下载TCGA所有癌症的maf文件做signature分析

才sanger研究所已经做好了这个分析，但是值得我们重复一下，效果如下：

TCGA所有癌症的mutation signature

首先TCGA所有癌症的maf文件

maf格式的mutation记录文件在TCGA里面已经是level4的数据啦，所以是完全open的，可以随意下载，只需要去其GDC官网简单点击，选择即可。

主要步骤就是在https://portal.gdc.cancer.gov/repository里面点击过滤文件类型，选择maf格式，再过滤access权限，选择open即可，最后得到的132个文件就是我们需要的。

总共是2.19GB的文件，每个癌症种类都有4种maf文件，分别是用mutect,muse,vanscan,somaticsniper这4款软件call 到的somatic mutation文件。

下载方式这里我选择下载它们132个文件的manifest文件，然后用GDC提供的官方工具来下载！关于这个工具，我在生信技能树论坛写过教程，就不多说了，自己去看哈，现在下载TCGA数据也是非常方便，首先是GDC网站及客户端就是安装成功后，运行 ./gdc-client download -m manifest_xxx.txt j即可。这个manifest文件就是自己刚才创造并且下载的。

cd ~/institute/TCGA/GDC_NCBI/all ~/biosoft/GDC/gdc-client download -m gdc_manifest.2017-08-25T02-57-11.281090.txt

但是这个工具，提供的电脑操作系统版本有限哦If you are a user of CentOS 6 or RedHat Enterprise Release 6 and wish to use the Data Transfer Tool, contact the GDC Help Desk for assistance.

所以我是在MAC里面下载好了，再上传到我的服务器去的！

然后根据MAF文件制作signature

我是根据这篇文章Mutational Profile of Metastatic Breast Cancers: A Retrospective Analysis里面的方法来做的，他们的方法描述如下：

De novo mutational signature analysis was done using the Matlab Welcome Trust Sanger Institute’s signature framework. We used the deconstructSigs R package to determine the contribution of the known signatures that explain each sample mutational profile with more than 50 somatic mutations. We considered the 13 signatures (Signatures 1, 2, 3, 5, 6, 8, 10, 13, 17, 18, 20, 26, and 30) operative in breast cancer as defined in COSMIC (http://cancer.sanger.ac.uk/signatures/matrix.png). A signature was defined as operative or predominant if its contribution to the mutational pattern was respectively >25% (or >100 mutations) or >50%.

虽然我以前写过一个类似的教程，用SomaticSignatures包来解析maf突变数据获得mutation signature 这里还是再学习学习这个新的工具deconstructSigs R package吧。

这是个R包，所以直接在Rstudio里面安装即可，这里选取BRCA的somatic mutation的MAF文件做一下分析，看看四个软件找出的变异，是否在signature上面有差异。

install.packages('deconstructSigs')# dependencies 'BSgenome', 'BSgenome.Hsapiens.UCSC.hg19' BiocInstaller::biocLite('BSgenome')BiocInstaller::biocLite('BSgenome.Hsapiens.UCSC.hg19')## https://github.com/raerose01/deconstructSigsfile1='TCGA.STAD.muse..somatic.maf.gz'TCGA.STAD.muse=read.table(file1,sep = 't',quote="",header = T)TCGA.STAD.muse[1:5,1:15]## data frame including 5 columns: sample.ID,chr,pos,ref,alt sample.mut.ref <- data.fram(Sample='TCGA.STAD.muse',                                 chr = TCGA.STAD.muse[,5],                                 pos = TCGA.STAD.muse[,6],                                 ref = TCGA.STAD.muse[,11],                                 alt = TCGA.STAD.muse[,13])sigs.input <- mut.to.sigs.input(mut.ref = sample.mut.ref,                                 sample.id = "Sample",                                 chr = "chr",                                 pos = "pos",                                 ref = "ref",                                 alt = "alt")class(sigs.input)sample_1 = whichSignatures(tumor.ref = sigs.input,                            signatures.ref = signatures.nature2013,                            sample.id = 'TCGA.STAD.muse',                            contexts.needed = TRUE,                           tri.counts.method = 'exome')# Plot example                  plot_example <- whichSignatures(tumor.ref = sigs.input  ,                         signatures.ref = signatures.nature2013,                        sample.id = 'TCGA.STAD.muse' )# Plot outputplotSignatures(plot_example, sub = 'example')

这个里面有一个问题，就是deconstructSigs R package似乎只支持hg19版本的基因组，而我下载的TCGA的MAF是hg38版本的，所以代码虽然是对的，但实际上做出的结果是不对的，需要把下载的TCGA的maf文件进行坐标转换。

而所谓批量，无非就是在上面的R脚本里面加入一个循环咯。

（点击阅读原文有这个包的详细说明书哈！）

注意事项，下载的MAF文件可能有两种格式，可能是47列，或者120列，第一行一般都是头文件，注释着每一列的信息，的确，信息量有点略大。如下：

     1    Hugo_Symbol     2    Entrez_Gene_Id     3    Center     4    NCBI_Build     5    Chromosome     6    Start_Position     7    End_Position     8    Strand     9    Consequence    10    Variant_Classification    11    Variant_Type    12    Reference_Allele    13    Tumor_Seq_Allele1    14    Tumor_Seq_Allele2    15    dbSNP_RS    16    dbSNP_Val_Status    17    Tumor_Sample_Barcode    18    Matched_Norm_Sample_Barcode    19    Match_Norm_Seq_Allele1    20    Match_Norm_Seq_Allele2    21    Tumor_Validation_Allele1    22    Tumor_Validation_Allele2    23    Match_Norm_Validation_Allele1    24    Match_Norm_Validation_Allele2    25    Verification_Status    26    Validation_Status    27    Mutation_Status    28    Sequencing_Phase    29    Sequence_Source    30    Validation_Method    31    Score    32    BAM_File    33    Sequencer    34    t_ref_count    35    t_alt_count    36    n_ref_count    37    n_alt_count    38    HGVSc    39    HGVSp    40    HGVSp_Short    41    Transcript_ID    42    RefSeq    43    Protein_position    44    Codons    45    Hotspot    46    cDNA_change    47    Amino_Acid_Change

     1    Hugo_Symbol     2    Entrez_Gene_Id     3    Center     4    NCBI_Build     5    Chromosome     6    Start_Position     7    End_Position     8    Strand     9    Variant_Classification    10    Variant_Type    11    Reference_Allele    12    Tumor_Seq_Allele1    13    Tumor_Seq_Allele2    14    dbSNP_RS    15    dbSNP_Val_Status    16    Tumor_Sample_Barcode    17    Matched_Norm_Sample_Barcode    18    Match_Norm_Seq_Allele1    19    Match_Norm_Seq_Allele2    20    Tumor_Validation_Allele1    21    Tumor_Validation_Allele2    22    Match_Norm_Validation_Allele1    23    Match_Norm_Validation_Allele2    24    Verification_Status    25    Validation_Status    26    Mutation_Status    27    Sequencing_Phase    28    Sequence_Source    29    Validation_Method    30    Score    31    BAM_File    32    Sequencer    33    Tumor_Sample_UUID    34    Matched_Norm_Sample_UUID    35    HGVSc    36    HGVSp    37    HGVSp_Short    38    Transcript_ID    39    Exon_Number    40    t_depth    41    t_ref_count    42    t_alt_count    43    n_depth    44    n_ref_count    45    n_alt_count    46    all_effects    47    Allele    48    Gene    49    Feature    50    Feature_type    51    One_Consequence    52    Consequence    53    cDNA_position    54    CDS_position    55    Protein_position    56    Amino_acids    57    Codons    58    Existing_variation    59    ALLELE_NUM    60    DISTANCE    61    TRANSCRIPT_STRAND    62    SYMBOL    63    SYMBOL_SOURCE    64    HGNC_ID    65    BIOTYPE    66    CANONICAL    67    CCDS    68    ENSP    69    SWISSPROT    70    TREMBL    71    UNIPARC    72    RefSeq    73    SIFT    74    PolyPhen    75    EXON    76    INTRON    77    DOMAINS    78    GMAF    79    AFR_MAF    80    AMR_MAF    81    ASN_MAF    82    EAS_MAF    83    EUR_MAF    84    SAS_MAF    85    AA_MAF    86    EA_MAF    87    CLIN_SIG    88    SOMATIC    89    PUBMED    90    MOTIF_NAME    91    MOTIF_POS    92    HIGH_INF_POS    93    MOTIF_SCORE_CHANGE    94    IMPACT    95    PICK    96    VARIANT_CLASS    97    TSL    98    HGVS_OFFSET    99    PHENO   100    MINIMISED   101    ExAC_AF   102    ExAC_AF_Adj   103    ExAC_AF_AFR   104    ExAC_AF_AMR   105    ExAC_AF_EAS   106    ExAC_AF_FIN   107    ExAC_AF_NFE   108    ExAC_AF_OTH   109    ExAC_AF_SAS   110    GENE_PHENO   111    FILTER   112    CONTEXT   113    src_vcf_id   114    tumor_bam_uuid   115    normal_bam_uuid   116    case_id   117    GDC_FILTER   118    COSMIC   119    MC3_Overlap   120    GDC_Validation_Status