Hi there,

I always used TCGAbiolinks to get raw count for TCGA projects like below:

    expquery <- GDCquery(project = "TCGA-KIRC",
                     data.category = "Transcriptome Profiling",
                     data.type = "Gene Expression Quantification",
                     workflow.type = "HTSeq - Counts")
    GDCdownload(expquery,directory = "GDCdata")
    expquery2 <- GDCprepare(expquery,directory = "GDCdata",summarizedExperiment = T)
    expMatrix <- TCGAanalyze_Preprocessing(expquery2)

However, it does not work today and it seems there is no HTSeq - Counts 

    Error in GDCquery(project = "TCGA-KIRC", data.category = "Transcriptome Profiling",  : 
      Please set a valid workflow.type argument from the list below:
      => STAR - Counts

Therefore, I used STAR - Counts to download the data, which has completely different format of what I downloaded before using HTSeq - Count. The expression matrix for each sample has more columns including fpkm_unstranded and tpm_unstranded. 

Actually the data downloaded using STAR - Counts is much more useful but I do not know how to extract the files to a readable expression matrix (ENSEMBL ID as rownames and TCGA tumor barcode as column names) because the GDCprepare() function fails to work on it:

    expquery2 <- GDCprepare(expquery,directory = "GDCdata",summarizedExperiment = T)
    |                                                          |  0%                      
    Error in readr::read_tsv(file = f, col_names = TRUE, progress = FALSE,  : 
      unused argument (show_col_types = FALSE)
    Error in if (value == n) { : argument is of length zero

Anyone has any solutions? Many thanks in advance!
```
projects <- c("TCGA-LGG","TCGA-GBM")

ids <- c("TCGA-76-6661", "TCGA-26-5132", "TCGA-19-1389")


# RNA Transcriptome
query <- GDCquery(
  project = projects,
  data.category = "Transcriptome Profiling",
  data.type = "Gene Expression Quantification",
  workflow.type = "STAR - Counts",
  barcode = ids
)

GDCdownload(query,
            method = "api", 
            directory = "./data",
            files.per.chunk  =  10)

rna <- GDCprepare(query, save = FALSE, summarizedExperiment = T, directory = "./data", remove.files.prepared = F)
rna <- as.data.frame(rna@assays@data@listData)



# DNA Meth Beta Value
query <-GDCquery(project = projects,
                     data.category= "DNA Methylation",
                     platform = "Illumina Human Methylation 450",
                     data.type = "Methylation Beta Value",
                     legacy = FALSE,
                     barcode = ids)

GDCdownload(query,
            method = "api", 
            directory = "./data",
            files.per.chunk  =  10)


met <- GDCprepare(query, save = FALSE, summarizedExperiment = T, directory = "./data", remove.files.prepared = FALSE)


# IDAT Files
query <-GDCquery(project = projects,
                   data.category = "Raw microarray data",
                   data.type = "Raw intensities", 
                   experimental.strategy = "Methylation array", 
                   legacy = TRUE,
                   file.type = ".idat",
                   platform = "Illumina human methylation 450",
                   barcode = ids)

GDCdownload(query,
            method = "api", 
            directory = "./data",
            files.per.chunk  =  10)
```