diff --git a/R/data.R b/R/data.R
index fc00cfa5..bdd96e81 100644
--- a/R/data.R
+++ b/R/data.R
@@ -1,4 +1,3 @@
-
 #' Unbalanced treatment and time sample list
 #'
 #' A sample list with 4 columns SampleName, Well, Time and Treatment
@@ -86,3 +85,26 @@
 #' \item{Sex}{Female (F) or Male (M) - a constraint which kind of animal may receive the respective treatment}
 #' }
 "invivo_study_treatments"
+
+#' Example dataset with a plate effect
+#'
+#' Here top and bottom row were both used as controls (in dilutions). The top
+#' row however was affected differently than the bottom one. This makes
+#' normalization virtually impossible.
+#'
+#' @docType data
+#' @keywords datasets
+#' @name plate_effect_example
+#' @usage data(plate_effect_example)
+#' @author Balazs Banfai
+#'
+#' @format An object of class \code{"tibble"}
+#' \describe{
+#' \item{row}{Plate row}
+#' \item{column}{Plate column}
+#' \item{conc}{Sample concentration}
+#' \item{log_conc}{Logarithm of sample concentration}
+#' \item{treatment}{Sample treatment}
+#' \item{readout}{Readout from experiment}
+#' }
+"plate_effect_example"
diff --git a/data/plate_effect_example.rda b/data/plate_effect_example.rda
new file mode 100644
index 00000000..7ae80008
Binary files /dev/null and b/data/plate_effect_example.rda differ
diff --git a/man/plate_effect_example.Rd b/man/plate_effect_example.Rd
new file mode 100644
index 00000000..0420eecf
--- /dev/null
+++ b/man/plate_effect_example.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{plate_effect_example}
+\alias{plate_effect_example}
+\title{Example dataset with a plate effect}
+\format{
+An object of class \code{"tibble"}
+\describe{
+\item{row}{Plate row}
+\item{column}{Plate column}
+\item{conc}{Sample concentration}
+\item{log_conc}{Logarithm of sample concentration}
+\item{treatment}{Sample treatment}
+\item{readout}{Readout from experiment}
+}
+}
+\usage{
+data(plate_effect_example)
+}
+\description{
+Here top and bottom row were both used as controls (in dilutions). The top
+row however was affected differently than the bottom one. This makes
+normalization virtually impossible.
+}
+\author{
+Balazs Banfai
+}
+\keyword{datasets}
diff --git a/vignettes/NCS22_talk.Rmd b/vignettes/NCS22_talk.Rmd
index adf608d9..373df995 100644
--- a/vignettes/NCS22_talk.Rmd
+++ b/vignettes/NCS22_talk.Rmd
@@ -26,25 +26,81 @@ library(tidyverse)
 
 # Introduction
 
-Examples in this vignette are used in a talk at NCS22.
+Examples in this vignette are used were used in our presentation.
 
 It uses a subset of the `longitudinal_subject_samples` dataset.
 
 ```{r get_data, include = TRUE}
 data("longitudinal_subject_samples")
 
-dat <- longitudinal_subject_samples %>% 
-  filter(Group %in% 1:5, Week %in% c(1,4)) %>% 
+dat <- longitudinal_subject_samples |>
+  filter(Group %in% 1:5, Week %in% c(1, 4)) |>
   select(SampleID, SubjectID, Group, Sex, Week)
 
-# for simplicity: remove two subjects that don't have both visits  
-dat <- dat %>% 
-  filter(SubjectID %in% (dat %>% count(SubjectID) %>% filter(n == 2) %>% .$SubjectID)) 
+# for simplicity: remove two subjects that don't have both visits
+dat <- dat |>
+  filter(SubjectID %in%
+    (dat |> count(SubjectID) |> filter(n == 2) |> pull(SubjectID)))
 
 
-subject_data <- dat %>% select(SubjectID, Group, Sex) %>% unique()
+subject_data <- dat |>
+  select(SubjectID, Group, Sex) |>
+  unique()
 ```
 
+## Batch effects matter
+Here's an example of plate effect. Here both top and bottom rows of the
+plate are used as controls. 
+
+This is the experiment design:
+
+```{r, fig.width= 4, fig.height=3, echo = FALSE}
+data("plate_effect_example")
+plate_effect_example |>
+  ggplot() +
+  aes(x = column, y = row, fill = treatment, alpha = log_conc) +
+  geom_tile() +
+  theme(
+    panel.grid.major = element_blank(),
+    panel.grid.minor = element_blank()
+  ) +
+  scale_y_discrete(limits = rev) +
+  scale_fill_brewer(palette = "Set1") +
+  # make transparency more visible
+  scale_alpha_continuous(range = c(0.2, 1)) +
+  ggtitle("Design")
+```
+
+These are the readouts:
+
+```{r, fig.width= 4, fig.height=5, echo = FALSE}
+p1 <- plate_effect_example |>
+  ggplot() +
+  aes(x = column, y = row, fill = readout) +
+  geom_tile() +
+  theme(
+    panel.grid.major = element_blank(),
+    panel.grid.minor = element_blank()
+  ) +
+  scale_y_discrete(limits = rev) +
+  ggtitle("Readout")
+
+p2 <- plate_effect_example |>
+  filter(treatment == "control") |>
+  mutate(column = as.numeric(column)) |>
+  ggplot() +
+  aes(x = column, y = readout, color = row) +
+  geom_point() +
+  geom_line() +
+  scale_color_brewer(palette = "Set1") +
+  ggtitle("Control")
+
+cowplot::plot_grid(p1, p2, nrow = 2)
+```
+
+Due to the plate effect, the control rows are affected differently. It is
+virtually impossible to normalize readouts in a meaningful way.
+
 ## Go fully random? 
 
 * Could it be sufficient to randomly distribute samples across batches?
@@ -56,8 +112,8 @@ subject_data <- dat %>% select(SubjectID, Group, Sex) %>% unique()
 set.seed(17) # gives `bad` random assignment
 
 bc <- BatchContainer$new(
-  dimensions = list("batch" = 3, "location" = 11),
-) %>%
+  dimensions = list("batch" = 3, "location" = 11)
+) |>
   assign_random(subject_data)
 ```
 
@@ -65,8 +121,9 @@ Gone wrong: Random distribution of 31 grouped subjects into 3 batches
 turns out unbalanced: 
 
 ```{r, fig.width= 3, fig.height=3, echo = FALSE}
-bc$get_samples() %>% ggplot(aes(x = batch, fill = Group)) +
-  geom_bar() + 
+bc$get_samples() |>
+  ggplot(aes(x = batch, fill = Group)) +
+  geom_bar() +
   labs(y = "subject count")
 ```
 
@@ -110,8 +167,8 @@ set.seed(17) # gives `bad` random assignment
 ```
 ```{r}
 bc <- BatchContainer$new(
-  dimensions = list("batch" = 3, "location" = 11),
-) %>%
+  dimensions = list("batch" = 3, "location" = 11)
+) |>
   assign_random(subject_data)
 ```
 
@@ -121,10 +178,12 @@ bc <- BatchContainer$new(
 ```{r, fig.width= 5.5, fig.height=3, echo = FALSE}
 cowplot::plot_grid(
   plotlist = list(
-    bc$get_samples() %>% ggplot(aes(x = batch, fill = Group)) +
+    bc$get_samples() |>
+      ggplot(aes(x = batch, fill = Group)) +
       geom_bar() +
       labs(y = "subject count"),
-    bc$get_samples() %>% ggplot(aes(x = batch, fill = Sex)) +
+    bc$get_samples() |>
+      ggplot(aes(x = batch, fill = Sex)) +
       geom_bar() +
       labs(y = "subject count")
   ),
@@ -137,16 +196,23 @@ bc$get_samples()
 ```
 
 ```{r, echo=FALSE}
-bind_rows(head(bc$get_samples(), 3) %>% 
-            mutate(across(everything(), as.character)),
-          tibble(batch = "...", 
-                 location = " ...", 
-                 SubjectID = "...", 
-                 Group = "...", Sex = "..."),
-          tail(bc$get_samples(), 3) %>% 
-            mutate(across(everything(), as.character))) %>% 
-  gt::gt() %>% gt::tab_options(table.font.size = 11, 
-                               data_row.padding = 0.1)
+bind_rows(
+  head(bc$get_samples(), 3) |>
+    mutate(across(everything(), as.character)),
+  tibble(
+    batch = "...",
+    location = " ...",
+    SubjectID = "...",
+    Group = "...", Sex = "..."
+  ),
+  tail(bc$get_samples(), 3) |>
+    mutate(across(everything(), as.character))
+) |>
+  gt::gt() |>
+  gt::tab_options(
+    table.font.size = 11,
+    data_row.padding = 0.1
+  )
 ```
 
 
@@ -183,10 +249,12 @@ bc <- optimize_design(
 ```{r, fig.width= 8, fig.height=3, echo = FALSE}
 cowplot::plot_grid(
   plotlist = list(
-    bc$get_samples() %>% ggplot(aes(x = batch, fill = Group)) +
+    bc$get_samples() |>
+      ggplot(aes(x = batch, fill = Group)) +
       geom_bar() +
       labs(y = "subject count"),
-    bc$get_samples() %>% ggplot(aes(x = batch, fill = Sex)) +
+    bc$get_samples() |>
+      ggplot(aes(x = batch, fill = Sex)) +
       geom_bar() +
       labs(y = "subject count"),
     bc$plot_trace(include_aggregated = TRUE)
@@ -197,16 +265,23 @@ cowplot::plot_grid(
 
 
 ```{r, echo=FALSE}
-bind_rows(head(bc$get_samples(), 3) %>% 
-            mutate(across(everything(), as.character)),
-          tibble(batch = "...", 
-                 location = " ...", 
-                 SubjectID = "...", 
-                 Group = "...", Sex = "..."),
-          tail(bc$get_samples(), 3) %>% 
-            mutate(across(everything(), as.character))) %>% 
-  gt::gt() %>% gt::tab_options(table.font.size = 11, 
-                               data_row.padding = 0.1)
+bind_rows(
+  head(bc$get_samples(), 3) |>
+    mutate(across(everything(), as.character)),
+  tibble(
+    batch = "...",
+    location = " ...",
+    SubjectID = "...",
+    Group = "...", Sex = "..."
+  ),
+  tail(bc$get_samples(), 3) |>
+    mutate(across(everything(), as.character))
+) |>
+  gt::gt() |>
+  gt::tab_options(
+    table.font.size = 11,
+    data_row.padding = 0.1
+  )
 ```
 
 
@@ -235,41 +310,117 @@ of interest evenly across the plate and adjust for the effect computationally.
     * sex (lower priority)
 
 ```{r}
-set.seed(1) #1 #2
+set.seed(4)
 
 bc <- BatchContainer$new(
-  dimensions = list("plate" = 3, "row" = 4, "col" = 6),
-) %>%
-  assign_random(dat)
-#  assign_in_order(dat)
+  dimensions = list("plate" = 3, "row" = 4, "col" = 6)
+) |>
+  assign_in_order(dat)
 ```
 
 ```{r, fig.width= 5, fig.height=4.5, eval=FALSE}
-plot_plate(bc, plate = plate, row = row, column = col,
-           .color = Group, title = "Initial layout by Group")
-plot_plate(bc, plate = plate, row = row, column = col, 
-           .color = Sex, title = "Initial layout by Sex")
+plot_plate(bc,
+  plate = plate, row = row, column = col,
+  .color = Group, title = "Initial layout by Group"
+)
+plot_plate(bc,
+  plate = plate, row = row, column = col,
+  .color = Sex, title = "Initial layout by Sex"
+)
 ```
 
 ```{r, fig.width= 5, fig.height=4.5, echo=FALSE}
 cowplot::plot_grid(
   plotlist = list(
-    plot_plate(bc, plate = plate, row = row, column = col, 
-               .color = Group, title = "Initial layout by Group"),
-    plot_plate(bc, plate = plate, row = row, column = col, 
-               .color = Sex, title = "Initial layout by Sex")
+    plot_plate(bc,
+      plate = plate, row = row, column = col,
+      .color = Group, title = "Initial layout by Group"
+    ),
+    plot_plate(bc,
+      plate = plate, row = row, column = col,
+      .color = Sex, title = "Initial layout by Sex"
+    )
   ),
   nrow = 2
 )
 ```
 
-**2-step optimization** `multi_plate_layout()`
+## 2-step optimization
 
-* Across plate optimization using osat score as before
-* Within plate optimization using distance based sample scoring function
+### Across plate optimization using osat score as before
+```{r, warning=FALSE}
+bc1 <- optimize_design(
+  bc,
+  scoring = list(
+    group = osat_score_generator(
+      batch_vars = "plate",
+      feature_vars = "Group"
+    ),
+    sex = osat_score_generator(
+      batch_vars = "plate",
+      feature_vars = "Sex"
+    )
+  ),
+  n_shuffle = 1,
+  acceptance_func =
+    ~ accept_leftmost_improvement(..., tolerance = 0.01),
+  max_iter = 150,
+  quiet = TRUE
+)
+```
 
+```{r, fig.width= 5, fig.height=4.5, echo=FALSE}
+cowplot::plot_grid(
+  plotlist = list(
+    plot_plate(bc1,
+      plate = plate, row = row, column = col,
+      .color = Group, title = "Layout after the first step, Group"
+    ),
+    plot_plate(bc1,
+      plate = plate, row = row, column = col,
+      .color = Sex, title = "Layout after the first step, Sex"
+    )
+  ),
+  nrow = 2
+)
+```
 
-## Spatial arrangement
+### Within plate optimization using distance based sample scoring function
+```{r, warning=FALSE}
+bc2 <- optimize_design(
+  bc1,
+  scoring = mk_plate_scoring_functions(
+    bc1,
+    plate = "plate", row = "row", column = "col",
+    group = "Group"
+  ),
+  shuffle_proposal_func = shuffle_with_constraints(dst = plate == .src$plate),
+  max_iter = 150,
+  quiet = TRUE
+)
+```
+
+```{r, fig.width= 5, fig.height=4.5, echo=FALSE}
+cowplot::plot_grid(
+  plotlist = list(
+    plot_plate(bc2,
+      plate = plate, row = row, column = col,
+      .color = Group, title = "Layout after the second step, Group"
+    ),
+    plot_plate(bc2,
+      plate = plate, row = row, column = col,
+      .color = Sex, title = "Layout after the second step, Sex"
+    )
+  ),
+  nrow = 2
+)
+```
+
+
+## 2-step optimization `multi_plate_layout()`
+
+We are performing the same optimization as before, but using the
+`multi_plate_layout()` function to combine the two steps.
 
 ```{r, warning=FALSE, message=FALSE} 
 bc <- optimize_multi_plate_design(
@@ -278,7 +429,7 @@ bc <- optimize_multi_plate_design(
   within_plate_variables = c("Group"),
   plate = "plate", row = "row", column = "col",
   n_shuffle = 2,
-  max_iter = 500 #2000
+  max_iter = 500 # 2000
 )
 ```
 
@@ -286,10 +437,14 @@ bc <- optimize_multi_plate_design(
 ```{r, fig.width= 5, fig.height=4.5, echo=FALSE}
 cowplot::plot_grid(
   plotlist = list(
-    plot_plate(bc, plate = plate, row = row, column = col, 
-               .color = Group, title = "Initial layout by Group"),
-    plot_plate(bc, plate = plate, row = row, column = col, 
-               .color = Sex, title = "Initial layout by Sex")
+    plot_plate(bc,
+      plate = plate, row = row, column = col,
+      .color = Group, title = "After optimization, Group"
+    ),
+    plot_plate(bc,
+      plate = plate, row = row, column = col,
+      .color = Sex, title = "After optimization, Sex"
+    )
   ),
   nrow = 2
 )
@@ -340,13 +495,14 @@ see vignette `invivo_study_design` for the full story.
 
 
 ```{r, fig.width=4.0, fig.hight = 5.0, echo = FALSE}
-layout <- crossing(row = 1:9, column = 1:12) %>%
-  mutate(Questions = "no") 
-layout$Questions[c(16, 17, 18, 19, 20, 21, 
-                  27, 28, 33, 34, 
-                  45, 46, 
-                  55, 56, 66, 67, 90, 91)] <- "yes"
+layout <- crossing(row = 1:9, column = 1:12) |>
+  mutate(Questions = "no")
+layout$Questions[c(
+  16, 17, 18, 19, 20, 21,
+  27, 28, 33, 34,
+  45, 46,
+  55, 56, 66, 67, 90, 91
+)] <- "yes"
 
 plot_plate(layout, .color = Questions, title = "Thank you")
-
 ```