Adding several simple normalizations.

adam-core/src/main/scala/org/bdgenomics/adam/models/Interval.scala

@@ -0,0 +1,34 @@
+/**
+ * Licensed to Big Data Genomics (BDG) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The BDG licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.bdgenomics.adam.models
+
+/**
+ * An interval is a region on a coordinate space that has a defined width. This
+ * can be used to express a region of a genome, a transcript, a gene, etc.
+ */
+trait Interval {
+
+  /**
+   * A width is the key property of an interval, which can represent a genomic
+   * region, a transcript, a gene, etc.
+   *
+   * @return The width of this interval.
+   */
+  def width: Long
+
+}

adam-core/src/main/scala/org/bdgenomics/adam/models/ReferenceRegion.scala

@@ -161,7 +161,7 @@ object ReferenceRegion {
  *            which is <i>not</i> in the region -- i.e. [start, end) define a 0-based
  *            half-open interval.
  */
-case class ReferenceRegion(referenceName: String, start: Long, end: Long) extends Ordered[ReferenceRegion] {
+case class ReferenceRegion(referenceName: String, start: Long, end: Long) extends Ordered[ReferenceRegion] with Interval {
 
   assert(start >= 0)
   assert(end >= start)

adam-core/src/main/scala/org/bdgenomics/adam/rdd/normalization/LengthNormalization.scala

@@ -0,0 +1,78 @@
+/**
+ * Licensed to Big Data Genomics (BDG) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The BDG licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.bdgenomics.adam.rdd.normalization
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.bdgenomics.adam.models.Interval
+
+object LengthNormalization extends Serializable with Logging {
+
+  /**
+   * Normalizes an RDD that contains a double value and an interval by the width
+   * of the interval.
+   *
+   * @param rdd An RDD containing (a value to be normalized, an interval, and an
+   * additional data value), for normalization.
+   * @return Returns an RDD containing (the double normalized by the interval
+   * length, the original interval, the original data value) after normalization.
+   *
+   * @tparam T Datatype of additional value parameter to maintain.
+   *
+   * @see pkn
+   */
+  def apply[I <: Interval, T](rdd: RDD[((Double, I), T)]): RDD[((Double, I), T)] = {
+    rdd.map(t => ((t._1._1 / t._1._2.width, t._1._2), t._2))
+  }
+
+  /**
+   * Normalizes an RDD that contains a double value and an interval by the width
+   * of the interval and the total aggregate value of all values. This is useful
+   * for calculating entities like reads/fragments per kilobase of transcript
+   * per million reads (RPKM/FPKM).
+   *
+   * @param rdd An RDD containing (a value to be normalized, an interval, and an
+   * additional data value), for normalization.
+   * @param n Global normalization factor. E.g., for RPKM, n = 1,000,000 (reads
+   * per kilobase transcript per _million_ reads).
+   *
+   * @return Returns an RDD containing (the double normalized by the interval
+   * length, the original interval, the original data value) after normalization.
+   *
+   * @tparam T Datatype of additional value parameter to maintain.
+   *
+   * @see apply
+   */
+  def pkn[I <: Interval, T](rdd: RDD[((Double, I), T)],
+                            k: Double = 1000.0,
+                            n: Double = 1000000.0): RDD[((Double, I), T)] = {
+    val cachedRdd = rdd.cache
+
+    // generate count
+    val norm = cachedRdd.map(kv => kv._1._1).reduce(_ + _) / n
+
+    // normalize the RDD
+    val normalizedRdd = apply(cachedRdd).map(t => ((t._1._1 * norm * k, t._1._2), t._2))
+
+    // uncache
+    cachedRdd.unpersist()
+
+    // return
+    normalizedRdd
+  }
+}

adam-core/src/main/scala/org/bdgenomics/adam/rdd/normalization/ZScoreNormalization.scala

@@ -0,0 +1,79 @@
+/**
+ * Licensed to Big Data Genomics (BDG) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The BDG licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.bdgenomics.adam.rdd.normalization
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import scala.math.sqrt
+
+object ZScoreNormalization extends Serializable with Logging {
+
+  /**
+   * Normalizes an RDD of double values by computing the Z score for each value.
+   * Per point, the Z score (also known as standard score) is computed by
+   * subtracting the mean across all values from the point, and then dividing
+   * by the standard deviation across all points.
+   *
+   * @param rdd RDD of (Double, Value) pairs to be normalized.
+   * @returns Returns an RDD where the original double value has been replaced
+   * by the Z score for that point.
+   *
+   * @tparam T Type of data passed along.
+   */
+  def apply[T](rdd: RDD[(Double, T)]): RDD[(Double, T)] = {
+    val cachedRdd = rdd.cache
+
+    // compute mean and standard deviation
+    val n = cachedRdd.count
+    val mu = mean(cachedRdd.map(kv => kv._1), n)
+    val sigma = sqrt(variance(cachedRdd.map(kv => kv._1), n, mu))
+
+    // update keys
+    log.info("Normalizing by z-score with µ: " + mu + " and σ: " + sigma)
+    val update = cachedRdd.map(kv => ((kv._1 - mu) / sigma, kv._2))
+
+    // unpersist rdd
+    cachedRdd.unpersist()
+
+    // return
+    update
+  }
+
+  /**
+   * Computes the mean of a set of samples.
+   *
+   * @param rdd An RDD of doubles.
+   * @param n The number of samples in the RDD.
+   * @return Returns the mean of the RDD of doubles.
+   */
+  private[normalization] def mean(rdd: RDD[Double], n: Long): Double = {
+    rdd.reduce(_ + _) / n.toDouble
+  }
+
+  /**
+   * Computes the variance of a set of samples.
+   *
+   * @param rdd An RDD of doubles.
+   * @param n The number of samples in the RDD.
+   * @param mu The mean of all the samples in the RDD.
+   * @return Returns the mean of the RDD of doubles.
+   */
+  private[normalization] def variance(rdd: RDD[Double], n: Long, mu: Double): Double = {
+    rdd.map(d => (d - mu) * (d - mu)).reduce(_ + _) / n.toDouble
+  }
+}

adam-core/src/test/scala/org/bdgenomics/adam/rdd/normalization/LengthNormalizationSuite.scala

@@ -0,0 +1,65 @@
+/**
+ * Licensed to Big Data Genomics (BDG) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The BDG licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.bdgenomics.adam.rdd.normalization
+
+import org.apache.spark.rdd.RDD
+import org.bdgenomics.adam.models.ReferenceRegion
+import org.bdgenomics.adam.util.SparkFunSuite
+import scala.math.{ abs, sqrt }
+
+class LengthNormalizationSuite extends SparkFunSuite {
+  def fpEquals(n1: Double, n2: Double, epsilon: Double = 1e-6): Boolean = {
+    abs(n1 - n2) < epsilon
+  }
+
+  sparkTest("normalize a single targeted region") {
+    val rdd = sc.parallelize(Seq(((1000.0, ReferenceRegion("chr1", 0L, 1001L)), 1)))
+
+    LengthNormalization(rdd)
+      .map(t => t._1._1)
+      .collect()
+      .foreach(fpEquals(_, 1.0))
+  }
+
+  sparkTest("normalize a set of targeted regions") {
+    val rddVals = sc.parallelize(Seq(1.0, 5.0, 3.0, 4.0, 2.0))
+
+    val rdd = rddVals.zip(sc.parallelize(Seq(1000.0, 500.0, 3215.0, 10000.0, 55000.0)))
+      .map(kv => ((kv._1 * kv._2, ReferenceRegion("", 0L, kv._2.toLong + 1)), 1))
+
+    LengthNormalization(rdd)
+      .map(t => t._1._1)
+      .zip(rddVals)
+      .collect()
+      .foreach(p => fpEquals(p._1, p._2))
+  }
+
+  sparkTest("calculate *pkm type normalization for a set of targeted regions") {
+    val rddVals = sc.parallelize(Seq(1.0, 5.0, 3.0, 4.0, 2.0))
+
+    val rdd = rddVals.map(_ * 100000.0)
+      .zip(sc.parallelize(Seq(1000.0, 500.0, 3215.0, 10000.0, 55000.0)))
+      .map(kv => ((kv._1 * kv._2, ReferenceRegion("", 0L, kv._2.toLong + 1)), 1))
+
+    LengthNormalization.pkn(rdd)
+      .map(t => t._1._1)
+      .zip(rddVals)
+      .collect()
+      .foreach(p => fpEquals(p._1, p._2))
+  }
+}

adam-core/src/test/scala/org/bdgenomics/adam/rdd/normalization/ZScoreNormalizationSuite.scala

@@ -0,0 +1,69 @@
+/**
+ * Licensed to Big Data Genomics (BDG) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The BDG licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.bdgenomics.adam.rdd.normalization
+
+import org.apache.spark.rdd.RDD
+import org.bdgenomics.adam.util.SparkFunSuite
+import scala.math.{ abs, sqrt }
+
+class ZScoreNormalizationSuite extends SparkFunSuite {
+  def fpEquals(n1: Double, n2: Double, epsilon: Double = 1e-6): Boolean = {
+    abs(n1 - n2) < epsilon
+  }
+
+  sparkTest("compute mean of a set of samples") {
+    val rdd = sc.parallelize(Seq(3.0, 4.0, 5.0, 4.0, 5.0, 3.0, 2.0, 6.0))
+
+    assert(fpEquals(4.0, ZScoreNormalization.mean(rdd, rdd.count)))
+  }
+
+  sparkTest("compute variance of a set of samples") {
+    val rdd = sc.parallelize(Seq(3.0, 4.0, 5.0, 4.0, 5.0, 3.0, 2.0, 6.0))
+
+    val expected = (4.0 * 1.0 + 2.0 * 4.0) / 8.0
+
+    assert(fpEquals(expected, ZScoreNormalization.variance(rdd, rdd.count, 4.0)))
+  }
+
+  sparkTest("variance should be 0 if all elements are the same") {
+    val rdd = sc.parallelize(Seq(3.0, 3.0, 3.0, 3.0, 3.0))
+
+    assert(fpEquals(0.0, ZScoreNormalization.variance(rdd, rdd.count,
+      ZScoreNormalization.mean(rdd, rdd.count))))
+  }
+
+  sparkTest("check z-score for a varying rdd") {
+    // this rdd contains a set of values whose square roots are equal to their z-score
+    // for this rdd, µ = 0.0, σ = 2.0
+    val rdd = sc.parallelize(Seq(-2.0, 0.0, 0.0, 2.0))
+
+    val r = ZScoreNormalization(rdd.map(v => (v, 1)))
+      .map(kv => kv._1)
+      .zip(rdd)
+      .collect()
+
+    r.foreach(p => {
+      val p2 = if (p._2 != 0.0) {
+        sqrt(abs(p._2)) * p._2 / abs(p._2)
+      } else {
+        0.0 // need this, else we try to div by 0
+      }
+      assert(fpEquals(p._1, p2))
+    })
+  }
+}