Immunarch包也可以用于处理单细胞配对链数据，单细胞支持目前处于开发版本。为了访问它，我们需要通过执行以下命令来安装该软件包的最新开发版本：

  1. install.packages ( "devtools" ); 
  2. devtools :: install_github ( "immunomind/immunarch" , ref = "dev" )

在Immunarch中，读取配对链免疫组库数据使用repLoad函数并指定.mode = "paired"参数。目前我们仅支持10X Genomics。

我们可以使用select_barcodes函数通过特定barcode条形码对免疫组库进行子集化，也可以使用select_clusters函数选取创建特定聚类群或特定患者的数据集。

使用Immunarch包内置的单细胞配对链数据集

  1. # 加载所需的R包
  2. library(immunarch)
  4. # 加载内置数据集
  5. data(scdata)
  7. # 查看数据信息
  8. names(scdata)
  9. # [1] "data"       "meta"       "bc_patient" "bc_cluster"
  11. head(scdata$meta)
  12. # A tibble: 1 x 1
  13. # Sample
  14. # <chr> 
  15. #1 flu 
  17. head(scdata$bc_patient)
  18. # GCTGGGTTCAAACCGT-1 CAGTCCTCATGGTAGG-1 GGAAAGCGTCTTCAAG-1 CACAAACGTAAACGCG-1 CATGACATCCTCGCAT-1 
  19. #        "PatientA"         "PatientA"         "PatientA"         "PatientA"         "PatientA" 
  20. # ATCCACCCAAGTCTGT-1 
  21. #        "PatientA" 
  23. head(scdata$bc_cluster)
  24. #CAGTCCTTCGCGCCAA-1 TCGTAGATCTTCGGTC-1 CACACAAGTCAATACC-1 CGATGTAGTCGGCTCA-1 CTGTTTATCCTCTAGC-1 
  25.  #          "Activ"            "Activ"            "Activ"            "Activ"            "Activ" 
  26. # CACACTCCACGACTCG-1 
  27. #           "Activ"

加载配对链数据

要加载您自己的数据集，请使用该repLoad函数。目前，我们仅对 10X Genomics 数据实施了配对链数据支持。将数据集加载到 R 的工作示例：

  1. file_path <- paste0(system.file(package = "immunarch"), "/extdata/sc/flu.csv.gz")
  2. igdata <- repLoad(file_path, .mode = "paired")
  4. ## == Step 1/3: loading repertoire files... ==
  5. ## Processing "<initial>" ...
  6. ##   -- Parsing "/private/var/folders/5d/g0z_tj9n3qd9r2b4tr8pp5sm0000gp/T/RtmpyOULE7/temp_libpath106e61c9ecf7b/immunarch/extdata/sc/flu.csv.gz" -- 10x (filt.contigs)
  7. ## Warning: The following named parsers don't match the column names:
  8. ## sample,barcode,is_cell,contig_id,high_confidence,length,chain,v_gene,d_gene,j_gene,c_gene,full_length,productive,cdr3,cdr3_nt,reads,umis,raw_clonotype_id,raw_consensus_id
  9. ## 
  10. ## == Step 2/3: checking metadata files and merging files... ==
  11. ## 
  12. ## Processing "<initial>" ...
  13. ##   -- Metadata file not found; creating a dummy metadata...
  14. ## 
  15. ## == Step 3/3: processing paired chain data... ==
  16. ## 
  17. ## Done!

查看加载成功后的数据信息

  1. igdata$meta
  2. ## # A tibble: 1 x 1
  3. ##   Sample
  4. ##   <chr> 
  5. ## 1 flu
  8. head(igdata$data[[1]][c(1:7, 16, 17)])
  9. ##   Clones Proportion
  10. ## 1      3      3e-04
  11. ## 2      3      3e-04
  12. ## 3      2      2e-04
  13. ## 4      2      2e-04
  14. ## 5      2      2e-04
  15. ## 6      2      2e-04
  16. ##                                                                                                         CDR3.nt
  17. ## 1          TGTGCACACACCACCGAACTCTATTGTACTAATGGTGTATGCTATGGGGGCTACTTTGACTACTGG;TGCCAACAGTATAATAGTTATTCGTGGACGTTC
  18. ## 2                                     TGTGCGAGGCTATGGGGTTGGGGATTACTCTACTGG;TGCACCTCATATGCAGGCAGCAACAATTTGGTATTC
  19. ## 3                                  TGTACCGCTCATGGTAGGGAGGGTACTTTTGATCTCTGG;TGTCAACAATATGATAATCTCCCTCGGTACACTTTT
  20. ## 4                            TGTGCAAGAGACGACTTTGCTTCGGGGGGTCGACACTTTGGCTGCTGG;TGTCAGCAGTCTGGTAACGCACCTCGAACTTTT
  21. ## 5                                           TGTGCAAGAGATCTGGACTACATGGACGTCTGG;TGTCAACAGAGTTACAGTACCCCTCGAACTTTT
  22. ## 6 TGTGCAAGAGGTTTAATACCCTCAGTTAGTGGCTACGACTACTACTACTACTACGGTATGGACGTCTGG;TGTCAATCAGCAGACAGCAGTGGTACTTATGAGGTATTC
  23. ##                                 CDR3.aa             V.name        D.name
  24. ## 1    CAHTTELYCTNGVCYGGYFDYW;CQQYNSYSWTF    IGHV2-5;IGKV1-5  IGHD2-8;None
  25. ## 2             CARLWGWGLLYW;CTSYAGSNNLVF  IGHV4-59;IGLV2-11 IGHD3-10;None
  26. ## 3            CTAHGREGTFDLW;CQQYDNLPRYTF  IGHV3-15;IGKV1-33 IGHD2-15;None
  27. ## 4          CARDDFASGGRHFGCW;CQQSGNAPRTF   IGHV1-2;IGKV3-20 IGHD3-10;None
  28. ## 5               CARDLDYMDVW;CQQSYSTPRTF IGHV3-13;IGKV1D-39     None;None
  29. ## 6 CARGLIPSVSGYDYYYYYGMDVW;CQSADSSGTYEVF  IGHV3-74;IGLV3-25 IGHD5-12;None
  30. ##        J.name   chain                                                  Barcode
  31. ## 1 IGHJ4;IGKJ1 IGH;IGK AGTAGTCAGTGTACTC-1;GGCGACTGTACCGAGA-1;TTGAACGGTCACCTAA-1
  32. ## 2 IGHJ4;IGLJ2 IGH;IGL AGAGCGACACCTTGTC-1;ATTGGTGAGACCTAGG-1;TCTTCGGAGGTGATTA-1
  33. ## 3 IGHJ3;IGKJ2 IGH;IGK                    CGCGGTATCCTCTAGC-1;TGAGCATCAGGAACGT-1
  34. ## 4 IGHJ4;IGKJ2 IGH;IGK                    ACTGTCCAGACGCAAC-1;CACACTCTCCGTTGTC-1
  35. ## 5 IGHJ6;IGKJ2 IGH;IGK                    AGGTCCGAGTCAAGCG-1;GGCGTGTTCTCTAGGA-1
  36. ## 6 IGHJ6;IGLJ2 IGH;IGL                    ACTGAGTCAAGCGATG-1;CTGCTGTCACGCATCG-1

提取barcode子集

要按barcode条形码对数据进行子集化，请使用该select_barcodes函数。

  1. barcodes <- c("AGTAGTCAGTGTACTC-1", "GGCGACTGTACCGAGA-1", "TTGAACGGTCACCTAA-1")
  3. new_df <- select_barcodes(scdata$data[[1]], barcodes)
  4. new_df
  5. ##                                                                                                CDR3.nt
  6. ## 1 TGTGCACACACCACCGAACTCTATTGTACTAATGGTGTATGCTATGGGGGCTACTTTGACTACTGG;TGCCAACAGTATAATAGTTATTCGTGGACGTTC
  7. ##                              CDR3.aa          V.name D.name      J.name V.end
  8. ## 1 CAHTTELYCTNGVCYGGYFDYW;CQQYNSYSWTF IGHV2-5;IGKV1-5     NA IGHJ4;IGKJ1    NA
  9. ##   D.start D.end J.start VJ.ins VD.ins DJ.ins Sequence   chain raw_clonotype_id
  10. ## 1      NA    NA      NA     NA     NA     NA       NA IGH;IGK               14
  11. ##   ContigID Clones                                                  Barcode
  12. ## 1       NA      3 AGTAGTCAGTGTACTC-1;GGCGACTGTACCGAGA-1;TTGAACGGTCACCTAA-1
  13. ##   Proportion
  14. ## 1          1

提取特定病人的数据集

要创建具有特定病人免疫组库的新数据集，请使用以下select_clusters函数：

  1. scdata_pat <- select_clusters(scdata, scdata$bc_patient, "Patient")
  3. names(scdata_pat$data)
  4. ## [1] "flu_PatientA" "flu_PatientB" "flu_PatientC"
  6. scdata_pat$meta
  7. ## # A tibble: 3 x 3
  8. ##   Sample       Patient.source Patient 
  9. ##   <chr>        <chr>          <chr>   
  10. ## 1 flu_PatientA flu            PatientA
  11. ## 2 flu_PatientB flu            PatientB
  12. ## 3 flu_PatientC flu            PatientC

提取特定聚类群的数据集

要创建具有特定聚类群免疫组库的新数据集，请使用该select_clusters函数。您可以在创建特定于患者的数据集后应用此函数以获取特定于患者的细胞簇特定免疫组库，例如特定患者的记忆 B 细胞组库：

  1. scdata_cl <- select_clusters(scdata_pat, scdata$bc_cluster, "Cluster")
  3. names(scdata_cl$data)
  4. ## [1] "flu_PatientA_Activ"  "flu_PatientA_Memory" "flu_PatientA_Naive" 
  5. ## [4] "flu_PatientB_Activ"  "flu_PatientB_Memory" "flu_PatientB_Naive" 
  6. ## [7] "flu_PatientC_Activ"  "flu_PatientC_Memory" "flu_PatientC_Naive"
  8. scdata_cl$meta
  9. ## # A tibble: 9 x 5
  10. ##   Sample              Patient.source Patient  Cluster.source Cluster
  11. ##   <chr>               <chr>          <chr>    <chr>          <chr>  
  12. ## 1 flu_PatientA_Activ  flu            PatientA flu_PatientA   Activ  
  13. ## 2 flu_PatientA_Memory flu            PatientA flu_PatientA   Memory 
  14. ## 3 flu_PatientA_Naive  flu            PatientA flu_PatientA   Naive  
  15. ## 4 flu_PatientB_Activ  flu            PatientB flu_PatientB   Activ  
  16. ## 5 flu_PatientB_Memory flu            PatientB flu_PatientB   Memory 
  17. ## 6 flu_PatientB_Naive  flu            PatientB flu_PatientB   Naive  
  18. ## 7 flu_PatientC_Activ  flu            PatientC flu_PatientC   Activ  
  19. ## 8 flu_PatientC_Memory flu            PatientC flu_PatientC   Memory 
  20. ## 9 flu_PatientC_Naive  flu            PatientC flu_PatientC   Naive

探索和计算统计数据

对于单细胞配对链数据，immunarch包中的可视化函数可以同样的适用。

  1. p1 <- repOverlap(scdata_cl$data) %>% vis()
  2. p2 <- repDiversity(scdata_cl$data) %>% vis()
  4. target <- c("CARAGYLRGFDYW;CQQYGSSPLTF", "CARATSFYYFHHW;CTSYTTRTTLIF", "CARDLSRGDYFPYFSYHMNVW;CQSDDTANHVIF", "CARGFDTNAFDIW;CTAWDDSLSGVVF", "CTREDYW;CMQTIQLRTF")
  5. p3 <- trackClonotypes(scdata_cl$data, target, .col = "aa") %>% vis()
  6. ## Warning in melt.data.table(.data): id.vars and measure.vars are internally
  7. ## guessed when both are 'NULL'. All non-numeric/integer/logical type columns are
  8. ## considered id.vars, which in this case are columns [CDR3.aa]. Consider providing
  9. ## at least one of 'id' or 'measure' vars in future.
  11. (p1 + p2) / p3

使用immunarch包进行单细胞免疫组库数据分析（三）：处理单细胞配对链数据 - 图1

参考来源：https://immunarch.com/articles/web_only/v21_singlecell.html