ImmuCellAI-mouse的使用--每一步的详细步骤-小白摸索
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安装好了，接下来就是探索使用方法了
data(package="ImmuCellAImouse") # 查看包中包含的数据集 > data(ImmuCellAI_mouse_example, package = "ImmuCellAImouse") # 加载示例数据
这个软件虽然安装很费劲，但是使用确实很方便
df1 <- read.table("immuedata.txt", header = TRUE, sep = "\t", check.names = FALSE,row.names = 1)
首先读入数据，我喜欢先读入表格，然后再做数据转化
> fpkm<-log2(df1+1)
> View(fpkm)
> Group <- c(rep("hp",8),rep("fp",8))
> fpkm_mat_rbind <- rbind(Group = Group,fpkm)
接下来就是使用该软件了
test <- ImmuCellAI_mouse(sample =fpkm_mat_rbind, data_type = "rnaseq",#数据类型,可选"rnaseq"/"microarray",即你输入的数据类型
+ group_tag = 1,#是否有分组信息,如果没有则填"0"
+ customer=FALSE)# 是否有自行上传的参考文件,有"1"无"0",一般来说不用上传
Estimating ssGSEA scores for 7 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 16 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 16 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 16 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 16 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 16 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 7 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 20 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 20 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 9 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
Estimating ssGSEA scores for 9 gene sets.
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
|===========================================================================================================================| 100%
[1] "Normalizing..."
运行结束了
> pheatmap(test$abundance,
+ scale = "row",
+ clustering_method = "complete",
+ color = colorRampPalette(c("blue", "white", "red"))(100))
> write.table(test$group_result,"00.txt",sep = "\t",quote = F)
> write.table(test$abundance,"00.txt",sep = "\t",quote = F)
保存结果
> head(df1)
Hp1 Hp2 Hp3 Hp4 Hp5 Hp6 Hp7 Hp8 FP1 FP2
0610005C13Rik 0.35607238 2.19597219 0.7133032 1.3349012 1.8958130 1.52013247 1.24006565 1.3403259 2.19440181 2.2000640
0610009B22Rik 15.57304080 15.13223732 12.3783338 13.7504584 16.0425190 14.08406419 13.47108619 16.8683000 14.74170255 14.6636289
0610009E02Rik 0.90592565 0.39538991 0.5988835 0.4437805 0.6086946 0.60569734 0.20785862 0.7757934 0.45877991 0.6420198
0610009L18Rik 5.11723590 3.06186255 3.0476303 3.0245539 3.7267484 3.50261897 2.82069859 5.0614082 2.93994663 4.4683006
0610010K14Rik 15.85369882 17.99520872 15.3550277 14.0462626 15.7888876 12.22929357 11.45765036 17.9166112 13.91305016 16.7954188
0610025J13Rik 0.01467935 0.06963892 0.0000000 0.0000000 0.0000000 0.01487052 0.01403367 0.0000000 0.06883272 0.0000000
FP3 FP4 FP5 FP6 FP7 FP8
0610005C13Rik 1.4403744 1.2136475 0.9327973 0.7420264 1.8170112 1.2759601
0610009B22Rik 14.9816303 15.3079062 16.5883706 12.6375185 16.8684701 15.1250927
0610009E02Rik 0.4068999 0.5822677 0.4060876 0.4908479 0.6996779 0.5768462
0610009L18Rik 3.0159519 3.7711861 2.9588813 3.7467796 4.6732326 3.7805492
0610010K14Rik 16.2183756 14.0791590 13.5729457 16.3370616 17.2432099 17.6806475
0610025J13Rik 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
> head(fpkm_mat_rbind)
Hp1 Hp2 Hp3 Hp4 Hp5 Hp6
Group hp hp hp hp hp hp
0610005C13Rik 0.439434183990669 1.67625485324343 0.776780485005664 1.2233614735223 1.53396842253438 1.33349957111871
0610009B22Rik 4.0507664255457 4.01187462926652 3.74182654209526 3.88268788078045 4.09106668802555 3.91495328985889
0610009E02Rik 0.930491838661966 0.480668302595694 0.677064862763133 0.529851394941991 0.685890424717781 0.683199978505712
0610009L18Rik 2.61287991298403 2.02214142087768 2.01707752796631 2.00882887644187 2.24084807019621 2.17076439674161
0610010K14Rik 4.07499334407862 4.24756365935961 4.03166230216824 3.91133327162713 4.06943473464168 3.72566412020249
Hp7 Hp8 FP1 FP2 FP3 FP4
Group hp hp fp fp fp fp
0610005C13Rik 1.16354101100086 1.22670942035385 1.67554579408666 1.67810076188075 1.28710249107602 1.14642548494381
0610009B22Rik 3.855101308541 4.1593304769872 3.97651967941816 3.96934658894808 3.99834268304098 4.02749965874061
0610009E02Rik 0.272451599302114 0.828463722965822 0.544762232484902 0.715471546542759 0.492519634153103 0.661993722916775
0610009L18Rik 1.93383645058255 2.59965301136653 1.97817608579163 2.45109254651279 2.00574197696182 2.25434796877664
0610010K14Rik 3.63896008189835 4.24158175940354 3.89850345651033 4.15343398154231 4.10587713529465 3.91448405943066
FP5 FP6 FP7 FP8
Group fp fp fp fp
0610005C13Rik 0.950690351137872 0.800766496087873 1.49416532116591 1.18647529504714
0610009B22Rik 4.13654992988374 3.76950925381753 4.15934420845455 4.01123555011304
0610009E02Rik 0.491686511732423 0.576133122317403 0.765261410382158 0.65704191319307
0610009L18Rik 1.98509280663949 2.24694905209201 2.50417102549929 2.25717638188832
0610010K14Rik 3.86522061970352 4.11578749582529 4.18928769027858 4.22347255528807
上面是我运行的数据格式，很简单，后续可以导出来自己各种画图和看结果，也可以参考别人文章里的分析思路，我看原文以及别人都有引用这个软件，使用率还是蛮高的。
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