lassoModel$lambda.1se
print(idealLambda); print(idealLambda1se)

# derive coefficients for each gene
co <- coef(cv.lassoModel, s=idealLambda, exact=TRUE)
co

co.se <- coef(cv.lassoModel, s=idealLambda1se, exact=TRUE)
co.se

#Select those genes that have non-zero coefficients for the best lambda
cv.glm.probe<-coef(cv.lassoModel, s="lambda.min")
x<-data.frame(cv.glm.probe[cv.glm.probe[,1]!=0,])

Answer 1

如果你看一下你的系数，它们前面有一个额外的“X”，因为 glm、lm、glmnet 等不喜欢以数字开头的变量，默认情况下会添加一个“X”。

x$symbol = daf$symbol[match(sub("^X","",rownames(x)),daf$probeID)]
x

             cv.glm.probe.cv.glm.probe...1.....0...  symbol
(Intercept)                            -41.23471919    <NA>
X217428_s_at                             0.18134947 COL10A1
X226228_at                               1.61933359    AQP4
X230030_at                              -0.03797544  HS6ST2

如果您说 X 不存在的 data.frame，例如：

df = data.frame(coefficients=runif(5))
rownames(df) = sample(daf$probeID,5)
df$symbol = daf$symbol[match(rownames(df),daf$probeID)]

或者在数据框中有实际的探测并合并：

df = data.frame(probe=sample(daf$probeID,5),coefficients=runif(5))
merge(df,daf[,c("probeID","symbol")],by.x="probe",by.y="probeID")

        probe coefficients  symbol
1   213456_at   0.40697051 SOSTDC1
2 217428_s_at   0.97655456 COL10A1
3   219230_at   0.09496977 TMEM100
4   226228_at   0.70865375    AQP4
5   230030_at   0.35125967  HS6ST2