DEseq2筛选RNA-seq差异表达基因-from raw c
2020-06-29 本文已影响0人
RachaelRiggs
------------------------------------参考--------------------------------------
- 2020-05-12 Analyzing RNA-seq data with DESeq2
- Count-Based Differential Expression Analysis of RNA-seq Data
pipeline -- 分四步
- 载入表达矩阵
- 设置好分组信息
- 用DEseq2进行差异分析
- 输出差异分析结果
根据input来源不同一共有4种不同pipiline构建DESeqDataSet方法，分别是：
- From transcript abundance files and tximport
- From a count matrix
- From htseq-count files
- From a SummarizedExperiment object
翻译过来就是：
- 从转录本丰度文件以及tximport构建
- 从count matrix 构建 ***
- 从htseq-count文件构建
- 从SummarizedExperiment对象构建
因为这次的源数据是countmatrix，所以从count matrix构建为例子；
> 其他三种input数据输入方法：
> 1.Salmon, Sailfish, kallito等软件输出的transcript quantification转录本定量文件，可以用DESeqDataSetFromTximport导入。
> 3.htseq-count软件输出的结果可以用DESeqDataSetFromHTSeq导入。
> 4.RangedSummarizedExperiment用DESeqDataSet导入。
0. 数据准备
-
正式载入数据之前，先看一下数据结构data structure，因为这是我们下面一切分析的起点。
-
我们需要准备2个table：一个是countData，一个是colData
Data structure
关于上面两个表的说明
- countData表示的是count矩阵，行代表gene，列代表样品，中间的数字代表对应count数，又叫表达矩阵。
- colData表示sample的元数据（metadata/information），因为这个表提供了sample的元数据，又叫样品信息矩阵。
- 注意：colData 第一列的名字与 countData 的各个列名一致（除去代表gene ID的第一列）
1.载入表达矩阵（countData和colData）
> library(tidyverse)
> library(DESeq2)
> #import countdata
> setwd("F:/rna_seq/data/matrix") # 设置工作环境, 非必须
> mycountData0<-read.csv("readcount.csv")
> head(mycountData0)
> # 共79组数据
----------------------------------------------------------------
Name ADU_PAS_1 ADU_PAS_3 ADU_PAS_5 ADU_SEC_1 ADU_SEC_3
1 0610007C21Rik 111.267773 76.820686 83.835725 127.626042 55.916969
2 0610007L01Rik 130.803336 160.871554 101.985727 113.739270 124.931606
3 0610007P08Rik 56.058572 89.473505 52.721435 24.467169 41.811787
4 0610007P14Rik 9.343095 18.979228 24.200003 3.967649 11.586399
5 0610007P22Rik 22.083680 27.113183 17.285717 13.886771 17.127720
6 0610009B22Rik 6.794978 3.615091 5.185715 4.628924 3.022539
ADU_SEC_4 ADU_SEC_5 ADU_CAR_1 ADU_CAR_3 ADU_CAR_4 ADU_CAR_5
1 74.41467 130.865137 83.885068 104.973782 91.925233 127.07060
2 107.85947 171.386829 115.797865 146.245526 100.006572 96.03045
3 15.05016 34.543082 63.825595 68.188098 39.396529 45.59022
4 25.08360 6.642900 18.235884 26.019143 21.213515 27.16013
5 19.23076 15.942961 22.794855 9.869330 13.132176 29.10014
6 12.54180 9.964351 2.735383 8.074906 8.081339 12.61006
ADU_AEC_1 ADU_AEC_3 ADU_AEC_4 ADU_AEC_5 PIC_SEC_1 PIC_SEC_2
1 68.3055654 68.689386 35.802014 75.707527 26.74912 40.075797
2 117.4241742 120.032967 112.244151 77.408820 90.43750 123.763490
3 65.2356523 40.242266 44.510612 35.727147 24.20158 8.250899
4 3.0699131 4.162993 1.935244 2.551939 0.00000 0.000000
5 14.5820870 15.264308 10.643842 25.519391 29.29666 2.357400
6 0.7674783 6.244490 6.773354 2.551939 0.00000 1.178700
PIC_SEC_3 PIC_SEC_4 PIC_AEC_1 PIC_AEC_2 PIC_AEC_3 PIC_AEC_4
1 26.059670 21.878360 46.752704 66.870297 26.39734 10.050369
2 150.435367 142.209337 122.160292 123.041346 121.11722 84.869782
3 14.214365 25.524753 27.146732 28.085525 17.08063 25.684276
4 1.184530 1.215464 4.524455 1.337406 0.00000 1.116708
5 5.922652 9.723715 3.016304 28.085525 20.18620 2.233415
6 2.369061 3.646393 1.508152 0.000000 0.00000 2.233415
AGE_CAR_2 AGE_CAR_3 AGE_CAR_4 AGE_CAR_6 YOU_CAR_1 YOU_CAR_2
1 96.189154 108.36790 126.989639 91.26077 60.61405 106.780035
2 148.429815 130.16972 136.264163 165.92867 122.09402 109.665982
3 43.119276 68.61163 53.506870 84.80799 31.17294 68.541239
4 35.656324 24.36675 21.402748 17.51469 32.90477 28.137982
5 14.925903 26.29044 7.847674 19.35834 31.17294 12.265274
6 6.633735 12.18337 6.420824 0.00000 5.19549 3.607434
YOU_CAR_4 YOU_CAR_5 LPS_CAR_1 LPS_CAR_2 LPS_CAR_3 LPS_CAR_4
1 119.687937 108.881288 65.300620 75.520652 74.300921 54.540737
2 123.863097 141.772511 147.729271 244.686911 234.098792 184.446857
3 55.668808 40.830483 57.807106 55.381811 102.799904 29.749493
4 38.968165 5.670900 22.480541 9.062478 15.267313 38.674341
5 34.793005 30.622862 21.410039 14.097188 28.498983 3.966599
6 9.742041 9.073441 2.141004 2.013884 1.017821 5.949899
LPS_AEC_1 LPS_AEC_2 LPS_AEC_3 LPS_AEC_4 LPS_PAS_1 LPS_PAS_2
1 26.236451 37.2735 43.8378759 27.618585 40.910379 96.82783
2 108.982180 151.5789 152.0334844 155.668388 303.759561 213.79584
3 88.800295 43.8999 26.1161814 40.172487 48.069695 34.08340
4 0.000000 4.9698 3.7308831 26.781658 3.068278 13.94321
5 25.227356 14.0811 31.7125060 16.738536 37.842100 20.14019
6 4.036377 12.4245 0.9327208 8.369268 1.022759 19.36557
LPS_PAS_3 LPS_PAS_4 AGE_PAS_2 AGE_PAS_3 AGE_PAS_4 AGE_PAS_6
1 68.458463 60.744946 117.002869 77.05853 92.591879 97.02280
2 261.156357 309.246996 122.369974 141.12968 247.819442 181.03035
3 83.671454 110.445356 37.569729 75.32688 57.189102 37.86256
4 17.748490 7.363024 11.807629 25.10896 8.169872 23.66410
5 21.129155 36.815119 27.908941 25.10896 24.509615 33.12974
6 3.380665 2.761134 7.513946 21.64566 1.361645 15.38166
YOU_PAS_1 YOU_PAS_2 YOU_PAS_4 YOU_PAS_5 AGE_AEC_2 AGE_AEC_3
1 79.457676 131.23664 126.293348 140.40012 72.938027 90.55541
2 92.904360 82.12874 98.838273 155.83931 114.101072 122.28850
3 50.119457 68.58173 38.437106 55.96706 44.773838 44.11674
4 12.224258 15.24038 8.236523 14.95671 8.665904 10.83569
5 13.446684 8.46688 17.845799 17.85156 20.942602 18.57547
6 6.112129 0.00000 6.863769 12.06187 15.165332 0.00000
AGE_AEC_4 AGE_AEC_6 YOU_AEC_1 YOU_AEC_2 YOU_AEC_4 YOU_AEC_5
1 71.251676 87.896622 80.56027 63.636851 76.960651 63.617037
2 123.133964 113.312513 103.77255 76.364221 71.463462 105.297164
3 46.348177 51.890777 46.42456 57.647500 52.773018 36.195900
4 11.759985 10.589954 16.38514 12.727370 3.298314 9.871609
5 4.842347 9.530959 13.65428 13.476039 15.392130 14.258991
6 11.068221 4.235982 23.21228 4.492013 3.298314 4.387382
LPS_SEC_1 LPS_SEC_2 LPS_SEC_3 LPS_SEC_4 AGE_SEC_2 AGE_SEC_3
1 39.025202 55.492586 63.792838 43.513547 105.152682 37.94807
2 173.445340 139.734342 154.014138 165.506884 160.538445 251.97519
3 46.830242 24.069073 17.315199 45.067602 58.596532 57.68107
4 4.336134 8.023024 8.201936 18.648663 8.026922 25.80469
5 6.070587 12.034537 20.960504 7.770276 12.040383 0.00000
6 19.946214 4.680098 9.113263 0.000000 4.013461 0.00000
AGE_SEC_4 AGE_SEC_6 YOU_SEC_1 YOU_SEC_2 YOU_SEC_4 YOU_SEC_5
1 93.85068 84.945754 60.489228 84.128353 72.113481 48.667825
2 139.63149 177.442243 86.329287 89.555989 101.920386 134.287148
3 70.96027 73.619654 44.045554 41.250031 59.613811 45.062801
4 0.00000 7.550734 3.523644 7.055926 10.576644 16.222608
5 15.26027 16.989151 15.269126 11.398035 18.268748 16.222608
6 0.00000 7.550734 4.698192 3.256581 2.884539 3.605024
PIC_PAS_1 PIC_PAS_2 PIC_PAS_3 PIC_PAS_4 PIC_CAR_1 PIC_CAR_2
1 53.664428 112.873023 56.092111 58.50205 59.392445 92.088575
2 196.152737 129.192015 132.439706 122.44616 52.321916 108.266297
3 48.112936 62.556133 210.345415 78.90974 53.736022 49.777608
4 35.159453 16.318991 7.790571 20.40769 4.242317 9.955522
5 3.700995 21.758655 0.000000 29.93128 0.000000 23.644364
6 0.000000 5.439664 0.000000 0.00000 5.656423 1.244440
PIC_CAR_3 PIC_CAR_4
1 69.50564 113.84738
2 187.50358 33.20549
3 22.62974 28.46184
4 1.61641 0.00000
5 14.54769 37.94913
6 14.54769 0.00000
----------------------------------------------------------------
> # 第一列有个Name，需要去除，先把第一列当作行名来处理
> rownames(mycountData0) <- mycountData0[,1]
> # 把带Name的列删除
> mycountData1 <- mycountData0[,-1]
> head(mycountData)
----------------------------------------------------------------
ADU_PAS_1 ADU_PAS_3 ADU_PAS_5 ADU_SEC_1 ADU_SEC_3
0610007C21Rik 111.267773 76.820686 83.835725 127.626042 55.916969
0610007L01Rik 130.803336 160.871554 101.985727 113.739270 124.931606
0610007P08Rik 56.058572 89.473505 52.721435 24.467169 41.811787
0610007P14Rik 9.343095 18.979228 24.200003 3.967649 11.586399
0610007P22Rik 22.083680 27.113183 17.285717 13.886771 17.127720
0610009B22Rik 6.794978 3.615091 5.185715 4.628924 3.022539
ADU_SEC_4 ADU_SEC_5 ADU_CAR_1 ADU_CAR_3 ADU_CAR_4
0610007C21Rik 74.41467 130.865137 83.885068 104.973782 91.925233
0610007L01Rik 107.85947 171.386829 115.797865 146.245526 100.006572
0610007P08Rik 15.05016 34.543082 63.825595 68.188098 39.396529
0610007P14Rik 25.08360 6.642900 18.235884 26.019143 21.213515
0610007P22Rik 19.23076 15.942961 22.794855 9.869330 13.132176
0610009B22Rik 12.54180 9.964351 2.735383 8.074906 8.081339
ADU_CAR_5 ADU_AEC_1 ADU_AEC_3 ADU_AEC_4 ADU_AEC_5
0610007C21Rik 127.07060 68.3055654 68.689386 35.802014 75.707527
0610007L01Rik 96.03045 117.4241742 120.032967 112.244151 77.408820
0610007P08Rik 45.59022 65.2356523 40.242266 44.510612 35.727147
0610007P14Rik 27.16013 3.0699131 4.162993 1.935244 2.551939
0610007P22Rik 29.10014 14.5820870 15.264308 10.643842 25.519391
0610009B22Rik 12.61006 0.7674783 6.244490 6.773354 2.551939
PIC_SEC_1 PIC_SEC_2 PIC_SEC_3 PIC_SEC_4 PIC_AEC_1
0610007C21Rik 26.74912 40.075797 26.059670 21.878360 46.752704
0610007L01Rik 90.43750 123.763490 150.435367 142.209337 122.160292
0610007P08Rik 24.20158 8.250899 14.214365 25.524753 27.146732
0610007P14Rik 0.00000 0.000000 1.184530 1.215464 4.524455
0610007P22Rik 29.29666 2.357400 5.922652 9.723715 3.016304
0610009B22Rik 0.00000 1.178700 2.369061 3.646393 1.508152
PIC_AEC_2 PIC_AEC_3 PIC_AEC_4 AGE_CAR_2 AGE_CAR_3
0610007C21Rik 66.870297 26.39734 10.050369 96.189154 108.36790
0610007L01Rik 123.041346 121.11722 84.869782 148.429815 130.16972
0610007P08Rik 28.085525 17.08063 25.684276 43.119276 68.61163
0610007P14Rik 1.337406 0.00000 1.116708 35.656324 24.36675
0610007P22Rik 28.085525 20.18620 2.233415 14.925903 26.29044
0610009B22Rik 0.000000 0.00000 2.233415 6.633735 12.18337
AGE_CAR_4 AGE_CAR_6 YOU_CAR_1 YOU_CAR_2 YOU_CAR_4
0610007C21Rik 126.989639 91.26077 60.61405 106.780035 119.687937
0610007L01Rik 136.264163 165.92867 122.09402 109.665982 123.863097
0610007P08Rik 53.506870 84.80799 31.17294 68.541239 55.668808
0610007P14Rik 21.402748 17.51469 32.90477 28.137982 38.968165
0610007P22Rik 7.847674 19.35834 31.17294 12.265274 34.793005
0610009B22Rik 6.420824 0.00000 5.19549 3.607434 9.742041
YOU_CAR_5 LPS_CAR_1 LPS_CAR_2 LPS_CAR_3 LPS_CAR_4
0610007C21Rik 108.881288 65.300620 75.520652 74.300921 54.540737
0610007L01Rik 141.772511 147.729271 244.686911 234.098792 184.446857
0610007P08Rik 40.830483 57.807106 55.381811 102.799904 29.749493
0610007P14Rik 5.670900 22.480541 9.062478 15.267313 38.674341
0610007P22Rik 30.622862 21.410039 14.097188 28.498983 3.966599
0610009B22Rik 9.073441 2.141004 2.013884 1.017821 5.949899
LPS_AEC_1 LPS_AEC_2 LPS_AEC_3 LPS_AEC_4 LPS_PAS_1
0610007C21Rik 26.236451 37.2735 43.8378759 27.618585 40.910379
0610007L01Rik 108.982180 151.5789 152.0334844 155.668388 303.759561
0610007P08Rik 88.800295 43.8999 26.1161814 40.172487 48.069695
0610007P14Rik 0.000000 4.9698 3.7308831 26.781658 3.068278
0610007P22Rik 25.227356 14.0811 31.7125060 16.738536 37.842100
0610009B22Rik 4.036377 12.4245 0.9327208 8.369268 1.022759
LPS_PAS_2 LPS_PAS_3 LPS_PAS_4 AGE_PAS_2 AGE_PAS_3
0610007C21Rik 96.82783 68.458463 60.744946 117.002869 77.05853
0610007L01Rik 213.79584 261.156357 309.246996 122.369974 141.12968
0610007P08Rik 34.08340 83.671454 110.445356 37.569729 75.32688
0610007P14Rik 13.94321 17.748490 7.363024 11.807629 25.10896
0610007P22Rik 20.14019 21.129155 36.815119 27.908941 25.10896
0610009B22Rik 19.36557 3.380665 2.761134 7.513946 21.64566
AGE_PAS_4 AGE_PAS_6 YOU_PAS_1 YOU_PAS_2 YOU_PAS_4
0610007C21Rik 92.591879 97.02280 79.457676 131.23664 126.293348
0610007L01Rik 247.819442 181.03035 92.904360 82.12874 98.838273
0610007P08Rik 57.189102 37.86256 50.119457 68.58173 38.437106
0610007P14Rik 8.169872 23.66410 12.224258 15.24038 8.236523
0610007P22Rik 24.509615 33.12974 13.446684 8.46688 17.845799
0610009B22Rik 1.361645 15.38166 6.112129 0.00000 6.863769
YOU_PAS_5 AGE_AEC_2 AGE_AEC_3 AGE_AEC_4 AGE_AEC_6
0610007C21Rik 140.40012 72.938027 90.55541 71.251676 87.896622
0610007L01Rik 155.83931 114.101072 122.28850 123.133964 113.312513
0610007P08Rik 55.96706 44.773838 44.11674 46.348177 51.890777
0610007P14Rik 14.95671 8.665904 10.83569 11.759985 10.589954
0610007P22Rik 17.85156 20.942602 18.57547 4.842347 9.530959
0610009B22Rik 12.06187 15.165332 0.00000 11.068221 4.235982
YOU_AEC_1 YOU_AEC_2 YOU_AEC_4 YOU_AEC_5 LPS_SEC_1
0610007C21Rik 80.56027 63.636851 76.960651 63.617037 39.025202
0610007L01Rik 103.77255 76.364221 71.463462 105.297164 173.445340
0610007P08Rik 46.42456 57.647500 52.773018 36.195900 46.830242
0610007P14Rik 16.38514 12.727370 3.298314 9.871609 4.336134
0610007P22Rik 13.65428 13.476039 15.392130 14.258991 6.070587
0610009B22Rik 23.21228 4.492013 3.298314 4.387382 19.946214
LPS_SEC_2 LPS_SEC_3 LPS_SEC_4 AGE_SEC_2 AGE_SEC_3
0610007C21Rik 55.492586 63.792838 43.513547 105.152682 37.94807
0610007L01Rik 139.734342 154.014138 165.506884 160.538445 251.97519
0610007P08Rik 24.069073 17.315199 45.067602 58.596532 57.68107
0610007P14Rik 8.023024 8.201936 18.648663 8.026922 25.80469
0610007P22Rik 12.034537 20.960504 7.770276 12.040383 0.00000
0610009B22Rik 4.680098 9.113263 0.000000 4.013461 0.00000
AGE_SEC_4 AGE_SEC_6 YOU_SEC_1 YOU_SEC_2 YOU_SEC_4
0610007C21Rik 93.85068 84.945754 60.489228 84.128353 72.113481
0610007L01Rik 139.63149 177.442243 86.329287 89.555989 101.920386
0610007P08Rik 70.96027 73.619654 44.045554 41.250031 59.613811
0610007P14Rik 0.00000 7.550734 3.523644 7.055926 10.576644
0610007P22Rik 15.26027 16.989151 15.269126 11.398035 18.268748
0610009B22Rik 0.00000 7.550734 4.698192 3.256581 2.884539
YOU_SEC_5 PIC_PAS_1 PIC_PAS_2 PIC_PAS_3 PIC_PAS_4
0610007C21Rik 48.667825 53.664428 112.873023 56.092111 58.50205
0610007L01Rik 134.287148 196.152737 129.192015 132.439706 122.44616
0610007P08Rik 45.062801 48.112936 62.556133 210.345415 78.90974
0610007P14Rik 16.222608 35.159453 16.318991 7.790571 20.40769
0610007P22Rik 16.222608 3.700995 21.758655 0.000000 29.93128
0610009B22Rik 3.605024 0.000000 5.439664 0.000000 0.00000
PIC_CAR_1 PIC_CAR_2 PIC_CAR_3 PIC_CAR_4
0610007C21Rik 59.392445 92.088575 69.50564 113.84738
0610007L01Rik 52.321916 108.266297 187.50358 33.20549
0610007P08Rik 53.736022 49.777608 22.62974 28.46184
0610007P14Rik 4.242317 9.955522 1.61641 0.00000
0610007P22Rik 0.000000 23.644364 14.54769 37.94913
0610009B22Rik 5.656423 1.244440 14.54769 0.00000
-----------------------------------------------------------------------
# colData可以自己在excel做好另存为.csv格式，再导入即可；
# 因为这里的样本比较多，取第一行所有colname，在excel中做好colData，另存为csv再导入
> columnname <- mycountData0[1,]
> columnname
-----------------------------------------------------------------------
Name ADU_PAS_1 ADU_PAS_3 ADU_PAS_5 ADU_SEC_1
0610007C21Rik 0610007C21Rik 111.2678 76.82069 83.83573 127.626
ADU_SEC_3 ADU_SEC_4 ADU_SEC_5 ADU_CAR_1 ADU_CAR_3
0610007C21Rik 55.91697 74.41467 130.8651 83.88507 104.9738
ADU_CAR_4 ADU_CAR_5 ADU_AEC_1 ADU_AEC_3 ADU_AEC_4
0610007C21Rik 91.92523 127.0706 68.30557 68.68939 35.80201
ADU_AEC_5 PIC_SEC_1 PIC_SEC_2 PIC_SEC_3 PIC_SEC_4
0610007C21Rik 75.70753 26.74912 40.0758 26.05967 21.87836
PIC_AEC_1 PIC_AEC_2 PIC_AEC_3 PIC_AEC_4 AGE_CAR_2
0610007C21Rik 46.7527 66.8703 26.39734 10.05037 96.18915
AGE_CAR_3 AGE_CAR_4 AGE_CAR_6 YOU_CAR_1 YOU_CAR_2
0610007C21Rik 108.3679 126.9896 91.26077 60.61405 106.78
YOU_CAR_4 YOU_CAR_5 LPS_CAR_1 LPS_CAR_2 LPS_CAR_3
0610007C21Rik 119.6879 108.8813 65.30062 75.52065 74.30092
LPS_CAR_4 LPS_AEC_1 LPS_AEC_2 LPS_AEC_3 LPS_AEC_4
0610007C21Rik 54.54074 26.23645 37.2735 43.83788 27.61858
LPS_PAS_1 LPS_PAS_2 LPS_PAS_3 LPS_PAS_4 AGE_PAS_2
0610007C21Rik 40.91038 96.82783 68.45846 60.74495 117.0029
AGE_PAS_3 AGE_PAS_4 AGE_PAS_6 YOU_PAS_1 YOU_PAS_2
0610007C21Rik 77.05853 92.59188 97.0228 79.45768 131.2366
YOU_PAS_4 YOU_PAS_5 AGE_AEC_2 AGE_AEC_3 AGE_AEC_4
0610007C21Rik 126.2933 140.4001 72.93803 90.55541 71.25168
AGE_AEC_6 YOU_AEC_1 YOU_AEC_2 YOU_AEC_4 YOU_AEC_5
0610007C21Rik 87.89662 80.56027 63.63685 76.96065 63.61704
LPS_SEC_1 LPS_SEC_2 LPS_SEC_3 LPS_SEC_4 AGE_SEC_2
0610007C21Rik 39.0252 55.49259 63.79284 43.51355 105.1527
AGE_SEC_3 AGE_SEC_4 AGE_SEC_6 YOU_SEC_1 YOU_SEC_2
0610007C21Rik 37.94807 93.85068 84.94575 60.48923 84.12835
YOU_SEC_4 YOU_SEC_5 PIC_PAS_1 PIC_PAS_2 PIC_PAS_3
0610007C21Rik 72.11348 48.66783 53.66443 112.873 56.09211
PIC_PAS_4 PIC_CAR_1 PIC_CAR_2 PIC_CAR_3 PIC_CAR_4
0610007C21Rik 58.50205 59.39244 92.08857 69.50564 113.8474
-----------------------------------------------------------------------
> # import coldata
> colData0 <- read.csv("/Users/quyue/Desktop/bonemarrowData/inflam-bm/colData.csv")
> colData0
-----------------------------------------------------------------------
id treatment age
1 ADU_PAS_1 no treatment 2 months
2 ADU_PAS_3 no treatment 2 months
3 ADU_PAS_5 no treatment 2 months
4 ADU_SEC_1 no treatment 2 months
5 ADU_SEC_3 no treatment 2 months
6 ADU_SEC_4 no treatment 2 months
7 ADU_SEC_5 no treatment 2 months
8 ADU_CAR_1 no treatment 2 months
9 ADU_CAR_3 no treatment 2 months
10 ADU_CAR_4 no treatment 2 months
11 ADU_CAR_5 no treatment 2 months
12 ADU_AEC_1 no treatment 2 months
13 ADU_AEC_3 no treatment 2 months
14 ADU_AEC_4 no treatment 2 months
15 ADU_AEC_5 no treatment 2 months
16 PIC_SEC_1 polyIC 2 months
17 PIC_SEC_2 polyIC 2 months
18 PIC_SEC_3 polyIC 2 months
19 PIC_SEC_4 polyIC 2 months
20 PIC_AEC_1 polyIC 2 months
21 PIC_AEC_2 polyIC 2 months
22 PIC_AEC_3 polyIC 2 months
23 PIC_AEC_4 polyIC 2 months
24 AGE_CAR_2 no treatment 2 years
25 AGE_CAR_3 no treatment 2 years
26 AGE_CAR_4 no treatment 2 years
27 AGE_CAR_6 no treatment 2 years
28 YOU_CAR_1 no treatment 2 weeks
29 YOU_CAR_2 no treatment 2 weeks
30 YOU_CAR_4 no treatment 2 weeks
31 YOU_CAR_5 no treatment 2 weeks
32 LPS_CAR_1 LPS 2 months
33 LPS_CAR_2 LPS 2 months
34 LPS_CAR_3 LPS 2 months
35 LPS_CAR_4 LPS 2 months
36 LPS_AEC_1 LPS 2 months
37 LPS_AEC_2 LPS 2 months
38 LPS_AEC_3 LPS 2 months
39 LPS_AEC_4 LPS 2 months
40 LPS_PAS_1 LPS 2 months
41 LPS_PAS_2 LPS 2 months
42 LPS_PAS_3 LPS 2 months
43 LPS_PAS_4 LPS 2 months
44 AGE_PAS_2 no treatment 2 years
45 AGE_PAS_3 no treatment 2 years
46 AGE_PAS_4 no treatment 2 years
47 AGE_PAS_6 no treatment 2 years
48 YOU_PAS_1 no treatment 2 weeks
49 YOU_PAS_2 no treatment 2 weeks
50 YOU_PAS_4 no treatment 2 weeks
51 YOU_PAS_5 no treatment 2 weeks
52 AGE_AEC_2 no treatment 2 years
53 AGE_AEC_3 no treatment 2 years
54 AGE_AEC_4 no treatment 2 years
55 AGE_AEC_6 no treatment 2 years
56 YOU_AEC_1 no treatment 2 weeks
57 YOU_AEC_2 no treatment 2 weeks
58 YOU_AEC_4 no treatment 2 weeks
59 YOU_AEC_5 no treatment 2 weeks
60 LPS_SEC_1 LPS 2 months
61 LPS_SEC_2 LPS 2 months
62 LPS_SEC_3 LPS 2 months
63 LPS_SEC_4 LPS 2 months
64 AGE_SEC_2 no treatment 2 years
65 AGE_SEC_3 no treatment 2 years
66 AGE_SEC_4 no treatment 2 years
67 AGE_SEC_6 no treatment 2 years
68 YOU_SEC_1 no treatment 2 weeks
69 YOU_SEC_2 no treatment 2 weeks
70 YOU_SEC_4 no treatment 2 weeks
71 YOU_SEC_5 no treatment 2 weeks
72 PIC_PAS_1 polyIC 2 months
73 PIC_PAS_2 polyIC 2 months
74 PIC_PAS_3 polyIC 2 months
75 PIC_PAS_4 polyIC 2 months
76 PIC_CAR_1 polyIC 2 months
77 PIC_CAR_2 polyIC 2 months
78 PIC_CAR_3 polyIC 2 months
79 PIC_CAR_4 polyIC 2 months
2. 构建dds对象,开始DESeq流程
注释：dds=DESeqDataSet Object
输入数据必须输入非标准化（非均一化）的counts值！！！我这里是normalized以后的（晕死，待填坑）
https://cloud.tencent.com/developer/article/1332399
https://cloud.tencent.com/developer/article/1332404
https://blog.csdn.net/xiaomotong123/article/details/106900481
https://zhuanlan.zhihu.com/p/31952724
https://zhuanlan.zhihu.com/p/32668252
https://www.jianshu.com/p/f685149ea247
https://www.jianshu.com/p/5f94ae79f298
