diff --git a/DESCRIPTION b/DESCRIPTION
index f544e69b0..e35ae591a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -100,7 +100,9 @@ Suggests:
     nlme,
     rpart,
     svglite,
-    ggplot2
+    ggplot2,
+    jointseg,
+    penaltyLearning
 Remotes: ropensci/RSelenium@v1.7.4
 License: GPL-3
 Encoding: UTF-8
diff --git a/tests/testthat/test-renderer3-figure-loss-small-data.R b/tests/testthat/test-renderer3-figure-loss-small-data.R
new file mode 100644
index 000000000..6937c79ce
--- /dev/null
+++ b/tests/testthat/test-renderer3-figure-loss-small-data.R
@@ -0,0 +1,329 @@
+acontext("FigureLossSmall")
+
+library(data.table)
+library(animint2)
+
+set.seed(100)
+numSamples <- 200000
+
+profile_id <- sample(8:182, numSamples, replace = TRUE) 
+
+# Construct a dataset which simulates neuroblastoma dataset
+random_chromosome <- sample(c(1:22, "X", "Y"), size = numSamples, replace = TRUE)
+random_position <- sample(5050:249063592, numSamples, replace = TRUE)
+logRatio <- runif(numSamples , min = -7.675, max = 9.871) 
+
+nb.dt <- data.table(profile.id = profile_id, chromosome = random_chromosome, position = random_position, logratio = logRatio)
+
+count.dt <- nb.dt[, list(
+  same=sum(diff(logratio)==0),
+  count=.N
+), by=list(profile.id, chromosome)]
+count.dt[order(same, -count)][count<1000]
+count.dt[order(count)]
+## Third data set, really small.
+## First profile has some consecutive data points that are the same.
+three <- nb.dt[profile.id=="117"&chromosome=="Y"]
+sum(diff(three$logratio)==0)
+max.segments <- nrow(three)
+fit <- jointseg::Fpsn(three$logratio, max.segments)
+loss.dt <- with(fit, data.table(
+  segments=1:max.segments,
+  loss=J.est))
+selected <- penaltyLearning::modelSelection(loss.dt, complexity="segments")
+loss.dt[, selected := ifelse(segments %in% selected$segments, "yes", "no")]
+loss.dt[, table(selected)]
+loss.dt[, table(selected)/nrow(loss.dt)]
+
+small.dt <- count.dt[count<1000]
+nb.small <- nb.dt[small.dt, on=list(profile.id, chromosome)]
+loss.small <- nb.small[, {
+  max.segments <- .N
+  fit <- jointseg::Fpsn(logratio, max.segments)
+  loss.dt <- with(fit, data.table(
+    changes=(1:max.segments)-1,
+    loss=J.est))
+  selected <- penaltyLearning::modelSelection(loss.dt, complexity="changes")
+  loss.dt[, selected := ifelse(changes %in% selected$changes, "yes", "no")]
+  loss.dt
+}, by=list(profile.id, chromosome)]
+
+
+
+prop.dt <- loss.small[, {
+  n.selected <- sum(selected=="yes")
+  data.table(
+    n.selected,
+    max.loss=max(loss),
+    prop.selected=n.selected/.N,
+    models=.N)
+}, by=list(profile.id, chromosome)]
+
+(some.props <- prop.dt[
+  sample(1:.N, 10)][, .SD[1], by=list(models, prop.selected)])
+
+some.loss <- loss.small[some.props, on=list(profile.id, chromosome)]
+some.nb <- nb.dt[some.props, on=list(profile.id, chromosome)]
+some.segs <- some.nb[, {
+  fit <- jointseg::Fpsn(logratio, .N)
+  seg.list <- list()
+  for(model.i in 1:.N){
+    end.vec <- fit$t.est[model.i, 1:model.i]
+    start.vec <- c(1, end.vec[-model.i]+1)
+    for(seg.i in 1:model.i){
+      start <- start.vec[[seg.i]]
+      end <- end.vec[[seg.i]]
+      seg.list[[paste(model.i, seg.i)]] <- data.table(
+        start,
+        end,
+        changes=model.i-1,
+        segStart=position[start],
+        segEnd=position[end],
+        mean=mean(logratio[start:end]))
+    }
+  }
+  do.call(rbind, seg.list)
+}, by=list(profile.id, chromosome)]
+some.selection <- some.loss[selected=="yes", {
+  penaltyLearning::modelSelection(.SD, complexity="changes")
+}, by=list(profile.id, chromosome)]
+some.breaks <- some.segs[0 < changes, list(
+  megabases=(segStart[-1]+segEnd[-.N])/2e6
+), by=list(profile.id, chromosome, changes)]
+some.segs[, beforeStart := {
+  c(
+    segStart[1]-1,
+    (segStart[-1]+segEnd[-.N])/2)/1e6
+}, by=list(profile.id, chromosome, changes)]
+some.segs[, afterEnd := {
+  c(
+    (segStart[-1]+segEnd[-.N])/2,
+    segEnd[.N]+1)/1e6
+}, by=list(profile.id, chromosome, changes)]
+
+some.nb[, megabases := position/1e6]
+some.nb[, pid.chr := paste0(profile.id, ".", chromosome)]
+some.breaks[, pid.chr := paste0(profile.id, ".", chromosome)]
+some.loss[, pid.chr := paste0(profile.id, ".", chromosome)]
+some.loss[, penalty := 0]
+some.loss[, min.changes := changes-0.5]
+some.loss[, max.changes := changes+0.5]
+some.props[, pid.chr := paste0(profile.id, ".", chromosome)]
+some.selection[, pid.chr := paste0(profile.id, ".", chromosome)]
+some.segs[, pid.chr := paste0(profile.id, ".", chromosome)]
+some.selection[, cost.at.min.lambda := min.lambda*changes+loss]
+vizWithUpdateAxes <- animint(
+  title="Changepoint model selection",
+  selected=ggplot()+
+    theme_bw()+
+    ggtitle("Click to select a data set")+
+    scale_x_log10(
+      "Number of data to segment",
+      breaks=c(range(some.props$models), 10, 100))+
+    ylab("Proportion of models selected by linear penalty")+
+    geom_point(aes(
+      models, prop.selected),
+      shape=21,
+      size=4,
+      clickSelects="pid.chr",
+      alpha=0.7,
+      data=data.frame(some.props)),
+  data=ggplot()+
+    ggtitle("Selected data set and model")+
+    theme_bw()+
+    ##theme_animint(update_axes=c("x", "y"))+
+    xlab("Position on chromosome (mega bases)")+
+    ylab("logratio (approximate DNA copy number")+
+    geom_segment(aes(
+      beforeStart, mean,
+      xend=afterEnd, yend=mean),
+      showSelected=c("pid.chr", "changes"),
+      color="green",
+      data=data.frame(some.segs))+
+    geom_vline(aes(
+      xintercept=megabases),
+      showSelected=c("pid.chr", "changes"),
+      color="green",
+      chunk_vars="pid.chr",
+      linetype="dashed",
+      data=data.frame(some.breaks))+
+    geom_point(aes(
+      megabases, logratio),
+      shape=21,
+      fill=NA,
+      showSelected="pid.chr",
+      data=data.frame(some.nb)),
+  loss=ggplot()+
+    ggtitle("Loss values for selected data set")+
+    ylab("loss")+
+    xlab("changes")+
+    theme_bw()+
+    theme_animint(update_axes=c("x", "y"))+
+    scale_size_manual(values=c(yes=3, no=4))+
+    geom_text(aes(
+      models, max.loss, label=paste0(
+        n.selected, "/", models,
+        " models selected by linear penalty")),
+      showSelected="pid.chr",
+      hjust=1,
+      data=data.frame(some.props))+
+    geom_point(aes(
+      changes, loss, color=selected, size=selected),
+      shape=21,
+      fill=NA,
+      showSelected="pid.chr",
+      data=data.frame(some.loss))+
+    geom_tallrect(aes(
+      xmin=min.changes, xmax=max.changes),
+      data=data.frame(some.loss),
+      alpha=0.5,
+      showSelected="pid.chr",
+      clickSelects="changes"),
+  lines=ggplot()+
+    ggtitle("Cost functions for selected data set")+
+    theme_bw()+
+    theme_animint(update_axes=c("x", "y"))+
+    geom_point(aes(
+      penalty, loss, color=selected),
+      fill=NA,
+      size=4,
+      showSelected="pid.chr",
+      data=data.frame(some.loss))+
+    geom_point(aes(
+      min.lambda, cost.at.min.lambda),
+      showSelected="pid.chr",
+      fill=NA,
+      data=data.frame(some.selection))+
+    ylab("cost(penalty) = loss + penalty*changes")+
+    geom_abline(aes(
+      slope=changes, intercept=loss, color=selected),
+      size=1,
+      showSelected="pid.chr",
+      chunk_vars="pid.chr",
+      data=data.frame(some.loss))+
+    geom_abline(aes(
+      slope=changes, intercept=loss, color=selected),
+      size=2,
+      showSelected=c("pid.chr", "changes"),
+      chunk_vars="pid.chr",
+      data=data.frame(some.loss))
+)
+
+infoWithUpdateAxes <- animint2HTML(vizWithUpdateAxes)
+
+test_that("<circle> rendered for all data sets (with update axes)", {
+  dataSets=getNodeSet(infoWithUpdateAxes$html, '//g[@class="geom1_point_selected"]//g//circle')
+  expect_equal(length(dataSets), nrow(some.props))
+})
+
+test_that("<rect> rendered for selected data set (with update axes)", {
+  nRect=getNodeSet(infoWithUpdateAxes$html, '//g[@class="geom4_point_data"]//g//circle')
+  expect_equal(length(nRect), nrow(some.loss[some.loss$profile.id==21, ]))
+})
+
+vizWithoutUpdateAxes <- animint(
+  title="Changepoint model selection",
+  selected=ggplot()+
+    theme_bw()+
+    ggtitle("Click to select a data set")+
+    scale_x_log10(
+      "Number of data to segment",
+      breaks=c(range(some.props$models), 10, 100))+
+    ylab("Proportion of models selected by linear penalty")+
+    geom_point(aes(
+      models, prop.selected),
+      shape=21,
+      size=4,
+      clickSelects="pid.chr",
+      alpha=0.7,
+      data=data.frame(some.props)),
+  data=ggplot()+
+    ggtitle("Selected data set and model")+
+    theme_bw()+
+    xlab("Position on chromosome (mega bases)")+
+    ylab("logratio (approximate DNA copy number")+
+    geom_segment(aes(
+      beforeStart, mean,
+      xend=afterEnd, yend=mean),
+      showSelected=c("pid.chr", "changes"),
+      color="green",
+      data=data.frame(some.segs))+
+    geom_vline(aes(
+      xintercept=megabases),
+      showSelected=c("pid.chr", "changes"),
+      color="green",
+      chunk_vars="pid.chr",
+      linetype="dashed",
+      data=data.frame(some.breaks))+
+    geom_point(aes(
+      megabases, logratio),
+      shape=21,
+      fill=NA,
+      showSelected="pid.chr",
+      data=data.frame(some.nb)),
+  loss=ggplot()+
+    ggtitle("Loss values for selected data set")+
+    ylab("loss")+
+    xlab("changes")+
+    theme_bw()+
+    scale_size_manual(values=c(yes=3, no=4))+
+    geom_text(aes(
+      models, max.loss, label=paste0(
+        n.selected, "/", models,
+        " models selected by linear penalty")),
+      showSelected="pid.chr",
+      hjust=1,
+      data=data.frame(some.props))+
+    geom_point(aes(
+      changes, loss, color=selected, size=selected),
+      shape=21,
+      fill=NA,
+      showSelected="pid.chr",
+      data=data.frame(some.loss))+
+    geom_tallrect(aes(
+      xmin=min.changes, xmax=max.changes),
+      data=data.frame(some.loss),
+      alpha=0.5,
+      showSelected="pid.chr",
+      clickSelects="changes"),
+  lines=ggplot()+
+    ggtitle("Cost functions for selected data set")+
+    theme_bw()+
+    geom_point(aes(
+      penalty, loss, color=selected),
+      fill=NA,
+      size=4,
+      showSelected="pid.chr",
+      data=data.frame(some.loss))+
+    geom_point(aes(
+      min.lambda, cost.at.min.lambda),
+      showSelected="pid.chr",
+      fill=NA,
+      data=data.frame(some.selection))+
+    ylab("cost(penalty) = loss + penalty*changes")+
+    geom_abline(aes(
+      slope=changes, intercept=loss, color=selected),
+      size=1,
+      showSelected="pid.chr",
+      chunk_vars="pid.chr",
+      data=data.frame(some.loss))+
+    geom_abline(aes(
+      slope=changes, intercept=loss, color=selected),
+      size=2,
+      showSelected=c("pid.chr", "changes"),
+      chunk_vars="pid.chr",
+      data=data.frame(some.loss))
+)
+
+infoWithoutUpdateAxes <- animint2HTML(vizWithUpdateAxes)
+
+test_that("<circle> rendered for all data sets (without update axes)", {
+  dataSets=getNodeSet(infoWithoutUpdateAxes$html, '//g[@class="geom1_point_selected"]//g//circle')
+  expect_equal(length(dataSets), nrow(some.props))
+})
+
+test_that("<rect> rendered for selected data set (widhout update axes)", {
+  nRect=getNodeSet(infoWithoutUpdateAxes$html, '//g[@class="geom4_point_data"]//g//circle')
+  expect_equal(length(nRect), nrow(some.loss[some.loss$profile.id==21, ]))
+})
+