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prefilters for eigengenes #91

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Apr 4, 2024
Merged

prefilters for eigengenes #91

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30a8b19
add names to abundancedata objects
d-callan Mar 25, 2024
fbf8d08
switch eigengenes in correlations plugins to use new CollectionWithMe…
d-callan Mar 25, 2024
21b29cd
translate compute to rserve api for diff abund
d-callan Mar 28, 2024
4822bf8
Merge pull request #92 from VEuPathDB/diffAbund-api
d-callan Apr 4, 2024
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5 changes: 4 additions & 1 deletion src/main/java/org/veupathdb/service/eda/compute/plugins/alphadiv/AlphaDivPlugin.java
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Expand Up @@ -3,6 +3,7 @@
import org.gusdb.fgputil.ListBuilder;
import org.jetbrains.annotations.NotNull;
import org.veupathdb.service.eda.common.client.spec.StreamSpec;
import org.veupathdb.service.eda.common.model.CollectionDef;
import org.veupathdb.service.eda.common.model.EntityDef;
import org.veupathdb.service.eda.common.model.ReferenceMetadata;
import org.veupathdb.service.eda.common.model.VariableDef;
Expand Down Expand Up @@ -42,6 +43,8 @@ protected void execute() {
AlphaDivComputeConfig computeConfig = getConfig();
PluginUtil util = getUtil();
ReferenceMetadata meta = getContext().getReferenceMetadata();
CollectionDef collection = meta.getCollection(computeConfig.getCollectionVariable()).orElseThrow();
String collectionMemberType = collection.getMember() == null ? "unknown" : collection.getMember();
String entityId = computeConfig.getCollectionVariable().getEntityId();
EntityDef entity = meta.getEntity(entityId).orElseThrow();
VariableDef computeEntityIdVarSpec = util.getEntityIdVarSpec(entityId);
Expand Down Expand Up @@ -74,7 +77,7 @@ protected void execute() {
}
dotNotatedIdColumnsString = dotNotatedIdColumnsString + ")";

connection.voidEval("abundDT <- microbiomeData::AbundanceData(data=" + INPUT_DATA +
connection.voidEval("abundDT <- microbiomeData::AbundanceData(name=" + util.singleQuote(collectionMemberType) + ",data=" + INPUT_DATA +
",recordIdColumn=" + util.singleQuote(computeEntityIdColName) +
",ancestorIdColumns=as.character(" + dotNotatedIdColumnsString + ")" +
",imputeZero=TRUE)");
Expand Down
5 changes: 4 additions & 1 deletion src/main/java/org/veupathdb/service/eda/compute/plugins/betadiv/BetaDivPlugin.java
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import org.gusdb.fgputil.ListBuilder;
import org.jetbrains.annotations.NotNull;
import org.veupathdb.service.eda.common.client.spec.StreamSpec;
import org.veupathdb.service.eda.common.model.CollectionDef;
import org.veupathdb.service.eda.common.model.EntityDef;
import org.veupathdb.service.eda.common.model.ReferenceMetadata;
import org.veupathdb.service.eda.common.model.VariableDef;
Expand Down Expand Up @@ -43,6 +44,8 @@ protected void execute() {
BetaDivComputeConfig computeConfig = getConfig();
PluginUtil util = getUtil();
ReferenceMetadata meta = getContext().getReferenceMetadata();
CollectionDef collection = meta.getCollection(computeConfig.getCollectionVariable()).orElseThrow();
String collectionMemberType = collection.getMember() == null ? "unknown" : collection.getMember();
String entityId = computeConfig.getCollectionVariable().getEntityId();
EntityDef entity = meta.getEntity(entityId).orElseThrow();
VariableDef computeEntityIdVarSpec = util.getEntityIdVarSpec(entityId);
Expand Down Expand Up @@ -76,7 +79,7 @@ protected void execute() {
}
dotNotatedIdColumnsString = dotNotatedIdColumnsString + ")";

connection.voidEval("abundDT <- microbiomeData::AbundanceData(data=" + INPUT_DATA +
connection.voidEval("abundDT <- microbiomeData::AbundanceData(name=" + singleQuote(collectionMemberType) + ",data=" + INPUT_DATA +
",recordIdColumn=" + singleQuote(computeEntityIdColName) +
",ancestorIdColumns=as.character(" + dotNotatedIdColumnsString + ")" +
",imputeZero=TRUE)");
Expand Down
70 changes: 13 additions & 57 deletions src/main/java/org/veupathdb/service/eda/compute/plugins/correlation/CorrelationPlugin.java
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Expand Up @@ -186,9 +186,9 @@ protected void execute() {
// NOTE: getMember tells us the member type, rather than gives us a literal member
String assayType = collection.getMember() == null ? "unknown" : collection.getMember();

boolean isEigengene = false;
String dataClassRString = "microbiomeData::AbundanceData";
if (assayType.toLowerCase().contains("eigengene")) {
isEigengene = true;
dataClassRString = "veupathUtils::CollectionWithMetadata";
}

// THIS CASE IS ASSAY X METADATA
Expand All @@ -208,43 +208,24 @@ protected void execute() {
connection.voidEval(util.getVoidEvalFreadCommand(INPUT_DATA, metadataInputVars));
connection.voidEval("sampleMetadata <- " + INPUT_DATA);

// Prep data and run correlation
if (isEigengene) {
// If we have eigenegene data, we'll use our base correlation function in veupathUtils, so we
// only need to make data frames for the assay data and sample metadata.
connection.voidEval("eigengeneData <- assayData[order(" + computeEntityIdColName + ")]; " +
"eigengeneData <- eigengeneData[, -as.character(" + dotNotatedEntityIdColumnsString + "), with=FALSE];" +
"eigengeneData <- eigengeneData[, -" + singleQuote(computeEntityIdColName) + ", with=FALSE];");

connection.voidEval("sampleMetadata <- sampleMetadata[order(" + computeEntityIdColName + ")]; " +
"sampleMetadata <- sampleMetadata[, -as.character(" + dotNotatedEntityIdColumnsString + "), with=FALSE];" +
"sampleMetadata <- sampleMetadata[, -" + singleQuote(computeEntityIdColName) + ", with=FALSE];");

connection.voidEval("computeResult <- veupathUtils::correlation(data1=eigengeneData, data2=sampleMetadata" +
", method=" + singleQuote(method) +
", verbose=TRUE)");
} else {
// If we don't have eigengene data, for now we can assume the data is abundance data.
// Abundance data can go through our microbiomeComputations pipeline.
connection.voidEval("sampleMetadata <- microbiomeData::SampleMetadata(data = sampleMetadata" +
connection.voidEval("sampleMetadata <- microbiomeData::SampleMetadata(data = sampleMetadata" +
", recordIdColumn=" + singleQuote(computeEntityIdColName) +
", ancestorIdColumns=as.character(" + dotNotatedEntityIdColumnsString + "))");

connection.voidEval("abundanceData <- microbiomeData::AbundanceData(data=assayData" +
connection.voidEval("abundanceData <- " + dataClassRString + "(name= " + singleQuote(assayType) + ",data=assayData" +
", sampleMetadata=sampleMetadata" +
", recordIdColumn=" + singleQuote(computeEntityIdColName) +
", ancestorIdColumns=as.character(" + dotNotatedEntityIdColumnsString + ")" +
", imputeZero=TRUE)");

// Run correlation!
connection.voidEval("computeResult <- veupathUtils::correlation(data1=abundanceData" +
// Run correlation!
connection.voidEval("computeResult <- veupathUtils::correlation(data1=abundanceData" +
", method=" + singleQuote(method) +
proportionNonZeroThresholdRParam +
varianceThresholdRParam +
stdDevThresholdRParam +
", verbose=TRUE)");
}
// THIS CASE IS ASSAY X ASSAY
// THIS CASE IS ASSAY X ASSAY
} else {
// Get the second assay collection
CollectionSpec assay2 = computeConfig.getCollectionVariable2();
Expand Down Expand Up @@ -282,50 +263,25 @@ protected void execute() {
String dotNotatedEntity2IdColumnsString = util.listToRVector(dotNotatedEntity2IdColumns);

String collection2MemberType = collection2.getMember() == null ? "unknown" : collection2.getMember();
// If either collection is an eigengene, we'll use our base correlation function in veupathUtils,
// so we want to set the isEigengene flag to true.
if (collection2MemberType.toLowerCase().contains("eigengene")) {
isEigengene = true;
}
String data2ClassRString = collection2MemberType.toLowerCase().contains("eigengene") ? "veupathUtils::CollectionWithMetadata" : "microbiomeData::AbundanceData";

// Prep data and run correlation
if (isEigengene) {
// If we have eigenegene data, we'll use our base correlation function in veupathUtils, so we
// only need to make data frames for the assay data and sample metadata.
connection.voidEval("data1 <- assayData; " +
"data1 <- data1[order(" + revisedComputeEntityIdColName + ")]; " +
"data1 <- data1[, -as.character(" + dotNotatedEntityIdColumnsString + "), with=FALSE];" +
"data1 <- data1[, -" + singleQuote(revisedComputeEntityIdColName) + ", with=FALSE]");

connection.voidEval("data2 <- assay2Data; " +
"data2 <- data2[order(" + revisedComputeEntityIdColName + ")]; " +
"data2 <- data2[, -as.character(" + dotNotatedEntity2IdColumnsString + "), with=FALSE];" +
"data2 <- data2[, -" + singleQuote(revisedComputeEntityIdColName) + ", with=FALSE]");

connection.voidEval("computeResult <- veupathUtils::correlation(data1=data1, data2=data2" +
", method=" + singleQuote(method) +
", verbose=TRUE)");
} else {
// If we don't have eigengene data, for now we can assume the data is abundance data.
// Abundance data can go through our microbiomeComputations pipeline.
connection.voidEval("data1 <- microbiomeData::AbundanceData(data=assayData" +
connection.voidEval("data1 <- " + dataClassRString + "(name= " + singleQuote(assayType) + ",data=assayData" +
", recordIdColumn=" + singleQuote(revisedComputeEntityIdColName) +
", ancestorIdColumns=as.character(" + dotNotatedEntityIdColumnsString + ")" +
", imputeZero=TRUE)");

connection.voidEval("data2 <- microbiomeData::AbundanceData(data = assay2Data" +
connection.voidEval("data2 <- " + data2ClassRString + "(name= " + singleQuote(collection2MemberType) + ",data = assay2Data" +
", recordIdColumn=" + singleQuote(revisedComputeEntityIdColName) +
", ancestorIdColumns=as.character(" + dotNotatedEntity2IdColumnsString + ")" +
", imputeZero=TRUE)");

// Run correlation!
connection.voidEval("computeResult <- veupathUtils::correlation(data1=data1, data2=data2" +
// Run correlation!
connection.voidEval("computeResult <- veupathUtils::correlation(data1=data1, data2=data2" +
", method=" + singleQuote(method) +
proportionNonZeroThresholdRParam +
varianceThresholdRParam +
stdDevThresholdRParam +
", verbose=TRUE)");
}
}

// Write results
Expand All @@ -334,4 +290,4 @@ protected void execute() {
getWorkspace().writeStatisticsResult(connection, statsCmd);
});
}
}
}
6 changes: 5 additions & 1 deletion ...pathdb/service/eda/compute/plugins/differentialabundance/DifferentialAbundancePlugin.java
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import org.gusdb.fgputil.ListBuilder;
import org.jetbrains.annotations.NotNull;
import org.veupathdb.service.eda.common.client.spec.StreamSpec;
import org.veupathdb.service.eda.common.model.CollectionDef;
import org.veupathdb.service.eda.common.model.EntityDef;
import org.veupathdb.service.eda.common.model.ReferenceMetadata;
import org.veupathdb.service.eda.common.model.VariableDef;
Expand Down Expand Up @@ -48,6 +49,8 @@ protected void execute() {
ReferenceMetadata meta = getContext().getReferenceMetadata();

CollectionSpec collectionSpec = computeConfig.getCollectionVariable();
CollectionDef collection = meta.getCollection(collectionSpec).orElseThrow();
String collectionMemberType = collection.getMember() == null ? "unknown" : collection.getMember();
String entityId = collectionSpec.getEntityId();
EntityDef entity = meta.getEntity(entityId).orElseThrow();
VariableDef computeEntityIdVarSpec = util.getEntityIdVarSpec(entityId);
Expand Down Expand Up @@ -125,7 +128,8 @@ protected void execute() {
{
abundanceDataClass = "AbsoluteAbundanceData";
}
connection.voidEval("inputData <- microbiomeData::" + abundanceDataClass + "(data=abundanceData" +
connection.voidEval("inputData <- microbiomeData::" + abundanceDataClass + "(name=" + singleQuote(collectionMemberType) +
", data=abundanceData" +
", sampleMetadata=sampleMetadata" +
", recordIdColumn=" + singleQuote(computeEntityIdColName) +
", ancestorIdColumns=as.character(" + dotNotatedIdColumnsString + ")" +
Expand Down
5 changes: 4 additions & 1 deletion ...java/org/veupathdb/service/eda/compute/plugins/rankedabundance/RankedAbundancePlugin.java
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import org.gusdb.fgputil.ListBuilder;
import org.jetbrains.annotations.NotNull;
import org.veupathdb.service.eda.common.client.spec.StreamSpec;
import org.veupathdb.service.eda.common.model.CollectionDef;
import org.veupathdb.service.eda.common.model.EntityDef;
import org.veupathdb.service.eda.common.model.ReferenceMetadata;
import org.veupathdb.service.eda.common.model.VariableDef;
Expand Down Expand Up @@ -42,6 +43,8 @@ protected void execute() {
RankedAbundanceComputeConfig computeConfig = getConfig();
PluginUtil util = getUtil();
ReferenceMetadata meta = getContext().getReferenceMetadata();
CollectionDef collection = meta.getCollection(computeConfig.getCollectionVariable()).orElseThrow();
String collectionMemberType = collection.getMember() == null ? "unknown" : collection.getMember();
String entityId = computeConfig.getCollectionVariable().getEntityId();
EntityDef entity = meta.getEntity(entityId).orElseThrow();
VariableDef computeEntityIdVarSpec = util.getEntityIdVarSpec(entityId);
Expand Down Expand Up @@ -75,7 +78,7 @@ protected void execute() {
}
dotNotatedIdColumnsString = dotNotatedIdColumnsString + ")";

connection.voidEval("abundDT <- microbiomeData::AbundanceData(data=" + INPUT_DATA +
connection.voidEval("abundDT <- microbiomeData::AbundanceData(name= " + util.singleQuote(collectionMemberType) + ",data=" + INPUT_DATA +
",recordIdColumn=" + util.singleQuote(computeEntityIdColName) +
",ancestorIdColumns=as.character(" + dotNotatedIdColumnsString + ")" +
",imputeZero=TRUE)");
Expand Down
26 changes: 6 additions & 20 deletions ...java/org/veupathdb/service/eda/compute/plugins/selfcorrelation/SelfCorrelationPlugin.java
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Original file line number Diff line number Diff line change
Expand Up @@ -110,37 +110,23 @@ protected void execute() {
// i think we cross that bridge when we get there and know more..
// NOTE: getMember tells us the member type, rather than gives us a literal member
String collectionMemberType = collection.getMember() == null ? "unknown" : collection.getMember();
boolean isEigengene = false;
String dataClassRString = "microbiomeData::AbundanceData";
if (collectionMemberType.toLowerCase().contains("eigengene")) {
isEigengene = true;
dataClassRString = "veupathUtils::CollectionWithMetadata";
}

// Prep data and run correlation
if (isEigengene) {
// If we have eigenegene data, we'll use our base correlation function in veupathUtils, so we
// only need to make data frames for the assay data and sample metadata.
connection.voidEval("assayData <- assayData[order(" + computeEntityIdColName + ")]; " +
"assayData <- assayData[, -as.character(" + dotNotatedEntityIdColumnsString +"), with=FALSE];" +
"assayData <- assayData[, -" + singleQuote(computeEntityIdColName) + ", with=FALSE]");

connection.voidEval("computeResult <- veupathUtils::selfCorrelation(data=assayData" +
", method=" + singleQuote(method) +
", verbose=TRUE)");
} else {
// If we don't have eigengene data, for now we can assume the data is abundance data.
// Abundance data can go through our microbiomeComputations pipeline.
connection.voidEval("data <- microbiomeData::AbundanceData(data=assayData" +
connection.voidEval("data <- " + dataClassRString + "(name=" + singleQuote(collectionMemberType) + ",data=assayData" +
", recordIdColumn=" + singleQuote(computeEntityIdColName) +
", ancestorIdColumns=as.character(" + dotNotatedEntityIdColumnsString + ")" +
", imputeZero=TRUE)");
// Run correlation!
connection.voidEval("computeResult <- veupathUtils::selfCorrelation(data=data" +

// Run correlation!
connection.voidEval("computeResult <- veupathUtils::selfCorrelation(data=data" +
", method=" + singleQuote(method) +
proportionNonZeroThresholdRParam +
varianceThresholdRParam +
stdDevThresholdRParam +
", verbose=TRUE)");
}

String statsCmd = "writeStatistics(computeResult, NULL, TRUE)";

Expand Down