[R] Help with DNA Methylation Analysis

Caitlin b|oprogr@mmer @end|ng |rom gm@||@com
Mon Aug 27 02:41:50 CEST 2018 

No worries Spencer. There is no downloaded data? Nothing is physically
stored on your hard drive? The dot in the path would be interpreted (no pun
intended!) as something like the following:

If the TCGA data was stored in a file named "tcga_data.dat" and it was in a
directory named "C:\spencer", the 4th line of that script would set the
path to "C:\spencer\tcga_data.dat" if you ran the script from that same
folder. If your tcga data is not stored in the same file from which the
script is being ran, it won't find any data to work with. Does this help?


On Sun, Aug 26, 2018 at 5:34 PM Spencer Brackett <
spbrackett20 using saintjosephhs.com> wrote:

> Caitlin,
>
>   Forgive me, but I’m not quite sure exactly what your question is asking.
> The data is originally from the TCGA and I have it downloaded onto another
> R script. I opened a new script to perform the functions I posted to this
> forum because I was unable to input any other commands into the console....
> due to the fact that the translated data filled the entirety of said
> consule. Perhaps overloaded it? Regardless, I was unable to input any
> further commands.
>
> -Spencer Brackett
>
>
> On Sun, Aug 26, 2018 at 8:27 PM Caitlin <bioprogrammer using gmail.com> wrote:
>
>> You're welcome Spencer :)
>>
>> The 4th line:
>>
>> path <– "."
>>
>> refers to the current directory (the dot in other words). Is the data
>> stored in the same directory where the code is being run?
>>
>>
>>
>> On Sun, Aug 26, 2018 at 5:22 PM Spencer Brackett <
>> spbrackett20 using saintjosephhs.com> wrote:
>>
>>>  Thank you! I will make note of that. Unfortunately, lines 1 and 4 of
>>> the first portion of this analysis appear to be where the error begins...
>>> to which several subsequent lines also come up as ‘errored’. Perhaps this
>>> is an issue of the capitalization and/or spacing (something within the
>>> text)? The proposed method for methylation data extraction is based on the
>>> first third of the following TCGA workflow:
>>> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302158/#!po=0.0715308
>>>
>>> Best,
>>>
>>> Spencer Brackett
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> On Sun, Aug 26, 2018 at 8:07 PM Caitlin <bioprogrammer using gmail.com> wrote:
>>>
>>>> Hi Spencer.
>>>>
>>>> Should you capitalize the following library import?
>>>>
>>>> library(summarizedExperiment)
>>>>
>>>> In other words, I think that line should be:
>>>>
>>>> library(SummarizedExperiment)
>>>>
>>>> Hope this helps.
>>>>
>>>> ~Caitlin
>>>>
>>>>
>>>>
>>>>
>>>> On Sun, Aug 26, 2018 at 2:09 PM Spencer Brackett <
>>>> spbrackett20 using saintjosephhs.com> wrote:
>>>>
>>>>> Good evening,
>>>>>
>>>>>   I am attempting to run the following analysis on TCGA data, however
>>>>> something is being reported as an error in my arguments... any ideas
>>>>> as to
>>>>> what is incorrect in the following? Thanks!
>>>>>
>>>>> 1 library(TCGAbiolinks)
>>>>> 2
>>>>> 3 # Download the DNA methylation data: HumanMethylation450 LGG and GBM.
>>>>> 4 path <– "."
>>>>> 5
>>>>> 6 query.met <– TCGAquery(tumor = c("LGG","GBM"),"HumanMethylation450",
>>>>> level = 3)
>>>>> 7 TCGAdownload(query.met, path = path )
>>>>> 8 met <– TCGAprepare(query = query.met,dir = path,
>>>>> 9                      add.subtype = TRUE, add.clinical = TRUE,
>>>>> 10                    summarizedExperiment = TRUE,
>>>>> 11                      save = TRUE, filename = "lgg_gbm_met.rda")
>>>>> 12
>>>>> 13 # Download the expression data: IlluminaHiSeq_RNASeqV2 LGG and GBM.
>>>>> 14 query.exp <– TCGAquery(tumor = c("lgg","gbm"), platform =
>>>>> "IlluminaHiSeq_
>>>>> RNASeqV2",level = 3)
>>>>> 15
>>>>> 16 TCGAdownload(query.exp,path = path, type = "rsem.genes.normalized_
>>>>> results")
>>>>> 17
>>>>> 18 exp <– TCGAprepare(query = query.exp, dir = path,
>>>>> 19                    summarizedExperiment = TRUE,
>>>>> 20                      add.subtype = TRUE, add.clinical = TRUE,
>>>>> 21                    type = "rsem.genes.normalized_results",
>>>>> 22                      save = T,filename = "lgg_gbm_exp.rda")
>>>>>
>>>>> To download data on DNA methylation and gene expression…
>>>>>
>>>>> 1 library(summarizedExperiment)
>>>>> 2 # get expression matrix
>>>>> 3 data <– assay(exp)
>>>>> 4
>>>>> 5 # get sample information
>>>>> 6 sample.info <– colData(exp)
>>>>> 7
>>>>> 8 # get genes information
>>>>> 9 genes.info <– rowRanges(exp)
>>>>>
>>>>> Following stepwise procedure for obtaining GBM and LGG clinical data…
>>>>>
>>>>> 1 # get clinical patient data for GBM samples
>>>>> 2 gbm_clin <– TCGAquery_clinic("gbm","clinical_patient")
>>>>> 3
>>>>> 4 # get clinical patient data for LGG samples
>>>>> 5 lgg_clin <– TCGAquery_clinic("lgg","clinical_patient")
>>>>> 6
>>>>> 7 # Bind the results, as the columns might not be the same,
>>>>> 8 # we will plyr rbind.fill , to have all columns from both files
>>>>> 9 clinical <– plyr::rbind.fill(gbm_clin ,lgg_clin)
>>>>> 10
>>>>> 11 # Other clinical files can be downloaded,
>>>>> 12 # Use ?TCGAquery_clinic for more information
>>>>> 13 clin_radiation <– TCGAquery_clinic("lgg","clinical_radiation")
>>>>> 14
>>>>> 15 # Also, you can get clinical information from different tumor types.
>>>>> 16 # For example sample 1 is GBM, sample 2 and 3 are TGCT
>>>>> 17 data <– TCGAquery_clinic(clinical_data_type = "clinical_patient",
>>>>> 18    samples = c("TCGA-06-5416-01A-01D-1481-05",
>>>>> 19  "TCGA-2G-AAEW-01A-11D-A42Z-05",
>>>>> 20  "TCGA-2G-AAEX-01A-11D-A42Z-05"))
>>>>>
>>>>>
>>>>> # Searching idat file for DNA methylation
>>>>> query <- GDCquery(project = "TCGA-GBM",
>>>>>                  data.category = "Raw microarray data",
>>>>>                  data.type = "Raw intensities",
>>>>>                  experimental.strategy = "Methylation array",
>>>>>                  legacy = TRUE,
>>>>>                  file.type = ".idat",
>>>>>                  platform = "Illumina Human Methylation 450")
>>>>>
>>>>> **Repeat for LGG**
>>>>>
>>>>> To access mutational information concerning TMZ methylation…
>>>>>
>>>>> > mutation <– TCGAquery_maf(tumor = "lgg")
>>>>> 2   Getting maf tables
>>>>> 3   Source: https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files
>>>>> 4   We found these maf files below:
>>>>> 5       MAF.File.Name
>>>>> 6   2             hgsc.bcm.edu_LGG.IlluminaGA_DNASeq.1.somatic.maf
>>>>> 7
>>>>> 8   3
>>>>> LGG_FINAL_ANALYSIS.aggregated.capture.tcga.uuid.curated.somatic.maf
>>>>> 9
>>>>> 10       Archive.Name Deploy.Date
>>>>> 11   2 hgsc.bcm.edu_LGG.IlluminaGA_DNASeq_automated.Level_2.1.0.0
>>>>>   10-DEC-13
>>>>> 12   3 broad.mit.edu_LGG.IlluminaGA_DNASeq_curated.Level_2.1.3.0
>>>>>  24-DEC-14
>>>>> 13
>>>>> 14   Please, select the line that you want to download: 3
>>>>>
>>>>> **Repeat this for GBM***
>>>>>
>>>>> Selecting specified lines to download…
>>>>>
>>>>> 1 gbm.subtypes <− TCGAquery_subtype(tumor = "gbm")
>>>>> 2 lgg.subtypes <− TCGAquery_subtype(tumor = "lgg”)
>>>>>
>>>>>
>>>>>
>>>>> Downloading data via the Bioconductor package RTCGAtoolbox…
>>>>>
>>>>> library(RTCGAToolbox)
>>>>> 2
>>>>> 3 # Get the last run dates
>>>>> 4 lastRunDate <− getFirehoseRunningDates()[1]
>>>>> 5 lastAnalyseDate <− getFirehoseAnalyzeDates(1)
>>>>> 6
>>>>> 7 # get DNA methylation data, RNAseq2 and clinical data for LGG
>>>>> 8 lgg.data <− getFirehoseData(dataset = "LGG",
>>>>> 9       gistic2_Date = getFirehoseAnalyzeDates(1), runDate =
>>>>> lastRunDate,
>>>>> 10       Methylation = TRUE, RNAseq2_Gene_Norm = TRUE, Clinic = TRUE,
>>>>> 11       Mutation = T,
>>>>> 12       fileSizeLimit = 10000)
>>>>> 13
>>>>> 14 # get DNA methylation data, RNAseq2 and clinical data for GBM
>>>>> 15 gbm.data <− getFirehoseData(dataset = "GBM",
>>>>> 16       runDate = lastDate, gistic2_Date = getFirehoseAnalyzeDates(1),
>>>>> 17       Methylation = TRUE, Clinic = TRUE, RNAseq2_Gene_Norm = TRUE,
>>>>> 18       fileSizeLimit = 10000)
>>>>> 19
>>>>> 20 # To access the data you should use the getData function
>>>>> 21 # or simply access with @ (for example gbm.data using Clinical)
>>>>> 22 gbm.mut <− getData(gbm.data,"Mutations")
>>>>> 23 gbm.clin <− getData(gbm.data,"Clinical")
>>>>> 24 gbm.gistic <− getData(gbm.data,"GISTIC")
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> Genomic Analysis/Final data extraction:
>>>>>
>>>>> Enable “getData” to access the data
>>>>>
>>>>> Obtaining GISTIC results…
>>>>>
>>>>> 1 # Download GISTIC results
>>>>> 2 gistic <− getFirehoseData("GBM",gistic2_Date ="20141017" )
>>>>> 3
>>>>> 4 # get GISTIC results
>>>>> 5 gistic.allbygene <− gistic using GISTIC@AllByGene
>>>>> 6 gistic.thresholedbygene <− gistic using GISTIC@ThresholedByGene
>>>>>
>>>>> Repeat this procedure to obtain LGG GISTIC results.
>>>>>
>>>>> ***Please ignore the 'non-coded' text as they are procedural
>>>>> steps/classifications***
>>>>>
>>>>>         [[alternative HTML version deleted]]
>>>>>
>>>>> ______________________________________________
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>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>>> PLEASE do read the posting guide
>>>>> http://www.R-project.org/posting-guide.html
>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>>
>>>>

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