diff --git a/DESCRIPTION b/DESCRIPTION
index 5bbf21f..52693ae 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: TFBlearner
 Title: Functionality for training TF-specific classifiers to predict TF bindings based on ATAC-seq data.
-Version: 0.1.0
+Version: 0.1.1
 Authors@R: 
     person("Emanuel", "Sonder", , "emanuel.sonder@hest.ethz.ch", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-4788-9508"))
diff --git a/R/getFeatureMatrix.R b/R/getFeatureMatrix.R
index b6f1058..9d277cf 100644
--- a/R/getFeatureMatrix.R
+++ b/R/getFeatureMatrix.R
@@ -178,8 +178,8 @@ getFeatureMatrix <- function(mae,
   }
 
   # get the cofactors
-  tfCofactors <- unique(unlist(subset(colData(mae[[TFFEAT]]),
-                                      get(TFNAMECOL)==tf)[[TFCOFACTORSCOL]]))
+  tfCofactors <- unlist(subset(colData(mae[[TFFEAT]]),
+                               get(TFNAMECOL)==tf)[[TFCOFACTORSCOL]])
 
   message("Attaching Site & TF-Features")
   selMotifs <- subset(colData(mae[[TFFEAT]]),
diff --git a/R/listFeatures.R b/R/listFeatures.R
index 27462e3..042b674 100644
--- a/R/listFeatures.R
+++ b/R/listFeatures.R
@@ -22,7 +22,9 @@
 #' @export
 listFeatures <- function(){
   motifPrefices <- c(PRIORMOTIFPREFIX, TFMOTIFPREFIX, TFCOFACTORMOTIFPREFIX,
-                     SELMOTIFPREFIX, CTCFMOTIFPREFIX)
+                     paste(SELMOTIFPREFIX, COMOTIFAFFIX, sep="."),
+                     paste(SELMOTIFPREFIX, EXMOTIFAFFIX, sep="."),
+                     CTCFMOTIFPREFIX)
   featList <- data.table(feature_name=c("Sequence",
                                         "Width",
                                         "Annot",
diff --git a/R/tfFeatures.R b/R/tfFeatures.R
index dac85ee..501ddf0 100644
--- a/R/tfFeatures.R
+++ b/R/tfFeatures.R
@@ -142,7 +142,7 @@
                           maxScores,
                           labels,
                           nMotifs=10,
-                          subSample=10000)
+                          subSample=2e5)
 {
   labels <- .binMat(labels, threshold=0L)
   labels <- .marginMax(labels, margin="row")
@@ -200,9 +200,7 @@
 
 .getCofactorBindings <- function(chIPMat, tfCofactors){
   tfCols <- unlist(tstrsplit(colnames(chIPMat), split="_", keep=2))
-  namesSub <- names(tfCofactors)[which(tfCofactors %in% tfCols)]
-  tfCofactors <- intersect(tfCols, tfCofactors)
-  names(tfCofactors) <- namesSub
+  tfCofactors <- tfCofactors[tfCofactors %in% tfCols]
 
   if(length(tfCofactors)>0){
     cofactBindings <- lapply(tfCofactors, function(tfCol){
@@ -210,7 +208,8 @@
       colnames(cofactBinding) <- paste(COBINDFEATNAME, tfCol, sep=".")
       cofactBinding})
     names(cofactBindings) <- paste(COBINDFEATNAME,
-                                   gsub(MOTIFAFFIX, "", namesSub), sep="_")
+                                   gsub(MOTIFAFFIX, "", names(tfCofactors)),
+                                   sep="_")
     return(cofactBindings)}
   else{
     return(NULL)
@@ -416,10 +415,12 @@ tfFeatures <- function(mae,
   if("Cofactor_Binding" %in% features){
     message("Cofactor Bindings")
     if(is.null(tfCofactors)){
-      stop("Please provide cofactor names (`tfCofactors`) if Cofactor_Bindings should be computed.")}
-    cofactBindings <- .getCofactorBindings(chIPMat, tfCofactors)
-    if(!is.null(cofactBindings)){
-      featMats <- append(featMats, cofactBindings)
+      warning("Please provide cofactor names (`tfCofactors`) if Cofactor_Bindings should be computed.")
+    }else{
+      cofactBindings <- .getCofactorBindings(chIPMat, tfCofactors)
+      if(!is.null(cofactBindings)){
+        featMats <- append(featMats, cofactBindings)
+      }
     }
   }
 
@@ -474,10 +475,7 @@ tfFeatures <- function(mae,
     names(tfSimMotifCols) <- paste(PRIORMOTIFPREFIX, 1:length(tfSimMotifCols),
                                 sep="_")}
 
-  tfCofactorCols <- intersect(tfCofactors, motifNames)
-  if(length(tfCofactorCols)>0){
-    names(tfCofactorCols) <- paste(TFCOFACTORMOTIFPREFIX,
-                                   1:length(tfCofactorCols), sep="_")}
+  tfCofactorCols <- tfCofactors[tfCofactors %in% motifNames]
   tfMotifCols <- intersect(tfName, motifNames)
   if(length(tfMotifCols)>0){
     names(tfMotifCols) <- paste(TFMOTIFPREFIX, 1:length(tfMotifCols), sep="_")}
@@ -529,10 +527,7 @@ tfFeatures <- function(mae,
     names(tfSimMotifCols) <- paste(PRIORMOTIFPREFIX, 1:length(tfSimMotifCols),
                                    sep="_")}
 
-  tfCofactorCols <- intersect(tfCofactors, actMotifNames)
-  if(length(tfCofactorCols)>0){
-    names(tfCofactorCols) <- paste(TFCOFACTORMOTIFPREFIX,
-                                   1:length(tfCofactorCols), sep="_")}
+  tfCofactorCols <- tfCofactors[tfCofactors %in% actMotifNames]
   tfMotifCols <- intersect(tfName, actMotifNames)
   if(length(tfMotifCols)>0){
     names(tfMotifCols) <- paste(TFMOTIFPREFIX, 1:length(tfMotifCols), sep="_")}
@@ -563,7 +558,7 @@ tfFeatures <- function(mae,
     selActMotifs <- .selectMotifs(actAssoc, rep(1*scaleFactAct, ncol(actAssoc)),
                                   labels, nMotifs=nMotifs)
     if(length(selActMotifs)>0){
-      names(selActMotifs) <- paste0(SELMOTIFPREFIX, names(selActMotifs))}
+      names(selActMotifs) <- paste(SELMOTIFPREFIX, names(selActMotifs), sep=".")}
   }
   else{
     selActMotifs <- NULL
diff --git a/R/trainTfModel.R b/R/trainTfModel.R
index b64472e..fe9ee0b 100644
--- a/R/trainTfModel.R
+++ b/R/trainTfModel.R
@@ -560,6 +560,17 @@
     stop(paste("Feature matrix has been computed for", fmTfName, "and not for", tfName))
   }
 
+  # check for cellular contexts containing no-positive labels
+  labelCountDt <- as.data.table(table(isPos=assays(fm)$features[,LABELCOLNAME]>0,
+                                      context=rowData(fm)[[annoCol]]))
+  labelCountDt[,isPos:=as.logical(isPos)]
+  zeroContexts <- subset(labelCountDt, isPos & N==0)$context
+  if(length(zeroContexts)>0){
+    warning(paste("Context(s):", paste(zeroContexts, collapse=","),
+          "contain(s) no positive labels - removed from training data"))
+    fm <- fm[!(rowData(fm)[[annoCol]] %in% zeroContexts),]
+  }
+
   # sample stacked chrs
   rangesFm <- unique(rowRanges(fm))
   mcols(rangesFm) <- NULL
diff --git a/tests/testthat/test-getFeatureMatrix.R b/tests/testthat/test-getFeatureMatrix.R
index 8c7fd95..768dd8a 100644
--- a/tests/testthat/test-getFeatureMatrix.R
+++ b/tests/testthat/test-getFeatureMatrix.R
@@ -93,7 +93,9 @@ test_that("Feature Matrix: Correct context selection - only for specified contex
                "A549")
   expect_equal(obsRanges, expRanges)
   expect_equal(metadata(fm)[[annoCol]], "A549")
-  expect_equal(metadata(fm)[[TFCOFACTORSCOL]], "JUN")
+  expCofactors <- c("JUN")
+  names(expCofactors) <- c("tfCofactorMotif_1")
+  expect_equal(metadata(fm)[[TFCOFACTORSCOL]], expCofactors)
 })
 
 test_that("Feature Matrix: Correct metadata assignment", {
diff --git a/tests/testthat/test-tfFeatures.R b/tests/testthat/test-tfFeatures.R
index 9edadb0..c4f960b 100644
--- a/tests/testthat/test-tfFeatures.R
+++ b/tests/testthat/test-tfFeatures.R
@@ -60,3 +60,9 @@ test_that("Preselected motifs are saved in colData", {
   expect_true(is.vector(preSelActMotifs))
   expect_equal(preSelActMotifs[[paste(TFMOTIFPREFIX, 1, sep="_")]], "JUN")
 })
+
+test_that("No cofactors provided for co-binding features", {
+  experiments(maeTest2)[[TFFEAT]] <- NULL
+  expect_warning(tfFeatures(maeTest2, tfName="JUN", tfCofactors=NULL),
+                 regexp="provide cofactor names")
+})
diff --git a/tests/testthat/test-trainTfModel.R b/tests/testthat/test-trainTfModel.R
index 43e9d53..a510d1c 100644
--- a/tests/testthat/test-trainTfModel.R
+++ b/tests/testthat/test-trainTfModel.R
@@ -337,3 +337,12 @@ test_that("Sampling of additional points for training unweighted",{
   expect_equal(sum(labels[trainSet]==1)/length(trainSet),
                posFracExp, tolerance=0.01)
 })
+
+test_that("Context containing no positive labels",{
+  assays(fmTest)$features[rowData(fmTest)$context=="A549", LABELCOLNAME] <- 0
+  expect_no_error(trainTfModel(tfName="CTCF", fmTest, evalRounds=1))
+  expect_warning(trainTfModel(tfName="CTCF", fmTest, evalRounds=1),
+                 regexp="A549.*no positive labels")
+})
+
+# Context(s): A549 contain(s) no positive labels - removed from training data