DEseq2篩選RNA-seq差異表達(dá)基因-from raw count matrix

------------------------------------參考--------------------------------------

2020-05-12 Analyzing RNA-seq data with DESeq2
Count-Based Differential Expression Analysis of RNA-seq Data

pipeline -- 分四步

載入表達(dá)矩陣
設(shè)置好分組信息
用DEseq2進(jìn)行差異分析
輸出差異分析結(jié)果

根據(jù)input來(lái)源不同一共有4種不同pipiline構(gòu)建DESeqDataSet方法粱锐，分別是：

From transcript abundance files and tximport
From a count matrix
From htseq-count files
From a SummarizedExperiment object

翻譯過(guò)來(lái)就是：

從轉(zhuǎn)錄本豐度文件以及tximport構(gòu)建
從count matrix 構(gòu)建 ***
從htseq-count文件構(gòu)建
從SummarizedExperiment對(duì)象構(gòu)建

因?yàn)檫@次的源數(shù)據(jù)是countmatrix洒疚，所以從count matrix構(gòu)建為例子；

> 其他三種input數(shù)據(jù)輸入方法：

> 1.Salmon, Sailfish, kallito等軟件輸出的transcript quantification轉(zhuǎn)錄本定量文件噪珊，可以用DESeqDataSetFromTximport導(dǎo)入谤职。
> 3.htseq-count軟件輸出的結(jié)果可以用DESeqDataSetFromHTSeq導(dǎo)入饰豺。
> 4.RangedSummarizedExperiment用DESeqDataSet導(dǎo)入。

0. 數(shù)據(jù)準(zhǔn)備

正式載入數(shù)據(jù)之前允蜈，先看一下數(shù)據(jù)結(jié)構(gòu)data structure冤吨，因?yàn)檫@是我們下面一切分析的起點(diǎn)。
我們需要準(zhǔn)備2個(gè)table：一個(gè)是countData陷寝，一個(gè)是colData

Data structure

關(guān)于上面兩個(gè)表的說(shuō)明

countData表示的是count矩陣锅很，行代表gene，列代表樣品凤跑，中間的數(shù)字代表對(duì)應(yīng)count數(shù)爆安，又叫表達(dá)矩陣。
colData表示sample的元數(shù)據(jù)（metadata/information）仔引，因?yàn)檫@個(gè)表提供了sample的元數(shù)據(jù)扔仓，又叫樣品信息矩陣褐奥。
注意：colData 第一列的名字與 countData 的各個(gè)列名一致（除去代表gene ID的第一列）

1.載入表達(dá)矩陣（countData和colData）

> library(tidyverse)
> library(DESeq2)
> #import countdata
> setwd("F:/rna_seq/data/matrix") # 設(shè)置工作環(huán)境, 非必須
> mycountData0<-read.csv("readcount.csv")
> head(mycountData0)
> # 共79組數(shù)據(jù)
----------------------------------------------------------------
  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
----------------------------------------------------------------
> # 第一列有個(gè)Name，需要去除翘簇，先把第一列當(dāng)作行名來(lái)處理
> rownames(mycountData0) <- mycountData0[,1]
> # 把帶Name的列刪除
> 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做好另存為.csv格式撬码，再導(dǎo)入即可；
# 因?yàn)檫@里的樣本比較多版保，取第一行所有colname呜笑，在excel中做好colData，另存為csv再導(dǎo)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. 構(gòu)建dds對(duì)象,開始DESeq流程

注釋：dds=DESeqDataSet Object
輸入數(shù)據(jù)必須輸入非標(biāo)準(zhǔn)化（非均一化）的counts值３估纭＝行病！我這里是normalized以后的（暈死汞幢，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
http://www.reibang.com/p/f685149ea247
http://www.reibang.com/p/5f94ae79f298

最后編輯于：2020.06.29 04:39:22

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