text: user6509: I want to download RNA-seq datasets of 4 or 5 different types of cancer from the TCGA to investigate what is happening with my gene of interest. The problem is that I'm a physician and I don't know people used to deal with Big Data to help me.
  I'm using a Bioconductor program called TCGAbiolinks and I'm able to download some datasets using the following script:
  if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
  
  BiocManager::install("TCGAbiolinks")
  
  browseVignettes("TCGAbiolinks")
  
  library(TCGAbiolinks)
  library(dplyr)
  library(DT)
  
  query <- GDCquery(project = "TCGA-BRCA", 
                    data.category = "Gene expression",
                    file.type  = "normalized_results",
                    data.type = "Gene expression quantification",
                    experimental.strategy = "RNA-Seq", 
                    barcode = c("TCGA-AN-A046", "TCGA-AC-A23H"),
                    legacy = TRUE)
  
  GDCdownload(query, method = "api", files.per.chunk = 10)
  
  # GDCprepare transforms data in R format
  
  # The default GDCprepare output is a SummarizedExperiment (SE) object,
  # but you can also set to output the data frame.
  
  data <- GDCprepare(query, summarizedExperiment = FALSE)
  
  
  The problems is that: 1) the gene list is not appearing in the first column; 2) I don't know how many datasets I'll be able to download and analyse without a server.. I'm working with my Macbook..
  Can someone familiar with the TCGA website or with the TCGAbiolinks help me with this project? I've been trying to progress in the last months, but the TCGA is a complex website and I'm feeling lost. I use R, I don't know SQL or other programming languages..
  
  user650: I had to play around a little to understand the object, but here's what I got:
  R version 4.0.2 (2020-06-22) -- "Taking Off Again"
  Copyright (C) 2020 The R Foundation for Statistical Computing
  Platform: x86_64-apple-darwin17.0 (64-bit)
  
  R is free software and comes with ABSOLUTELY NO WARRANTY.
  You are welcome to redistribute it under certain conditions.
  Type 'license()' or 'licence()' for distribution details.
  
    Natural language support but running in an English locale
  
  R is a collaborative project with many contributors.
  Type 'contributors()' for more information and
  'citation()' on how to cite R or R packages in publications.
  
  Type 'demo()' for some demos, 'help()' for on-line help, or
  'help.start()' for an HTML browser interface to help.
  Type 'q()' to quit R.
  
  > library(TCGAbiolinks)
  > query <- GDCquery(project = "TCGA-BRCA",
  +                   data.category = "Gene expression",
  +                   file.type  = "normalized_results",
  +                   data.type = "Gene expression quantification",
  +                   experimental.strategy = "RNA-Seq",
  +                   barcode = c("TCGA-AN-A046", "TCGA-AC-A23H"),
  +                   legacy = TRUE)
  --------------------------------------
  o GDCquery: Searching in GDC database
  --------------------------------------
  Genome of reference: hg19
  --------------------------------------------
  oo Accessing GDC. This might take a while...
  --------------------------------------------
  ooo Project: TCGA-BRCA
  --------------------
  oo Filtering results
  --------------------
  ooo By experimental.strategy
  ooo By data.type
  ooo By file.type
  ooo By barcode
  ----------------
  oo Checking data
  ----------------
  ooo Check if there are duplicated cases
  ooo Check if there results for the query
  -------------------
  o Preparing output
  -------------------
  
  
  > GDCdownload(query, method = "api", files.per.chunk = 10)
  Downloading data for project TCGA-BRCA
  GDCdownload will download 3 files. A total of 1.306357 MB
  Downloading chunk 1 of 1 (3 files, size = 1.306357 MB) as Wed_Aug_19_12_20_11_2020_0.tar.gz
  
  Downloading: 580 kB
  
  
  > data <- GDCprepare(query, summarizedExperiment = TRUE)
  ------------------
  oo Reading 3 files
  ------------------
  |====================================================|100%                      Completed after 0 s
  ------------------
  oo Merging 3 files
  ------------------
  Accessing grch37.ensembl.org to get gene information
  Downloading genome information (try:0) Using: Human genes (GRCh37.p13)
  Starting to add information to samples
   => Add clinical information to samples
   => Adding TCGA molecular information from marker papers
   => Information will have prefix 'paper_'
  brca subtype information from:doi.org/10.1016/j.ccell.2018.03.014
  
  
  > head(data)
  class: RangedSummarizedExperiment
  dim: 6 3
  metadata(1): data_release
  assays(1): normalized_count
  rownames(6): A1BG A2M ... RP11-986E7.7 AADAC
  rowData names(3): gene_id entrezgene ensembl_gene_id
  colnames(3): TCGA-AN-A046-01A-21R-A034-07 TCGA-AC-A23H-11A-12R-A157-07
    TCGA-AC-A23H-01A-11R-A157-07
  colData names(83): barcode patient ... paper_PARADIGM Clusters
    paper_Pan-Gyn Clusters
  
  I see from this representation that I can use metadata(data), assays(data), etc to get more information.
  > head(SummarizedExperiment::assays(data))
  List of length 1
  names(1): normalized_count
  
  Alright, so this is a named list. I know how to deal with lists: I can use either the index ([[1]]) or the name ($normalized_count)
  > head(SummarizedExperiment::assays(data)$normalized_count)
               TCGA-AN-A046-01A-21R-A034-07 TCGA-AC-A23H-11A-12R-A157-07 TCGA-AC-A23H-01A-11R-A157-07
  A1BG                             121.9879                      38.5795                      13.6874
  A2M                             9703.8622                   37640.8373                    4767.0490
  NAT1                            8488.0727                     114.8582                     270.4753
  NAT2                               6.0583                       4.2938                       2.3804
  RP11-986E7.7                   33016.6641                   11308.3460                    3894.9639
  AADAC                              1.1359                     219.6976                       1.1902
  
  I can now get more information on the samples using colData(). I'm truncating the output below because there are 83 columns.
  > SummarizedExperiment::colData(data)
  DataFrame with 3 rows and 83 columns
                                                    barcode      patient           sample shortLetterCode          definition sample_submitter_id         sample_type       state
                                                <character>  <character>      <character>     <character>         <character>         <character>         <character> <character>
  TCGA-AN-A046-01A-21R-A034-07 TCGA-AN-A046-01A-21R-A034-07 TCGA-AN-A046 TCGA-AN-A046-01A              TP Primary solid Tumor    TCGA-AN-A046-01A       Primary Tumor    released
  TCGA-AC-A23H-11A-12R-A157-07 TCGA-AC-A23H-11A-12R-A157-07 TCGA-AC-A23H TCGA-AC-A23H-11A              NT Solid Tissue Normal    TCGA-AC-A23H-11A Solid Tissue Normal    released
  TCGA-AC-A23H-01A-11R-A157-07 TCGA-AC-A23H-01A-11R-A157-07 TCGA-AC-A23H TCGA-AC-A23H-01A              TP Primary solid Tumor    TCGA-AC-A23H-01A       Primary Tumor    released
                                                          sample_id   is_ffpe sample_type_id  tissue_type submitter_id days_to_collection oct_embedded                pathology_report_uuid
                                                        <character> <logical>    <character>  <character>  <character>          <integer>  <character>                          <character>
  TCGA-AN-A046-01A-21R-A034-07 57aff09f-0e97-4e65-a227-3dc7a8516367     FALSE             01 Not Reported TCGA-AN-A046                 27         true 1304FB17-A20A-4EBC-9CC8-1554808AC1F6
  TCGA-AC-A23H-11A-12R-A157-07 7df59ca8-7e51-4581-9d7f-8bba0395ce17     FALSE             11 Not Reported TCGA-AC-A23H                478        false                                   NA
  TCGA-AC-A23H-01A-11R-A157-07 d7e3b628-d5fd-4e79-9c4a-6409330fb8a7     FALSE             01 Not Reported TCGA-AC-A23H                478        false A7C7D409-D086-4A9B-8C8F-E7E231D5891D
  
  I can get more information on the genes using rowData():
  > SummarizedExperiment::rowData(data)
  DataFrame with 19947 rows and 3 columns
                    gene_id entrezgene ensembl_gene_id
                <character>  <integer>     <character>
  A1BG                 A1BG          1 ENSG00000121410
  A2M                   A2M          2 ENSG00000175899
  NAT1                 NAT1          9 ENSG00000171428
  NAT2                 NAT2         10 ENSG00000156006
  RP11-986E7.7 RP11-986E7.7         12 ENSG00000273259
  ...                   ...        ...             ...
  RASAL2-AS1     RASAL2-AS1  100302401 ENSG00000224687
  LINC00882       LINC00882  100302640 ENSG00000242759
  FTX                   FTX  100302692 ENSG00000230590
  TICAM2             TICAM2  100302736 ENSG00000243414
  SLC25A5-AS1   SLC25A5-AS1  100303728 ENSG00000224281
  
  You want the assay data as a data.matrix, I guess. That'd be:
  > data_as_matrix <- data.matrix(SummarizedExperiment::assays(data)$normalized_count)
  
  My session Info:
  > sessionInfo()
  R version 4.0.2 (2020-06-22)
  Platform: x86_64-apple-darwin17.0 (64-bit)
  Running under: macOS Catalina 10.15.5
  
  Matrix products: default
  BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
  LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
  
  locale:
  [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
  
  attached base packages:
  [1] stats     graphics  grDevices utils     datasets  methods   base
  
  other attached packages:
  [1] TCGAbiolinks_2.16.1
  
  loaded via a namespace (and not attached):
    [1] bitops_1.0-6                matrixStats_0.56.0          bit64_0.9-7.1               doParallel_1.0.15           RColorBrewer_1.1-2          progress_1.2.2
    [7] httr_1.4.2                  GenomeInfoDb_1.24.2         backports_1.1.8             tools_4.0.2                 R6_2.4.1                    DBI_1.1.0
   [13] BiocGenerics_0.34.0         colorspace_1.4-1            tidyselect_1.1.0            gridExtra_2.3               prettyunits_1.1.1           bit_1.1-15.2
   [19] curl_4.3                    compiler_4.0.2              rvest_0.3.6                 Biobase_2.48.0              xml2_1.3.2                  DelayedArray_0.14.0
   [25] rtracklayer_1.48.0          scales_1.1.1                survMisc_0.5.5              readr_1.3.1                 genefilter_1.70.0           askpass_1.1
   [31] rappdirs_0.3.1              stringr_1.4.0               digest_0.6.25               Rsamtools_2.4.0             foreign_0.8-80              R.utils_2.9.2
   [37] rio_0.5.16                  XVector_0.28.0              pkgconfig_2.0.3             dbplyr_1.4.4                readxl_1.3.1                rlang_0.4.7
   [43] ggthemes_4.2.0              RSQLite_2.2.0               generics_0.0.2              zoo_1.8-8                   jsonlite_1.7.0              BiocParallel_1.22.0
   [49] zip_2.0.4                   car_3.0-8                   dplyr_1.0.0                 R.oo_1.23.0                 RCurl_1.98-1.2              magrittr_1.5
   [55] GenomeInfoDbData_1.2.3      Matrix_1.2-18               Rcpp_1.0.5                  munsell_0.5.0               S4Vectors_0.26.1            abind_1.4-5
   [61] lifecycle_0.2.0             R.methodsS3_1.8.0           stringi_1.4.6               carData_3.0-4               SummarizedExperiment_1.18.1 zlibbioc_1.34.0
   [67] plyr_1.8.6                  BiocFileCache_1.12.0        grid_4.0.2                  blob_1.2.1                  parallel_4.0.2              ggrepel_0.8.2
   [73] forcats_0.5.0               crayon_1.3.4                survminer_0.4.8             lattice_0.20-41             haven_2.3.1                 Biostrings_2.56.0
   [79] splines_4.0.2               GenomicFeatures_1.40.0      annotate_1.66.0             hms_0.5.3                   knitr_1.29                  pillar_1.4.6
   [85] ggpubr_0.4.0                GenomicRanges_1.40.0        ggsignif_0.6.0              codetools_0.2-16            biomaRt_2.44.1              stats4_4.0.2
   [91] XML_3.99-0.5                glue_1.4.1                  downloader_0.4              data.table_1.13.0           vctrs_0.3.2                 selectr_0.4-2
   [97] foreach_1.5.0               cellranger_1.1.0            gtable_0.3.0                openssl_1.4.2               purrr_0.3.4                 tidyr_1.1.0
  [103] km.ci_0.5-2                 assertthat_0.2.1            ggplot2_3.3.2               openxlsx_4.1.5              xfun_0.16                   xtable_1.8-4
  [109] broom_0.7.0                 rstatix_0.6.0               survival_3.1-12             tibble_3.0.3                iterators_1.0.12            KMsurv_0.1-5
  [115] GenomicAlignments_1.24.0    AnnotationDbi_1.50.1        memoise_1.1.0               IRanges_2.22.2              ellipsis_0.3.1
  
  
original_text: 6509: <p>I want to download RNA-seq datasets of 4 or 5 different types of cancer from the TCGA to investigate what is happening with my gene of interest. The problem is that I'm a physician and I don't know people used to deal with Big Data to help me.</p>
  <p>I'm using a Bioconductor program called TCGAbiolinks and I'm able to download some datasets using the following script:</p>
  <pre><code>if (!requireNamespace(&quot;BiocManager&quot;, quietly = TRUE))
    install.packages(&quot;BiocManager&quot;)
  
  BiocManager::install(&quot;TCGAbiolinks&quot;)
  
  browseVignettes(&quot;TCGAbiolinks&quot;)
  
  library(TCGAbiolinks)
  library(dplyr)
  library(DT)
  
  query &lt;- GDCquery(project = &quot;TCGA-BRCA&quot;, 
                    data.category = &quot;Gene expression&quot;,
                    file.type  = &quot;normalized_results&quot;,
                    data.type = &quot;Gene expression quantification&quot;,
                    experimental.strategy = &quot;RNA-Seq&quot;, 
                    barcode = c(&quot;TCGA-AN-A046&quot;, &quot;TCGA-AC-A23H&quot;),
                    legacy = TRUE)
  
  GDCdownload(query, method = &quot;api&quot;, files.per.chunk = 10)
  
  # GDCprepare transforms data in R format
  
  # The default GDCprepare output is a SummarizedExperiment (SE) object,
  # but you can also set to output the data frame.
  
  data &lt;- GDCprepare(query, summarizedExperiment = FALSE)
  </code></pre>
  <p><a href="https://i.stack.imgur.com/eJLGY.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/eJLGY.png" alt="enter image description here" /></a>
  The problems is that: 1) the gene list is not appearing in the first column; 2) I don't know how many datasets I'll be able to download and analyse without a server.. I'm working with my Macbook..</p>
  <p>Can someone familiar with the TCGA website or with the TCGAbiolinks help me with this project? I've been trying to progress in the last months, but the TCGA is a complex website and I'm feeling lost. I use R, I don't know SQL or other programming languages..</p>
   650: <p>I had to play around a little to understand the object, but here's what I got:</p>
  <pre><code>R version 4.0.2 (2020-06-22) -- &quot;Taking Off Again&quot;
  Copyright (C) 2020 The R Foundation for Statistical Computing
  Platform: x86_64-apple-darwin17.0 (64-bit)
  
  R is free software and comes with ABSOLUTELY NO WARRANTY.
  You are welcome to redistribute it under certain conditions.
  Type 'license()' or 'licence()' for distribution details.
  
    Natural language support but running in an English locale
  
  R is a collaborative project with many contributors.
  Type 'contributors()' for more information and
  'citation()' on how to cite R or R packages in publications.
  
  Type 'demo()' for some demos, 'help()' for on-line help, or
  'help.start()' for an HTML browser interface to help.
  Type 'q()' to quit R.
  
  &gt; library(TCGAbiolinks)
  &gt; query &lt;- GDCquery(project = &quot;TCGA-BRCA&quot;,
  +                   data.category = &quot;Gene expression&quot;,
  +                   file.type  = &quot;normalized_results&quot;,
  +                   data.type = &quot;Gene expression quantification&quot;,
  +                   experimental.strategy = &quot;RNA-Seq&quot;,
  +                   barcode = c(&quot;TCGA-AN-A046&quot;, &quot;TCGA-AC-A23H&quot;),
  +                   legacy = TRUE)
  --------------------------------------
  o GDCquery: Searching in GDC database
  --------------------------------------
  Genome of reference: hg19
  --------------------------------------------
  oo Accessing GDC. This might take a while...
  --------------------------------------------
  ooo Project: TCGA-BRCA
  --------------------
  oo Filtering results
  --------------------
  ooo By experimental.strategy
  ooo By data.type
  ooo By file.type
  ooo By barcode
  ----------------
  oo Checking data
  ----------------
  ooo Check if there are duplicated cases
  ooo Check if there results for the query
  -------------------
  o Preparing output
  -------------------
  </code></pre>
  <hr />
  <pre><code>&gt; GDCdownload(query, method = &quot;api&quot;, files.per.chunk = 10)
  Downloading data for project TCGA-BRCA
  GDCdownload will download 3 files. A total of 1.306357 MB
  Downloading chunk 1 of 1 (3 files, size = 1.306357 MB) as Wed_Aug_19_12_20_11_2020_0.tar.gz
  
  Downloading: 580 kB
  </code></pre>
  <hr />
  <pre><code>&gt; data &lt;- GDCprepare(query, summarizedExperiment = TRUE)
  ------------------
  oo Reading 3 files
  ------------------
  |====================================================|100%                      Completed after 0 s
  ------------------
  oo Merging 3 files
  ------------------
  Accessing grch37.ensembl.org to get gene information
  Downloading genome information (try:0) Using: Human genes (GRCh37.p13)
  Starting to add information to samples
   =&gt; Add clinical information to samples
   =&gt; Adding TCGA molecular information from marker papers
   =&gt; Information will have prefix 'paper_'
  brca subtype information from:doi.org/10.1016/j.ccell.2018.03.014
  </code></pre>
  <hr />
  <pre><code>&gt; head(data)
  class: RangedSummarizedExperiment
  dim: 6 3
  metadata(1): data_release
  assays(1): normalized_count
  rownames(6): A1BG A2M ... RP11-986E7.7 AADAC
  rowData names(3): gene_id entrezgene ensembl_gene_id
  colnames(3): TCGA-AN-A046-01A-21R-A034-07 TCGA-AC-A23H-11A-12R-A157-07
    TCGA-AC-A23H-01A-11R-A157-07
  colData names(83): barcode patient ... paper_PARADIGM Clusters
    paper_Pan-Gyn Clusters
  </code></pre>
  <p>I see from this representation that I can use <code>metadata(data)</code>, <code>assays(data)</code>, etc to get more information.</p>
  <pre><code>&gt; head(SummarizedExperiment::assays(data))
  List of length 1
  names(1): normalized_count
  </code></pre>
  <p>Alright, so this is a named list. I know how to deal with lists: I can use either the index (<code>[[1]]</code>) or the name (<code>$normalized_count</code>)</p>
  <pre><code>&gt; head(SummarizedExperiment::assays(data)$normalized_count)
               TCGA-AN-A046-01A-21R-A034-07 TCGA-AC-A23H-11A-12R-A157-07 TCGA-AC-A23H-01A-11R-A157-07
  A1BG                             121.9879                      38.5795                      13.6874
  A2M                             9703.8622                   37640.8373                    4767.0490
  NAT1                            8488.0727                     114.8582                     270.4753
  NAT2                               6.0583                       4.2938                       2.3804
  RP11-986E7.7                   33016.6641                   11308.3460                    3894.9639
  AADAC                              1.1359                     219.6976                       1.1902
  </code></pre>
  <p>I can now get more information on the samples using <code>colData()</code>. I'm truncating the output below because there are 83 columns.</p>
  <pre><code>&gt; SummarizedExperiment::colData(data)
  DataFrame with 3 rows and 83 columns
                                                    barcode      patient           sample shortLetterCode          definition sample_submitter_id         sample_type       state
                                                &lt;character&gt;  &lt;character&gt;      &lt;character&gt;     &lt;character&gt;         &lt;character&gt;         &lt;character&gt;         &lt;character&gt; &lt;character&gt;
  TCGA-AN-A046-01A-21R-A034-07 TCGA-AN-A046-01A-21R-A034-07 TCGA-AN-A046 TCGA-AN-A046-01A              TP Primary solid Tumor    TCGA-AN-A046-01A       Primary Tumor    released
  TCGA-AC-A23H-11A-12R-A157-07 TCGA-AC-A23H-11A-12R-A157-07 TCGA-AC-A23H TCGA-AC-A23H-11A              NT Solid Tissue Normal    TCGA-AC-A23H-11A Solid Tissue Normal    released
  TCGA-AC-A23H-01A-11R-A157-07 TCGA-AC-A23H-01A-11R-A157-07 TCGA-AC-A23H TCGA-AC-A23H-01A              TP Primary solid Tumor    TCGA-AC-A23H-01A       Primary Tumor    released
                                                          sample_id   is_ffpe sample_type_id  tissue_type submitter_id days_to_collection oct_embedded                pathology_report_uuid
                                                        &lt;character&gt; &lt;logical&gt;    &lt;character&gt;  &lt;character&gt;  &lt;character&gt;          &lt;integer&gt;  &lt;character&gt;                          &lt;character&gt;
  TCGA-AN-A046-01A-21R-A034-07 57aff09f-0e97-4e65-a227-3dc7a8516367     FALSE             01 Not Reported TCGA-AN-A046                 27         true 1304FB17-A20A-4EBC-9CC8-1554808AC1F6
  TCGA-AC-A23H-11A-12R-A157-07 7df59ca8-7e51-4581-9d7f-8bba0395ce17     FALSE             11 Not Reported TCGA-AC-A23H                478        false                                   NA
  TCGA-AC-A23H-01A-11R-A157-07 d7e3b628-d5fd-4e79-9c4a-6409330fb8a7     FALSE             01 Not Reported TCGA-AC-A23H                478        false A7C7D409-D086-4A9B-8C8F-E7E231D5891D
  </code></pre>
  <p>I can get more information on the genes using <code>rowData()</code>:</p>
  <pre><code>&gt; SummarizedExperiment::rowData(data)
  DataFrame with 19947 rows and 3 columns
                    gene_id entrezgene ensembl_gene_id
                &lt;character&gt;  &lt;integer&gt;     &lt;character&gt;
  A1BG                 A1BG          1 ENSG00000121410
  A2M                   A2M          2 ENSG00000175899
  NAT1                 NAT1          9 ENSG00000171428
  NAT2                 NAT2         10 ENSG00000156006
  RP11-986E7.7 RP11-986E7.7         12 ENSG00000273259
  ...                   ...        ...             ...
  RASAL2-AS1     RASAL2-AS1  100302401 ENSG00000224687
  LINC00882       LINC00882  100302640 ENSG00000242759
  FTX                   FTX  100302692 ENSG00000230590
  TICAM2             TICAM2  100302736 ENSG00000243414
  SLC25A5-AS1   SLC25A5-AS1  100303728 ENSG00000224281
  </code></pre>
  <p>You want the assay data as a data.matrix, I guess. That'd be:</p>
  <pre><code>&gt; data_as_matrix &lt;- data.matrix(SummarizedExperiment::assays(data)$normalized_count)
  </code></pre>
  <p>My session Info:</p>
  <pre><code>&gt; sessionInfo()
  R version 4.0.2 (2020-06-22)
  Platform: x86_64-apple-darwin17.0 (64-bit)
  Running under: macOS Catalina 10.15.5
  
  Matrix products: default
  BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
  LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
  
  locale:
  [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
  
  attached base packages:
  [1] stats     graphics  grDevices utils     datasets  methods   base
  
  other attached packages:
  [1] TCGAbiolinks_2.16.1
  
  loaded via a namespace (and not attached):
    [1] bitops_1.0-6                matrixStats_0.56.0          bit64_0.9-7.1               doParallel_1.0.15           RColorBrewer_1.1-2          progress_1.2.2
    [7] httr_1.4.2                  GenomeInfoDb_1.24.2         backports_1.1.8             tools_4.0.2                 R6_2.4.1                    DBI_1.1.0
   [13] BiocGenerics_0.34.0         colorspace_1.4-1            tidyselect_1.1.0            gridExtra_2.3               prettyunits_1.1.1           bit_1.1-15.2
   [19] curl_4.3                    compiler_4.0.2              rvest_0.3.6                 Biobase_2.48.0              xml2_1.3.2                  DelayedArray_0.14.0
   [25] rtracklayer_1.48.0          scales_1.1.1                survMisc_0.5.5              readr_1.3.1                 genefilter_1.70.0           askpass_1.1
   [31] rappdirs_0.3.1              stringr_1.4.0               digest_0.6.25               Rsamtools_2.4.0             foreign_0.8-80              R.utils_2.9.2
   [37] rio_0.5.16                  XVector_0.28.0              pkgconfig_2.0.3             dbplyr_1.4.4                readxl_1.3.1                rlang_0.4.7
   [43] ggthemes_4.2.0              RSQLite_2.2.0               generics_0.0.2              zoo_1.8-8                   jsonlite_1.7.0              BiocParallel_1.22.0
   [49] zip_2.0.4                   car_3.0-8                   dplyr_1.0.0                 R.oo_1.23.0                 RCurl_1.98-1.2              magrittr_1.5
   [55] GenomeInfoDbData_1.2.3      Matrix_1.2-18               Rcpp_1.0.5                  munsell_0.5.0               S4Vectors_0.26.1            abind_1.4-5
   [61] lifecycle_0.2.0             R.methodsS3_1.8.0           stringi_1.4.6               carData_3.0-4               SummarizedExperiment_1.18.1 zlibbioc_1.34.0
   [67] plyr_1.8.6                  BiocFileCache_1.12.0        grid_4.0.2                  blob_1.2.1                  parallel_4.0.2              ggrepel_0.8.2
   [73] forcats_0.5.0               crayon_1.3.4                survminer_0.4.8             lattice_0.20-41             haven_2.3.1                 Biostrings_2.56.0
   [79] splines_4.0.2               GenomicFeatures_1.40.0      annotate_1.66.0             hms_0.5.3                   knitr_1.29                  pillar_1.4.6
   [85] ggpubr_0.4.0                GenomicRanges_1.40.0        ggsignif_0.6.0              codetools_0.2-16            biomaRt_2.44.1              stats4_4.0.2
   [91] XML_3.99-0.5                glue_1.4.1                  downloader_0.4              data.table_1.13.0           vctrs_0.3.2                 selectr_0.4-2
   [97] foreach_1.5.0               cellranger_1.1.0            gtable_0.3.0                openssl_1.4.2               purrr_0.3.4                 tidyr_1.1.0
  [103] km.ci_0.5-2                 assertthat_0.2.1            ggplot2_3.3.2               openxlsx_4.1.5              xfun_0.16                   xtable_1.8-4
  [109] broom_0.7.0                 rstatix_0.6.0               survival_3.1-12             tibble_3.0.3                iterators_1.0.12            KMsurv_0.1-5
  [115] GenomicAlignments_1.24.0    AnnotationDbi_1.50.1        memoise_1.1.0               IRanges_2.22.2              ellipsis_0.3.1
  </code></pre>
  