deseq_analysis.r 差异基因分析DESeq2

deseq_analysis.r 差异基因分析DESeq2

使用方法:

$Rscript $scriptdir/deseq_analysis.r -h
usage: /work/my_stad_immu/scripts/deseq_analysis.r [-h] -i filepath -m
                                                   filepath -t treatname
                                                   --control CONTROL --case
                                                   CASE [-f fdr] [-c fc]
                                                   [-s size] [-a alpha]
                                                   [-X x.lab] [-Y y.lab]
                                                   [-T title] [-H height]
                                                   [-W width] [-o path]
                                                   [-p prefix]

DESeq2 analysis : https://www.omicsclass.com/article/1500

optional arguments:
  -h, --help            show this help message and exit
  -i filepath, --input filepath
                        input read count file [required]
  -m filepath, --metadata filepath
                        metadata file , required
  -t treatname, --treatname treatname
                        treat colname in group file, required
  --control CONTROL     set control group name required
  --case CASE           set case group name required
  -f fdr, --fdr fdr     set fdr threshold [default 0.05]
  -c fc, --fc fc        set fold change threshold [default 2]
  -s size, --size size  point size [optional, default: 0.7]
  -a alpha, --alpha alpha
                        point transparency [0-1] [optional, default: 1]
  -X x.lab, --x.lab x.lab
                        the label for x axis [optional, default: log2FC]
  -Y y.lab, --y.lab y.lab
                        the label for y axis [optional, default: -log10(FDR)]
  -T title, --title title
                        the label for main title [optional, default: Volcano]
  -H height, --height height
                        the height of pic inches [default 5]
  -W width, --width width
                        the width of pic inches [default 5]
  -o path, --outdir path
                        output file directory [default
                        /work/my_stad_immu/05.enrich]
  -p prefix, --prefix prefix
                        out file name prefix [default Volcano]


参数说明:

-i 输入基因表达矩阵文件,必须为count表达文件:

ID TCGA-B7-A5TK-01A-12R-A36D-31 TCGA-BR-7959-01A-11R-2343-13 TCGA-IN-8462-01A-11R-2343-13 TCGA-BR-A4CR-01A-11R-A24K-31 TCGA-CG-4443-01A-01R-1157-13 TCGA-KB-A93J-01A-11R-A39E-31 TCGA-BR-4371-01A-01R-1157-13
TSPAN6 5951 4036 2834 3484 2537 2027 4749
TNMD 3 4 0 1 0 1 8
DPM1 4672 4330 1725 4370 6523 3094 4415
SCYL3 1260 2057 702 1483 924 1451 982
C1orf112 523 992 172 1400 234 733 958
FGR 1249 1127 285 148 56 941 208
CFH 12831 11435 5387 995 4571 2189 1795
FUCA2 5896 7857 3208 5625 1527 7530 3290
GCLC 2682 5509 1447 9323 6422 5265 2418

-k 输入基因表达矩阵文件,为fpkm 或者 tpm文件 用于相关性分析:

ID TCGA-B7-A5TK-01A-12R-A36D-31 TCGA-BR-7959-01A-11R-2343-13 TCGA-IN-8462-01A-11R-2343-13 TCGA-BR-A4CR-01A-11R-A24K-31 TCGA-CG-4443-01A-01R-1157-13
TSPAN6 59.65411 32.83064 40.7596 39.84131 53.37611
TNMD 0.084708 0.091652 0 0.032211 0
DPM1 175.9638 132.3385 93.21574 187.7617 515.6368
SCYL3 8.321862 11.02458 6.652222 11.17368 12.80849
C1orf112 3.984496 6.132832 1.880095 12.1676 3.741654
FGR 16.34408 11.96739 5.350846 2.209351 1.53802


-m metadata文件路径,样本的分组信息,第一列必须和表达文件的样本名称对应:


barcode subtype.hclust StromalScore ImmuneScore ESTIMATEScore TumourPurity
TCGA-B7-A5TK-01A-12R-A36D-31 S1 1026.057 2386.835 3412.892 0.448276
TCGA-BR-7959-01A-11R-2343-13 S2 1130.722 729.402 1860.124 0.638667
TCGA-IN-8462-01A-11R-2343-13 S2 112.2318 683.9349 796.1667 0.750581
TCGA-BR-A4CR-01A-11R-A24K-31 S2 -1060.35 -766.618 -1826.97 0.943814
TCGA-CG-4443-01A-01R-1157-13 S2 -261.577 -258.629 -520.206 0.8635
TCGA-KB-A93J-01A-11R-A39E-31 S1 -202.255 1605.12 1402.865 0.688838
TCGA-BR-4371-01A-01R-1157-13 S2 -828.231 711.3379 -116.893 0.832147
TCGA-IN-A6RO-01A-12R-A33Y-31 S2 -1406.57 68.58307 -1337.98 0.917683
TCGA-HU-A4H3-01A-21R-A251-31 S2 -619.208 538.7225 -80.4854 0.829171
TCGA-RD-A8MV-01A-11R-A36D-31 S1 113.4127 2309.647 2423.06 0.572976
TCGA-VQ-A91X-01A-12R-A414-31 S2 -1845.85 -590.017 -2435.87 0.969545
TCGA-D7-8575-01A-11R-2343-13 S2 -206.112 1392.799 1186.687 0.711491
TCGA-BR-4257-01A-01R-1131-13 S1 861.029 1676.148 2537.177 0.559167
TCGA-BR-8485-01A-11R-2402-13 S1 373.0961 1110.516 1483.612 0.680198
TCGA-BR-4370-01A-01R-1157-13 S1 1300.495 1802.327 3102.822 0.488483


-t subtype.hclust   --case S1 --control  S2  : 指定metadata 分组列名,分组里面的比较组名字 ,如果分组名字有空格,应该用引号引起来:  “Stage IA”


--fdr 0.01 --fc 2  设置差异基因的筛选条件: 显著性和差异倍数

使用举例:

Rscript $scriptdir/deseq_analysis.r  -i ../01.TCGA_download/TCGA-STAD_gene_expression_Counts.tsv \
  -k ../01.TCGA_download/TCGA-STAD_gene_expression_TPM.tsv --fdr 0.01 --fc 2 \
  -m ../03.TIME/metadata.group.tsv -t subtype.hclust   --case S1 --control  S2 -p S1_vs_S2

结果展示:

火山图:



attachments-2021-06-vixQomQb60d2b01a431c0.png


参考文献:

Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology15, 550. doi: 10.1186/s13059-014-0550-8.


  • 发表于 2021-06-23 11:31
  • 阅读 ( 1674 )
  • 分类:转录组

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