[R] Help with DNA Methylation Analysis

Spencer Brackett @pbr@ckett20 @end|ng |rom @@|ntjo@ephh@@com
Mon Aug 27 02:49:48 CEST 2018


Caitlin,

 Perhaps that is the problem. To be more specific, the data was transferred
from the TCGA database to a CSV file... there are technically two separate
files (CSV) for this analysis.... one for GBM and one for LGG. Both CVS
files were then individually downloaded onto my open R console. Upon
arranging them with the summary () function, the data expanded and took up
the whole console page... even seemingly abrogating the arguments which
allowed for the data to be downloaded onto R in the first place. Are you
suggesting that I would need to utilize a flash drive to successfully
utilize the function you suggested? Or could I perhaps do so with the CSV
field I mentioned? If so, how?

-Spencer B

On Sun, Aug 26, 2018 at 8:42 PM Caitlin <bioprogrammer using gmail.com> wrote:

> 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]]
>>>>>>
>>>>>> ______________________________________________
>>>>>> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>>>> 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.
>>>>>>
>>>>>

	[[alternative HTML version deleted]]




More information about the R-help mailing list