Question: Hi guys, I have got a CNV data from TCGA as shown below and my goal to extract the list of the genes by using GISTIC
Could anyone plz told me how can i deal with this data by using GISTIC and how to apply GISTIC.

- TCGA-2A-A8VL-10A-01D-A379-01	1	51598	1500664	226	0.1646
- TCGA-2A-A8VL-10A-01D-A379-01	1	1617778	1653196	12	-0.4115
- TCGA-2A-A8VL-10A-01D-A379-01	1	1653256	15362197 7748	0.0056
- TCGA-2A-A8VL-10A-01D-A379-01	1	15362212	15362449	6	0.7626

https://postimg.cc/image/g4kusd56j/

Answer: Credits to Tiago Silva; https://f1000research.com/articles/5-1542

**Update April 5, 2020:**

To be clear, this pipeline has little to do with GISTIC. Here, the segmented copy number data that is obtained from Broad Institute is processed via an R package, *gaia*, which performs the same function as GISTIC, i.e., given a cohort of samples, identifies recurrent copy number events.

**Update October 14, 2018:**

This thread has become a sort of focal point, so, I wanted to make it absolutely clear the process to follow. If your goal is to obtain **somatic copy number alterations (sCNA)** for a group of TCGA patients and/or identify **recurrent sCNA** in these patients, then follow these steps:

- Part I - download segmented sCNA data for any TCGA cohort from Broad Institute's FireBrowse server and identify recurrent sCNA regions in these with *gaia* (R)
- Part II - plot recurrent sCNA gains and losses from GAIA
- **Part III - annotate the recurrent sCNA regions (this post, just below)**
- Part IV -
generate heatmap of recurrent sCNA regions over your cohort

----------------------
---------------

.

#Part III

#A
Create a reference dataset of all genes and save it as a GenomicRanges object

library(biomaRt)
library(GenomicRanges)

#Set up an gene annotation template to use
mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")
mart <- useMart(biomart="ENSEMBL_MART_ENSEMBL", host="grch37.ensembl.org", path="/biomart/martservice", dataset="hsapiens_gene_ensembl")
genes <- getBM(attributes=c("hgnc_symbol","chromosome_name","start_position","end_position"), mart=mart)
genes <- genes[genes[,1]!="" & genes[,2] %in% c(1:22,"X","Y"),]
xidx <- which(genes[,2]=="X")
yidx <- which(genes[,2]=="Y")
genes[xidx, 2] <- 23
genes[yidx, 2] <- 24
genes[,2] <- sapply(genes[,2],as.integer)
genes <- genes[order(genes[,3]),]
genes <- genes[order(genes[,2]),]
colnames(genes) <- c("GeneSymbol","Chr","Start","End")
genes_GR <- makeGRangesFromDataFrame(genes,keep.extra.columns = TRUE)

#B
Store your own data as a GenomicRanges object:

**NB - if following from Part II, the `df` used here is the `RecCNV` object, produced in Part II**

colnames(df) <- c("chr", "start", "end", "extra1", "extra2")
df
chr start end extra1 extra2
1 51598 1500664 226 0.1646
1 1617778 1653196 12 -0.4115
1 1653256 15362197 7748 0.0056

df_GR <- makeGRangesFromDataFrame(df, keep.extra.columns = TRUE)
df_GR
GRanges object with 4 ranges and 3 metadata columns:
seqnames ranges strand |
|
[1] 1 [ 51598, 1500664] * |
[2] 1 [ 1617778, 1653196] * |
[3] 1 [ 1653256, 15362197] * |
extra1 extra2

[1] 226 0.1646
[2] 12 -0.4115
[3] 7748 0.0056
-------

#C
Overlap your regions with the reference dataset that you created

hits <- findOverlaps(genes_GR, df_GR, type="within")
df_ann <- cbind(df[subjectHits(hits),],genes[queryHits(hits),])
head(df_ann)
chr start end extra1 extra2 GeneSymbol Chr
1 51598 1500664 226 0.1646 OR4G4P 1
1 51598 1500664 226 0.1646 OR4G11P 1
1 51598 1500664 226 0.1646 OR4F5 1
Start End
52473 54936
62948 63887
69091 70008

#D
To make filtering easier, we can further manipulate the data to show the precise co-ordinates of each gene and the region co-ordinates in which it was found to have CNA / CNV:

AberrantRegion <- paste0(df_ann[,1],":", df_ann[,3],"-", df_ann[,4])
GeneRegion <- paste0(df_ann[,7],":", df_ann[,8],"-", df_ann[,9])
Final_regions <- cbind(df_ann[,c(6,2,5)], AberrantRegion, GeneRegion)
Final_regions[seq(1, nrow(Final_regions), 20),]
extra2 chr extra1 AberrantRegion
0.1646 1 226 1:51598-1500664
0.1646 1 226 1:51598-1500664
0.1646 1 226 1:51598-1500664
0.1646 1 226 1:51598-1500664

GeneRegion
OR4G4P:1-52473
TUBB8P11:1-808672
FAM132A:1-1177826
TMEM240:1-1470554