In marziasettino/MMRFBiolinks: R package for integrative analysis of MMRF-CoMMpass data

library(MMRFBiolinks)

devtools::load_all(".")

library(TCGAbiolinks)
library(SummarizedExperiment)
library(dplyr)
library(DT)
library(png)
library(grid)

Analyze data from MMRF-COMMPASS database available at GDC Data Portal

MMRFGDC_prepare

Reads the data downloaded and prepare it into an R object (A summarizedExperiment or a data.frame)

Arguments | Description -----|----- query|A query for GDCquery function save|Save result as RData object it it is TRUE save.filename|Name of the file to be save if empty an automatic will be created directory|Directory/Folder where the data was downloaded. Default: GDCdata summarizedExperiment|Create a summarizedExperiment if it is TRUE (Default TRUE) remove.files.prepared|Remove the files read if it is TRUE (Default FALSE). This argument will be considered only if save argument is set to true add.gistic2.mut|If a list of genes (gene symbol) is given, columns with gistic2 results from GDAC firehose (hg19) and a column indicating if there is or not mutation in that gene (hg38) (TRUE or FALSE - use the MAF file for more information) will be added to the sample matrix in the summarized Experiment object If mut.pipeline|If add.gistic2.mut is not NULL this field will be taken in consideration. Four separate variant calling pipelines are implemented for GDC data harmonization. Options: muse, varscan2, somaticsniper, MuTect2. For more information: https://gdc-docs.nci.nih.gov/Data/Bioinformatics_Pipelines/DNA_Seq_Variant_Calling_Pipeline/ mutant_variant_classification|List of mutant_variant_classification that will be consider a sample mutant or not. Default: "Frame_Shift_Del", "Frame_Shift_Ins", "Missense_Mutation", "Nonsense_Mutation", "Splice_Site", "In_Frame_Del", "In_Frame_Ins", "Translation_Start_Site", "Nonstop_Mutation"

Example:

# You can define a list of samples to query and download providing relative TCGA barcodes.
listSamples <- c("MMRF_2473","MMRF_2111",
                 "MMRF_2362","MMRF_1824",
                 "MMRF_1458","MRF_1361",
                 "MMRF_2203","MMRF_2762",
                 "MMRF_2680","MMRF_1797")


# Query platform Illumina HiSeq with a list of barcode 
query <- GDCquery(project = "MMRF-COMMPASS", 
                               data.category = "Transcriptome Profiling",
                               data.type = "Gene Expression Quantification",
                               experimental.strategy = "RNA-Seq",
                               workflow.type="HTSeq - FPKM",
                               barcode = listSamples)

# Download 
GDCdownload(query)

# Prepare expression matrix with geneID in the rows and samples (barcode) in the columns
MMRnaseqSE <- MMRFGDC_prepare(query,
                              save = TRUE ,
                              save.filename = "RNASeqSE.rda" ,
                              directory = "GDCdata",
                              summarizedExperiment = TRUE)

MMRnaseqSE<-MMRnaseqSE.filt

# For gene expression if you need to see a boxplot correlation and AAIC plot to define outliers you can run
MMRFdataPrepro <- TCGAanalyze_Preprocessing(MMRnaseqSE)

MMRFdataPrepro<-MMRFdataPrepro.filt

knitr::include_graphics('img/PreprocessingOutput.png')

MMRFclin <- MMRFqueryGDC_clinic(type = "clinical")

MMRFclin<-clin.mm

#MMRnaseqSE is a matrix of Gene expression (genes in rows, samples in cols) from MMRFGDC_prepare
tokenStop<- 1
tabSurvKMcomplete <- NULL
for( i in 1: round(nrow(MMRFdataPrepro)/500)){
    message( paste( i, "of ", round(nrow(MMRFdataPrepro)/100)))
    tokenStart <- tokenStop
    tokenStop <-100*i
    tabSurvKM <- TCGAanalyze_SurvivalKM (MMRFclin,
                                         MMRFdataPrepro,
                                         Genelist = rownames(MMRFdataPrepro)[tokenStart:tokenStop],
                                        Survresult = F,ThreshTop=0.76,ThreshDown=0.33)
    tabSurvKMcomplete <- rbind(tabSurvKMcomplete,tabSurvKM)
}
tabSurvKMcomplete <- tabSurvKMcomplete[tabSurvKMcomplete$pvalue < 0.01,]

tabSurvKMcomplete %>% datatable(options = list(scrollX = TRUE, keys = TRUE))

Analyze data from MMRF-COMMPASS database available at MMRF-CoMMpass Researcher Gateway

You can easily analyze data using following functions:

MMRFRG_GetBorPlot

draws plot of the Best Overall Response to the Treatment.

The useful arguments for MMRFRG_GetBorPlot are:

Arguments | Description -----|----- therapyname|is a string containing the list of therapy for filtering data treat.resp|is a data.frame of clinical information downloaded from MMRF-Commpass Researcher Gateway topN|is the top number of case count that the user want to deal dpi|is the figure dpi filename|is the name of png file width|is the image width height|is the image height

Examples:

MMRFRG_GetBorPlot(clinMMGateway)

knitr::include_graphics('img/MMRF_TreatResp.png')

MMRFRG_GetBorPlot(clinMMGateway,"Bortezomib")

knitr::include_graphics('img/MMRF_TreatRespFilt.png')

MMRFRG_TimeBorPlot

draws plot correlating the time to Best Overall Response (BO) leveraging the BO classification

The useful arguments for MMRFRG_TimeBorPlot are:

Arguments | Description -----|----- ttime|is the time expressed as cycles or days treat.resp|is a data.frame of clinical information downloaded from MMRF-Commpass Researcher Gateway dpi|is the figure dpi filename|is the name of png file width|is the image width height|is the image height

Example:

MMRFRG_TimeBorPlot(clinMMGateway,"Dexamethasone","days")

knitr::include_graphics('img/TimeBestOverall_responsePlot_1.png')

MMRFRG_TreatBorDurationPlot

draws plot of Treatment duration (cycle/days) for the Best Overall Response filtered by Therapy classification.

Arguments | Description -----|----- therapyname|is a string containing the therapy for filtering data ttime|is the time expressed as cycles or days treat.resp|is a data.frame of clinical information downloaded from MMRF-Commpass Researcher Gateway line|is a string containing the line of therapy bor| is a string containing the type of BO Response (e.g.'Partial Response','Complete Response', etc. ) dpi|is the figure dpi filename|is the name of png file width|is the image width height|is the image height

Example:

MMRFRG_TreatBorDurationPlot(clinMMGateway,"Bortezomib",ttime="cycles",bor="PR",height=10, width=10)

knitr::include_graphics('img/Trt_DurationPlot_1.png')

MMRFRG_VariantCountPlot

draw plot of annoteted variants by Best Overall Response and Therapy class

Arguments | Description -----|----- variant.ann|is the dataframe of annotated variants downloaded from MMRF-Commpass Researcher Gateway trt|is the dataframe containing information about treatment-response downloaded from MMRF-Commpass Researcher Gateway treat.resp|is a data.frame of clinical information downloaded from MMRF-Commpass Researcher Gateway filenm|is the name of the png file. If filenm is Null, the plot is draw but it is not saved. width| Image width height|Image height topN|is the top number of variant count

Example:

Variant cout grouped by Best Overall Response and Treatment: (top variant count=80)

summary.var<-MMRFRG_VariantCountPlot(variant.ann,trt,topN=80,filenm=NULL)

knitr::include_graphics('img/MMRFvariantCount.png')

MMRFRG_GetIDSamplebyVariant

Filter patient information by dbSNP variant

Arguments | Description -----|----- variant|is the vector of dbSNP ID variant.ann|is the data.frame of annotated variants downloaded from MMRF-Commpass Researcher Gateway treat.resp|is a data.frame of clinical information downloaded from MMRF-Commpass Researcher Gateway patient|is the dataframe of the patient clinical data downloaded from MMRF-Commpass Researcher Gateway

Example:

Get patients having variants in "variant":

variant <- c("rs2157615","rs61731685","rs372409204","rs111362472")

patient.var<-MMRFRG_GetIDSamplebyVariant(variant.ann,patient,variant)

MMRFRG_SurvivalKM

Perform Survival analysis focused on patients having the before selected variants

Arguments | Description -----|----- patient|is the data.frame of the patient clinical data downloaded from MMRF-Commpass Researcher Gateway trt|is the data.frame of the patient clinical data (i.e. treatment-response) downloaded from MMRF-Commpass Researcher Gateway FilterBy| Column with groups to plot. This is a mandatory field. risk.table| show or not the risk table legend| Legend title of the figure xlim| x axis limits e.g. xlim = c(0, 1000). Present narrower X axis, but not affect survival estimates. main| main title of the plot labels| labels of the plot ylab| y axis text of the plot xlab| x axis text of the plot filename| The name of the pdf file. color| Define the colors/Pallete for lines. width| Image width height| Image height pvalue| show p-value of log-rank test conf.range| show confidence intervals for point estimates of survival curves. dpi| Figure quality

Example:

 MMRFRG_SurvivalKM(patient.var,
                   trt,
                   FilterBy="treatment", 
                   filename="SurvivalKM",
                   conf.range = FALSE,
                   risk.table=FALSE,
                   color = c("Dark2"))

knitr::include_graphics('img/MMRFSurvival.png')

marziasettino/MMRFBiolinks documentation built on Jan. 24, 2023, 4:49 a.m.