@@ -55,7 +55,7 @@ install_github("RGLab/openCyto", ref="trunk")

 ### Installing from [BioConductor](https://www.bioconductor.org).

   - [Current BioConductor

-    Relase](https://doi.org/doi:10.18129/B9.bioc.CytoML)

+    Release](https://doi.org/doi:10.18129/B9.bioc.CytoML)

 <!-- end list -->

@@ -83,7 +83,7 @@ BiocManager::install("CytoML", version = "devel")

 <!-- end list -->

 ``` r

-install.packges("devtools")

+install.packages("devtools")

 devtools::install_github("RGLab/CytoML", ref = "trunk")

 ```

@@ -92,7 +92,7 @@ devtools::install_github("RGLab/CytoML", ref = "trunk")

 <!-- end list -->

 ``` r

-install.packges("devtools")

+install.packages("devtools")

 devtools::install_github("RGLab/CytoML@*release")

 ```

@@ -209,7 +209,7 @@ plotGate(gh)

 Because CytoML and flowWorkspace reproduce the entire analysis in a

 workspace in R, we have access to information about which cells are part

-of which cell popualtions.

+of which cell populations.

 flowWorkspace has convenience methods to extract the cells from specific

 cell populations:

@@ -324,7 +324,7 @@ GatingSet2flowJo(gs, outFile)

 ## Next Steps

 See the [flowWorskspace](http://www.github.com/RGLab/flowWorkspace) and

-\[openCyto\](<http://www.github.com/RGLab/openCyto>\] packages to learn

+[openCyto](http://www.github.com/RGLab/openCyto] packages to learn

 more about what can be done with `GatingSet` objects.

 ## Code of conduct

@@ -439,9 +439,16 @@ public:

 			caltbl.setY(y);

 			caltbl.setX(x);

 			unsigned nX = x.size();

-			bt->maxValue = caltbl.getX()[nX-1];

-			bt->channelRange = caltbl.getY()[nX-1];

+			if(nX==0)

+			{//assign the default values when caltbl is not present in xml

+				bt->maxValue = 262144;

+				bt->channelRange = 4096;

+			}else

+			{

+				bt->maxValue = caltbl.getX()[nX-1];

+				bt->channelRange = caltbl.getY()[nX-1];

+			}

 			transformation * curTran = bt;

 			/*

 			 * sometime, the biexp parameters may not be stored properly