Merge pull request #519 from GoekeLab/xgboost_bug_fix_for_v4

Xgboost bug fix for v4

Author: Chen Ying

R/bambu-processReads_scoreReadClasses.R

@@ -294,6 +294,7 @@ prepareTranscriptModelFeatures = function(rowData){
                       tx_strand_bias = readCount.posStrand, labels = equal) %>%
         mutate(
             tx_strand_bias=(1-abs(0.5-(tx_strand_bias/numReads))),
+			labels = as.logical(labels),
             numReads = log2(pmax(1,1+(numReads/scalingFactor)))
         )
     return(outData)

R/bambu-processReads_utilityJunctionErrorCorrection.R

@@ -92,7 +92,7 @@ testSpliceSites <- function(data, splice = "Start", prime = "start",
         predSplice.prime <- NULL
         if (is.null(junctionModel)) { 
             model = fitXGBoostModel(labels.train = 
-                as.integer(annotatedSplice)[mySet.all][mySet.training], 
+                as.logical(annotatedSplice)[mySet.all][mySet.training], 
                 data.train = modelmatrix[mySet.training,],
                 show.cv = verbose, maxSize.cv = 10000)
 

R/sysdata.rda

@@ -685,6 +685,17 @@ metadata(rowRanges(se))$warnings
 
 ### Release History
 
+**bambu v3.13.1**
+
+Release date: 2026-01-14
+
+Minor changes:
+
+- Resolve xgboost object incompatibility error [issue](https://github.com/GoekeLab/bambu/issues/447)  
+- Resolve process.y.margin.and.object error [issue](https://github.com/GoekeLab/bambu/issues/505)  
+- Resolve xgboost warnings related to argument updates in the recent xgboost version
+- Resolve the dplyr warning related to summarise usage [issue](https://github.com/GoekeLab/bambu/issues/380) 
+
 
 **bambu v3.8.2**
 

README.md

@@ -4,7 +4,7 @@
 # fitXGBoostModel() in test_xgboost.R 
 data_train <- matrix(seq(1:300000), nrow=100000)
 data_test <- matrix(c(seq(1:28000), seq(280001:300000)), nrow=16000)
-labels_train <- c(rep(1,50000), rep(0,50000))
+labels_train <- as.logical(c(rep(1,50000), rep(0,50000)))
 xgb_model <- fitXGBoostModel(labels_train, data_train, show.cv=TRUE)
 # Extract the predictions and results from the list
 xgb_predictions = predict(xgb_model, data_test)
@@ -110,14 +110,14 @@ seCombinedExtendedGeneExpected <- transcriptToGeneExpression(seCombinedExtended)
 ## prior models to use for scoreReadClass() and junctions()
 ##to train new ones see update_xgboost_models.R
 defaultModels = readRDS("./inst/extdata/defaultModels.rds")
-defaultModels$transcriptModelME = xgb.load("./inst/extdata/read_class_ME.model")
-defaultModels$transcriptModelSE = xgb.load("./inst/extdata/read_class_SE.model")  
+defaultModels$transcriptModelME = xgb.load("./inst/extdata/read_class_ME.ubj")
+defaultModels$transcriptModelSE = xgb.load("./inst/extdata/read_class_SE.ubj")  
 
 standardJunctionModels_temp = list()
-standardJunctionModels_temp$spliceSitePredictionStart.start = xgb.load("./inst/extdata/spliceSitePredictionStart.start.model")
-standardJunctionModels_temp$spliceSitePredictionStart.end = xgb.load("./inst/extdata/spliceSitePredictionStart.end.model")
-standardJunctionModels_temp$spliceSitePredictionEnd.start = xgb.load("./inst/extdata/spliceSitePredictionEnd.start.model")
-standardJunctionModels_temp$spliceSitePredictionEnd.end = xgb.load("./inst/extdata/spliceSitePredictionEnd.end.model")
+standardJunctionModels_temp$spliceSitePredictionStart.start = xgb.load("./inst/extdata/model_spliceSitePredictionStart.start.ubj")
+standardJunctionModels_temp$spliceSitePredictionStart.end = xgb.load("./inst/extdata/model_spliceSitePredictionStart.end.ubj")
+standardJunctionModels_temp$spliceSitePredictionEnd.start = xgb.load("./inst/extdata/model_spliceSitePredictionEnd.start.ubj")
+standardJunctionModels_temp$spliceSitePredictionEnd.end = xgb.load("./inst/extdata/model_spliceSitePredictionEnd.end.ubj")
 
 usethis::use_data(data1, data2, data3, data4, data5,
                   estOutput_woBC,