diff --git a/DESCRIPTION b/DESCRIPTION
index d00d34c..4ba80a1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -27,7 +27,8 @@ Imports:
     RColorBrewer,
     Rhtslib,
     survival,
-    DNAcopy
+    DNAcopy,
+    pheatmap
 Suggests: 
     berryFunctions,
     Biostrings,
@@ -43,7 +44,6 @@ Suggests:
     RaggedExperiment,
     rmarkdown,
     S4Vectors,
-    pheatmap,
     curl
 LinkingTo: 
     Rhtslib,
diff --git a/NAMESPACE b/NAMESPACE
index 6824b16..7f04222 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -62,6 +62,7 @@ export(rainfallPlot)
 export(read.maf)
 export(readGistic)
 export(sampleSwaps)
+export(segSummarize)
 export(segmentLogR)
 export(setdiffMAF)
 export(signatureEnrichment)
diff --git a/R/segSummarize.R b/R/segSummarize.R
new file mode 100644
index 0000000..3a2262c
--- /dev/null
+++ b/R/segSummarize.R
@@ -0,0 +1,177 @@
+#' Summarize CBS segmentation results
+#' @details
+#' A handy function to summarize CBS segmentation results. Takes segmentation results generated by DNAcopy package \code{\link{segment}} and summarizes the CN for each cytoband and chromosomal arms.
+#'
+#' @param seg segmentation results generated from \code{DNAcopy} package \code{\link{segment}}. Input should be a multi-sample segmentation file or a data.frame. First six columns should correspond to sample name, chromosome,  start,  end, Num_Probes, Segment_Mean in log2 scale. (default output format from DNAcopy)
+#' @param build genome build. Default hg19. Can be hg19, hg38. If other than these, use `cytoband` argument
+#' @param cytoband cytoband data from UCSC genome browser. Only needed if `build` is other than `hg19` or `hg38`
+#' @param thr threshold to call amplification and deletion. Any cytobands or chromosomal arms with median logR above or below this will be called. Default 0.3
+#' @param verbose Default TRUE
+#' @param maf optional MAF
+#' @param genes Add mutation status of these genes as an annotation to the heatmap
+#' @param topanno annotation for each sample. This is passed as an input to `annotation_col` of `pheatmap`
+#' @param topannocols annotation cols for `topanno`. This is passed as an input to `annotation_colors` of `pheatmap`
+#' @return List of median CN values for each cytoband and chromosomal arm along with the plotting matrix
+#' @export
+#' @examples
+#' laml.seg <- system.file("extdata", "LAML_CBS_segments.tsv.gz", package = "maftools")
+#' segSummarize(seg = laml.seg)
+#'
+#' #Heighlight some genes as annotation
+#' laml.maf = system.file("extdata", "tcga_laml.maf.gz", package = "maftools") #MAF file
+#' laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools') #clinical data
+#' laml = read.maf(maf = laml.maf, clinicalData = laml.clin)
+#'
+#' segSummarize(seg = laml.seg, maf = laml, genes = c("FLT3", "DNMT3A"))
+
+segSummarize = function(seg = NULL, build = "hg19", cytoband = NULL, thr = 0.3, verbose = TRUE, maf = NULL, genes = NULL, topanno = NULL, topannocols = NA){
+
+  if(is.null(cytoband)){
+    build = match.arg(arg = build, choices = c("hg19", "hg38"))
+    cytoband <- system.file("extdata", paste0(build, "_cytobands.tsv.gz"), package = "maftools")
+    if(!file.exists(cytoband)){
+      stop("Cytoband file does not exist!")
+    }
+  }
+
+  if (is(object = seg, class2 = "data.frame")) {
+    seg = data.table::as.data.table(seg)
+  }else {
+    seg = data.table::fread(input = seg)
+  }
+  colnames(seg)[1:6] = c("Sample", "Chromosome",  "Start",  "End", "Num_Probes", "Segment_Mean")
+  seg$Chromosome = gsub(pattern = "^chr", replacement = "", x = seg$Chromosome)
+  data.table::setkey(x = seg, Chromosome, Start, End)
+
+  if(nrow(seg[,.N,Sample]) < 3){
+    stop("Not enough samples to proceed (N: ", nrow(seg[,.N,Sample]), ")")
+  }
+
+  cy = data.table::fread(input = cytoband)
+  colnames(cy) = c("Chromosome", "Start", "End", "name", "stain")
+  cy$arm = substr(cy$name, 1, 1)
+  cy$Chromosome = gsub(pattern = "^chr", replacement = "", x = cy$Chromosome)
+  data.table::setkey(x = cy, Chromosome, Start, End)
+
+  seg_events = lapply(split(seg, seg$Sample), function(x){
+    .seg2arm(se = x, cy = cy, thr = thr)
+  })
+
+  cy[, id := paste(Chromosome, name, sep = "_")]
+  cy[, arm := paste(Chromosome, substr(name, 1, 1), sep = "_")]
+
+  #Summarize CN per chromosomal cytoband
+  cytoband_events = lapply(seg_events, function(x) x$seg) |> data.table::rbindlist(idcol = "Tumor_Sample_Barcode")
+  cytoband_events = data.table::dcast(data = cytoband_events, formula = chromosome+arm ~ Tumor_Sample_Barcode, value.var = "cn")
+
+  cytoband_events[, id := paste(chromosome, arm, sep = "_")]
+  cytoband_events$chromosome = NULL
+  cytoband_events$arm = NULL
+  data.table::setDF(x = cytoband_events, cytoband_events$id)
+  cytoband_events$id = NULL
+  cytoband_events = cytoband_events[cy[id %in% rownames(cytoband_events), id],,]
+  cytoband_events[cytoband_events > 4] = 4
+  cytoband_events = cytoband_events[complete.cases(cytoband_events),]
+
+  ##row anno
+  cn_band_anno = as.data.frame(cy[id %in% rownames(cytoband_events)])
+  cn_band_anno = data.frame(row.names = cn_band_anno$id, chr = cn_band_anno$Chromosome, arm = substr(x = cn_band_anno$name, 1, 1))
+
+  #order by chromosome for human build
+  chr_ord = intersect(c(1:22, "X", "Y"), names(split(cn_band_anno, cn_band_anno$chr)))
+  cn_band_anno = cn_band_anno[unlist(lapply(split(cn_band_anno, cn_band_anno$chr)[chr_ord], rownames), use.names = FALSE), , drop = FALSE]
+  cytoband_events = cytoband_events[rownames(cn_band_anno),, drop = FALSE]
+
+  ##Heatmap colors
+  cols_chromosome = setNames(object = rep(c("#95a5a6", "#7f8c8d"), length(unique(cn_band_anno$chr))), nm = unique(cn_band_anno$chr))
+  cols_chromosome = cols_chromosome[unique(cn_band_anno$chr)]
+  cols_chromosome_arms = setNames(object = c("#ecf0f1", "#bdc3c7"), nm = c("p", "q"))
+
+  gene2barcode = gene2barcode_cols = NA
+  show_annotation_legend = FALSE
+
+  if(!is.null(maf)){
+    if(!is.null(genes)){
+      gene2barcode = genesToBarcodes(maf = maf, genes = genes, justNames = TRUE)
+      print(gene2barcode)
+      gene2barcode = lapply(gene2barcode, function(x) data.table::data.table(Tumor_Sample_Barcode = x, status = 'yes')) |> data.table::rbindlist(idcol = "Hugo_Symbol") |> data.table::dcast(formula = Tumor_Sample_Barcode ~ Hugo_Symbol, value.var = "status")
+      data.table::setDF(x = gene2barcode, rownames = gene2barcode$Tumor_Sample_Barcode)
+      gene2barcode = gene2barcode[, 2:ncol(gene2barcode), drop = FALSE]
+      gene2barcode_cols = lapply(X = colnames(gene2barcode), function(x) setNames(object = "#34495e", nm = "yes"))
+      names(gene2barcode_cols) = colnames(gene2barcode)
+    }
+  }
+
+  if(!is.null(topanno)){
+    if(!is.null(nrow(gene2barcode))){
+      gene2barcode = merge(gene2barcode, topanno, by = "row.names", all = TRUE)
+      rownames(gene2barcode) = gene2barcode$`Row.names`
+      gene2barcode$`Row.names` = NULL
+    }else{
+      gene2barcode = topanno
+    }
+    show_annotation_legend = TRUE
+  }
+
+  pheatmap::pheatmap(
+    cytoband_events,
+    cluster_rows = FALSE,
+    cluster_cols = TRUE,
+    show_colnames = FALSE,
+    annotation_row = cn_band_anno,
+    color = grDevices::colorRampPalette(rev(brewer.pal(
+      n = 7, name = "PiYG"
+    )))(100),
+    annotation_colors = c(list(
+      chr = cols_chromosome,
+      arm = cols_chromosome_arms
+    ), gene2barcode_cols, topannocols),
+    show_rownames = FALSE,
+    border_color = 'black', annotation_legend = show_annotation_legend, legend_breaks = seq(0, 4, 1), legend_labels = c("0", "1", "2", "3", ">4"), annotation_col = gene2barcode, na_col = "gray"
+  )
+
+  #Arm events
+  arm_events = lapply(seg_events, function(x) x$arm) |> data.table::rbindlist(fill = TRUE, use.names = TRUE, idcol = "Tumor_Sample_Barcode")
+  arm_events = arm_events[!is.na(Variant_Classification)][order(chromosome)]
+  arm_events_n = arm_events[,.N,.(arm,Variant_Classification)][!is.na(Variant_Classification)][order(-N)]
+
+  if(verbose){
+    message("Recurrent chromosomal arm aberrations")
+    print(arm_events_n)
+  }
+
+  #Focal band events
+  band_events = lapply(seg_events, function(x) x$seg) |> data.table::rbindlist(fill = TRUE, use.names = TRUE, idcol = "Tumor_Sample_Barcode")
+  band_events = band_events[!is.na(Variant_Classification)][order(chromosome)]
+  colnames(band_events)[3] = "cytoband"
+  band_events_n = band_events[,.N,.(chromosome, cytoband,Variant_Classification)][!is.na(Variant_Classification)][order(-N)]
+
+  attr(cytoband_events, "meta") = list(top_anno = gene2barcode, top_anno_cols = gene2barcode_cols)
+
+  list(heatmap_matrix = cytoband_events, arm_events = arm_events, band_events = band_events)
+}
+
+.seg2arm = function (se, cy = NULL, thr = 0.3) {
+
+  data.table::setkey(x = se, Chromosome, Start, End)
+  se_olaps = data.table::foverlaps(x = cy, y = se)
+  cytoband_mean = se_olaps[, median(Segment_Mean, na.rm = TRUE),
+                           .(Chromosome, name)]
+  colnames(cytoband_mean) = c("chromosome", "arm", "logR")
+  cytoband_mean[, `:=`(cn, 2 * (2^logR))]
+  cytoband_mean$Variant_Classification = ifelse(cytoband_mean$logR >
+                                                  thr, "Amp", no = ifelse(cytoband_mean$logR < -thr, yes = "Del",
+                                                                          no = NA))
+  cytoband_mean = cytoband_mean[!chromosome %in% c("chrX",
+                                                   "chrY")]
+  arm_mean = se_olaps[, median(Segment_Mean, na.rm = TRUE),
+                      .(Chromosome, arm)]
+  colnames(arm_mean) = c("chromosome", "arm", "logR")
+  arm_mean[, `:=`(cn, 2 * (2^logR))]
+  arm_mean$Variant_Classification = ifelse(arm_mean$logR >
+                                             thr, "Gain", no = ifelse(arm_mean$logR < -thr, yes = "Loss",
+                                                                     no = NA))
+  arm_mean$arm = paste(arm_mean$chromosome, arm_mean$arm, sep = "_")
+  arm_mean = arm_mean[!chromosome %in% c("chrX", "chrY")]
+  list(arm = arm_mean, seg = cytoband_mean)
+}
diff --git a/inst/extdata/LAML_CBS_segments.tsv.gz b/inst/extdata/LAML_CBS_segments.tsv.gz
new file mode 100644
index 0000000..c206ed4
Binary files /dev/null and b/inst/extdata/LAML_CBS_segments.tsv.gz differ
diff --git a/inst/extdata/hg19_cytobands.tsv.gz b/inst/extdata/hg19_cytobands.tsv.gz
new file mode 100644
index 0000000..d431db0
Binary files /dev/null and b/inst/extdata/hg19_cytobands.tsv.gz differ
diff --git a/inst/extdata/hg38_cytobands.tsv.gz b/inst/extdata/hg38_cytobands.tsv.gz
new file mode 100644
index 0000000..1a6ab25
Binary files /dev/null and b/inst/extdata/hg38_cytobands.tsv.gz differ
diff --git a/man/segSummarize.Rd b/man/segSummarize.Rd
new file mode 100644
index 0000000..7a93c52
--- /dev/null
+++ b/man/segSummarize.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/segSummarize.R
+\name{segSummarize}
+\alias{segSummarize}
+\title{Summarize CBS segmentation results}
+\usage{
+segSummarize(
+  seg = NULL,
+  build = "hg19",
+  cytoband = NULL,
+  thr = 0.3,
+  verbose = TRUE,
+  maf = NULL,
+  genes = NULL,
+  topanno = NULL,
+  topannocols = NA
+)
+}
+\arguments{
+\item{seg}{segmentation results generated from \code{DNAcopy} package \code{\link{segment}}. Input should be a multi-sample segmentation file or a data.frame. First six columns should correspond to sample name, chromosome,  start,  end, Num_Probes, Segment_Mean in log2 scale. (default output format from DNAcopy)}
+
+\item{build}{genome build. Default hg19. Can be hg19, hg38. If other than these, use `cytoband` argument}
+
+\item{cytoband}{cytoband data from UCSC genome browser. Only needed if `build` is other than `hg19` or `hg38`}
+
+\item{thr}{threshold to call amplification and deletion. Any cytobands or chromosomal arms with median logR above or below this will be called. Default 0.3}
+
+\item{verbose}{Default TRUE}
+
+\item{maf}{optional MAF}
+
+\item{genes}{Add mutation status of these genes as an annotation to the heatmap}
+
+\item{topanno}{annotation for each sample. This is passed as an input to `annotation_col` of `pheatmap`}
+
+\item{topannocols}{annotation cols for `topanno`. This is passed as an input to `annotation_colors` of `pheatmap`}
+}
+\value{
+List of median CN values for each cytoband and chromosomal arm along with the plotting matrix
+}
+\description{
+Summarize CBS segmentation results
+}
+\details{
+A handy function to summarize CBS segmentation results. Takes segmentation results generated by DNAcopy package \code{\link{segment}} and summarizes the CN for each cytoband and chromosomal arms.
+}
+\examples{
+laml.seg <- system.file("extdata", "LAML_CBS_segments.tsv.gz", package = "maftools")
+segSummarize(seg = laml.seg)
+
+#Heighlight some genes as annotation
+laml.maf = system.file("extdata", "tcga_laml.maf.gz", package = "maftools") #MAF file
+laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools') #clinical data
+laml = read.maf(maf = laml.maf, clinicalData = laml.clin)
+
+segSummarize(seg = laml.seg, maf = laml, genes = c("FLT3", "DNMT3A"))
+}
diff --git a/vignettes/maftools.Rmd b/vignettes/maftools.Rmd
index b59e48f..4f284f2 100644
--- a/vignettes/maftools.Rmd
+++ b/vignettes/maftools.Rmd
@@ -29,7 +29,7 @@ If you find this tool useful, please cite:
 
 ------------------------------------------------------------------------
 
-***Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. 2018. Maftools: efficient and comprehensive analysis of somatic variants in cancer. [Genome Resarch. PMID: 30341162](https://doi.org/10.1101/gr.239244.118)***
+***Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. 2018. Maftools: efficient and comprehensive analysis of somatic variants in cancer. [Genome Research. PMID: 30341162](https://doi.org/10.1101/gr.239244.118)***
 
 ------------------------------------------------------------------------
 
@@ -279,6 +279,19 @@ This is similar to oncoplots except for copy number data. One can again sort the
 gisticOncoPlot(gistic = laml.gistic, clinicalData = getClinicalData(x = laml), clinicalFeatures = 'FAB_classification', sortByAnnotation = TRUE, top = 10)
 ```
 
+## CBS segments
+
+### Summarizing chromosomal arm level CN
+
+A multi-sample CBS segments file can be summarized to get the CN status of each cytoband and chromosomal arms. Below plot shows 5q deletions in a subset of AML samples.
+The function returns the plot data and the CN status of each chromosomal cytoband and arms for every sample. 
+
+```{r segSummarize}
+laml.seg <- system.file("extdata", "LAML_CBS_segments.tsv.gz", package = "maftools")
+segSummarize_results = segSummarize(seg = laml.seg)
+```
+
+
 ### Visualizing CBS segments
 
 ```{r plotCBSsegments, fig.height=2.5,fig.width=4,fig.align='left'}