diff --git a/pom.xml b/pom.xml
index 5e3ce0b..0e8ee4b 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1,16 +1,16 @@
 <project xmlns="http://maven.apache.org/POM/4.0.0"
          xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
          xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
-                      http://maven.apache.org/xsd/maven-4.0.0.xsd">
+                      http://maven.apache.org/maven-v4_0_0.xsd">
     <modelVersion>4.0.0</modelVersion>
     <groupId>com.neilferguson</groupId>
     <artifactId>popstrat</artifactId>
     <version>0.1-SNAPSHOT</version>
     <properties>
-        <spark.version>1.2.0</spark.version>
-        <h2o.version>3.0.0.8</h2o.version>
-        <sparklingwater.version>1.2.5</sparklingwater.version>
-        <adam.version>0.16.0</adam.version>
+        <spark.version>1.6.1</spark.version>
+        <h2o.version>3.8.2.6</h2o.version>
+        <sparklingwater.version>1.6.5</sparklingwater.version>
+        <adam.version>0.19.0</adam.version>
     </properties>
     <pluginRepositories>
         <pluginRepository>
@@ -27,12 +27,12 @@
         </dependency>
         <dependency>
             <groupId>org.bdgenomics.adam</groupId>
-            <artifactId>adam-core</artifactId>
+            <artifactId>adam-core_2.10</artifactId>
             <version>${adam.version}</version>
         </dependency>
         <dependency>
             <groupId>org.bdgenomics.adam</groupId>
-            <artifactId>adam-apis</artifactId>
+            <artifactId>adam-apis_2.10</artifactId>
             <version>${adam.version}</version>
         </dependency>
         <dependency>
@@ -87,7 +87,7 @@
             <plugin>
                 <groupId>org.scala-tools</groupId>
                 <artifactId>maven-scala-plugin</artifactId>
-                <version>2.14.1</version>
+                <version>2.15.2</version>
                 <executions>
                     <execution>
                         <goals>
diff --git a/src/main/scala/com/neilferguson/PopStrat.scala b/src/main/scala/com/neilferguson/PopStrat.scala
index 286fb39..9fb9eed 100644
--- a/src/main/scala/com/neilferguson/PopStrat.scala
+++ b/src/main/scala/com/neilferguson/PopStrat.scala
@@ -17,6 +17,14 @@ import scala.collection.JavaConverters._
 import scala.collection.immutable.Range.inclusive
 import scala.io.Source
 
+import org.apache.spark.sql.types.DataTypes
+import hex._
+import water.fvec._
+import water.support._
+import _root_.hex.Distribution.Family
+import _root_.hex.deeplearning.DeepLearningModel
+import _root_.hex.tree.gbm.GBMModel
+import _root_.hex.{Model, ModelMetricsBinomial}
 object PopStrat {
 
   def main(args: Array[String]): Unit = {
@@ -96,8 +104,8 @@ object PopStrat {
       case (sampleId, variants) =>
         (sampleId, variants.toArray.sortBy(_.variantId))
     }
-    val header = StructType(Array(StructField("Region", StringType)) ++
-      sortedVariantsBySampleId.first()._2.map(variant => {StructField(variant.variantId.toString, IntegerType)}))
+    val header = DataTypes.createStructType(Array(DataTypes.createStructField("Region", DataTypes.StringType,false)) ++
+      sortedVariantsBySampleId.first()._2.map(variant => {DataTypes.createStructField(variant.variantId.toString,DataTypes.IntegerType,false)}))
     val rowRDD: RDD[Row] = sortedVariantsBySampleId.map {
       case (sampleId, sortedVariants) =>
         val region: Array[String] = Array(panel.getOrElse(sampleId, "Unknown"))
@@ -107,13 +115,15 @@ object PopStrat {
 
     // Create the SchemaRDD from the header and rows and convert the SchemaRDD into a H2O dataframe
     val sqlContext = new org.apache.spark.sql.SQLContext(sc)
+    //val dataFrame=sqlContext.createDataFrame(rowRDD, header)
     val schemaRDD = sqlContext.applySchema(rowRDD, header)
     val h2oContext = new H2OContext(sc).start()
-    import h2oContext._
-    val dataFrame = h2oContext.toDataFrame(schemaRDD)
+    import h2oContext._ 
+    val dataFrame1 =h2oContext.asH2OFrame(schemaRDD)
+    val dataFrame=H2OFrameSupport.allStringVecToCategorical(dataFrame1)
 
     // Split the dataframe into 50% training, 30% test, and 20% validation data
-    val frameSplitter = new FrameSplitter(dataFrame, Array(.5, .3), Array("training", "test", "validation").map(Key.make), null)
+    val frameSplitter = new FrameSplitter(dataFrame, Array(.5, .3), Array("training", "test", "validation").map(Key.make[Frame](_)), null)
     water.H2O.submitTask(frameSplitter)
     val splits = frameSplitter.getResult
     val training = splits(0)
@@ -121,8 +131,8 @@ object PopStrat {
 
     // Set the parameters for our deep learning model.
     val deepLearningParameters = new DeepLearningParameters()
-    deepLearningParameters._train = training
-    deepLearningParameters._valid = validation
+    deepLearningParameters._train = training._key
+    deepLearningParameters._valid = validation._key
     deepLearningParameters._response_column = "Region"
     deepLearningParameters._epochs = 10
     deepLearningParameters._activation = Activation.RectifierWithDropout
@@ -134,7 +144,7 @@ object PopStrat {
 
     // Score the model against the entire dataset (training, test, and validation data)
     // This causes the confusion matrix to be printed
-    deepLearningModel.score(dataFrame)('predict)
+    deepLearningModel.score(dataFrame)
 
   }