diff --git a/DESCRIPTION b/DESCRIPTION
index e1c353a..0a54c7b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: imsig
 Type: Package
 Title: Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumours
-Version: 1.1.0
+Version: 1.1.1
 Author: Ajit Johnson Nirmal
 Maintainer: Ajit Johnson Nirmal <ajitjohnson.n@gmail.com>
 Description: Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data. 
@@ -17,5 +17,5 @@ Imports:
   ggplot2 (>= 2.2),
   gridExtra (>= 2.3),
   survival (>= 2.4)
-RoxygenNote: 6.0.1
+RoxygenNote: 7.1.1
 Suggests: testthat
diff --git a/R/imsig.R b/R/imsig.R
index 58d1e38..c396285 100644
--- a/R/imsig.R
+++ b/R/imsig.R
@@ -6,7 +6,7 @@
 #' @param sort_by Can be used to sort the samples by predicted abundance of a particular cell type. All other cell types follow this sorting. By default it is sorted by `T cells`
 #' @return Relative abundance of immune cells across samples. Returns a dataframe.
 #' @examples
-#' cell_abundance = imsig (exp = example_data, r = 0.7)
+#' cell_abundance = imsig (exp = example_data, r = 0.7, sort=TRUE, sort_by='T cells')
 #' head(cell_abundance)
 #' @seealso \code{\link{feature_select}}, \code{\link{example_data}}
 #' @export
diff --git a/R/imsig_survival.R b/R/imsig_survival.R
index f204182..b7d6379 100644
--- a/R/imsig_survival.R
+++ b/R/imsig_survival.R
@@ -7,7 +7,7 @@
 #' @param r Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.
 #' @return Hazard Ratio
 #' @examples \donttest{
-#' survival = imsig_survival (exp = example_data)
+#' survival = imsig_survival (exp = example_data, cli = example_cli)
 #' head(survival)
 #' }
 #' @import survival
@@ -25,7 +25,7 @@ imsig_survival <- function(exp, cli, time = 'time', status= 'status', r = 0.6){
   for (i in 1: length(levels(sig$cell))){
     cell_ordered <- cell_cli[sort.list(cell_cli[,i]),]
     cell_ordered$group <- ifelse(cell_ordered[,i] <= median(cell_cli[,i]), "low", "high")
-    cox <- coxph(Surv(time, status) ~ group, cell_ordered)
+    cox <- survival::coxph(Surv(time, status) ~ group, cell_ordered)
     x <- summary(cox)[8]
     HR.OS <- data.frame(log2(x[[1]][1]))
     HR.UP <- data.frame(log2(x[[1]][4]))
diff --git a/man/corr_matrix.Rd b/man/corr_matrix.Rd
index e3e2245..126e9a5 100644
--- a/man/corr_matrix.Rd
+++ b/man/corr_matrix.Rd
@@ -9,7 +9,7 @@ corr_matrix(exp, r)
 \arguments{
 \item{exp}{Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): \code{\link{example_data}}.}
 
-\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}} and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
+\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
 }
 \value{
 Gene-gene correlation matrix of ImSig genes.
diff --git a/man/example_cli.Rd b/man/example_cli.Rd
index f3eb517..f2fc3df 100644
--- a/man/example_cli.Rd
+++ b/man/example_cli.Rd
@@ -4,7 +4,9 @@
 \name{example_cli}
 \alias{example_cli}
 \title{Example clinical data file for survival analysis with ImSig}
-\format{dataframe}
+\format{
+dataframe
+}
 \usage{
 example_cli
 }
diff --git a/man/example_data.Rd b/man/example_data.Rd
index 8cc702a..1447a7b 100644
--- a/man/example_data.Rd
+++ b/man/example_data.Rd
@@ -4,7 +4,9 @@
 \name{example_data}
 \alias{example_data}
 \title{Example transcriptomics data}
-\format{dataframe}
+\format{
+dataframe
+}
 \usage{
 example_data
 }
diff --git a/man/feature_select.Rd b/man/feature_select.Rd
index 1ff6365..e06d9f4 100644
--- a/man/feature_select.Rd
+++ b/man/feature_select.Rd
@@ -9,7 +9,7 @@ feature_select(exp, r = 0.6)
 \arguments{
 \item{exp}{Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): \code{\link{example_data}}.}
 
-\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}} and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
+\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
 }
 \value{
 Returns a list of 'feature selected' genes based on the set r value.
diff --git a/man/gene_stat.Rd b/man/gene_stat.Rd
index f8fafed..5a2518c 100644
--- a/man/gene_stat.Rd
+++ b/man/gene_stat.Rd
@@ -9,7 +9,7 @@ gene_stat(exp, r = 0.6)
 \arguments{
 \item{exp}{Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): \code{\link{example_data}}.}
 
-\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}} and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
+\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
 }
 \value{
 Dataframe of general statistics of ImSig analysis.
diff --git a/man/imsig.Rd b/man/imsig.Rd
index 6e398de..2cca08a 100644
--- a/man/imsig.Rd
+++ b/man/imsig.Rd
@@ -4,12 +4,16 @@
 \alias{imsig}
 \title{Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data}
 \usage{
-imsig(exp, r = 0.6)
+imsig(exp, r = 0.6, sort = TRUE, sort_by = "T cells")
 }
 \arguments{
 \item{exp}{Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): \code{\link{example_data}}.}
 
-\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}} and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
+\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
+
+\item{sort}{Sort the samples based on abundance of a particular cell type. `Set sort = FALSE` if you wish not to apply sorting. By default the function sorts by abundance of T cells. The cell type of interest for sorting can be controlled by the `sort_by` parameter.}
+
+\item{sort_by}{Can be used to sort the samples by predicted abundance of a particular cell type. All other cell types follow this sorting. By default it is sorted by `T cells`}
 }
 \value{
 Relative abundance of immune cells across samples. Returns a dataframe.
@@ -18,7 +22,7 @@ Relative abundance of immune cells across samples. Returns a dataframe.
 Estimates the relative abundance of immune cells across patients/samples.
 }
 \examples{
-cell_abundance = imsig (exp = example_data, r = 0.7)
+cell_abundance = imsig (exp = example_data, r = 0.7, sort=TRUE, sort_by='T cells')
 head(cell_abundance)
 }
 \seealso{
diff --git a/man/imsig_survival.Rd b/man/imsig_survival.Rd
index 9b54ede..a950f97 100644
--- a/man/imsig_survival.Rd
+++ b/man/imsig_survival.Rd
@@ -15,7 +15,7 @@ imsig_survival(exp, cli, time = "time", status = "status", r = 0.6)
 
 \item{status}{Column name of event (dead or alive) parameter.}
 
-\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}} and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
+\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
 }
 \value{
 Hazard Ratio
@@ -25,7 +25,7 @@ Patients are split into two groups based on their immune cell abundance (median
 }
 \examples{
 \donttest{
-survival = imsig_survival (exp = example_data)
+survival = imsig_survival (exp = example_data, cli = example_cli)
 head(survival)
 }
 }
diff --git a/man/plot_abundance.Rd b/man/plot_abundance.Rd
index 172f995..a316b8f 100644
--- a/man/plot_abundance.Rd
+++ b/man/plot_abundance.Rd
@@ -9,7 +9,7 @@ plot_abundance(exp, r = 0.6)
 \arguments{
 \item{exp}{Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): \code{\link{example_data}}.}
 
-\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}} and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
+\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
 }
 \value{
 ggplot
diff --git a/man/plot_network.Rd b/man/plot_network.Rd
index 4036565..3e7600a 100644
--- a/man/plot_network.Rd
+++ b/man/plot_network.Rd
@@ -4,14 +4,22 @@
 \alias{plot_network}
 \title{Network graph of ImSig genes}
 \usage{
-plot_network(exp, r = 0.6, pt.cex = 2, cex = 1, inset = 0,
-  x.intersp = 2, vertex.size = 3, vertex.label = NA,
-  layout = layout_with_fr)
+plot_network(
+  exp,
+  r = 0.6,
+  pt.cex = 2,
+  cex = 1,
+  inset = 0,
+  x.intersp = 2,
+  vertex.size = 3,
+  vertex.label = NA,
+  layout = layout_with_fr
+)
 }
 \arguments{
 \item{exp}{Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): \code{\link{example_data}}.}
 
-\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}} and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
+\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
 
 \item{pt.cex}{expansion factor(s) for the points.}
 
diff --git a/man/plot_survival.Rd b/man/plot_survival.Rd
index a7a18b4..b4f1a06 100644
--- a/man/plot_survival.Rd
+++ b/man/plot_survival.Rd
@@ -15,7 +15,7 @@ plot_survival(exp, cli, time = "time", status = "status", r = 0.6)
 
 \item{status}{Column name of event (dead or alive) parameter.}
 
-\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}} and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
+\item{r}{Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.}
 }
 \value{
 Forest plot
diff --git a/man/sig.Rd b/man/sig.Rd
index 4fcdb55..ad46d17 100644
--- a/man/sig.Rd
+++ b/man/sig.Rd
@@ -4,7 +4,9 @@
 \name{sig}
 \alias{sig}
 \title{ImSig genes}
-\format{dataframe}
+\format{
+dataframe
+}
 \usage{
 sig
 }
diff --git a/tests/testthat/test_imsig.R b/tests/testthat/test_imsig.R
index 1a5557c..f6cb6a5 100644
--- a/tests/testthat/test_imsig.R
+++ b/tests/testthat/test_imsig.R
@@ -1,2 +1,2 @@
-cell_abundance = imsig (exp = example_data, r = 0.7)
+cell_abundance = imsig (exp = example_data, r = 0.7, sort=TRUE, sort_by='T cells')
 head(cell_abundance)