diff --git a/README.Rmd b/README.Rmd
index f46e41a..6886246 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -2,68 +2,101 @@
 title: 'ggcyto : Visualize `Cytometry` data with `ggplot`'
 output:
   html_document:
-    fig_height: 2
-    fig_width: 2
+    fig_height: 1
+    fig_width: 1
     keep_md: yes
+vignette: >    
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteIndexEntry{Feature summary of ggcyto}
 ---
 
 ```{r, echo=FALSE}
 library(knitr)
 opts_chunk$set(message = FALSE, warning = FALSE, fig.height= 3, fig.width= 5)
 ```
+`ggcyto` is a cytometry data visualization tool built around ggplot and the grammar of graphics paradigm. The software extends the popular ggplot2 framework, already familiar to many data scientists, enabling it to recog-nize the core Bioconductor flow cytometry data structures for gated and annotated cytometry data. It simplifies visualization and plotting of flow data for publication quality graphics. 
 
-### Overloaded `fortify` S3 method makes `Cytometry` data to be fully compatible with `ggplot`. 
+
+There are three ways to construct the `ggcyto` plots. Each represents a different level of complexity and flexibility.  They meet the needs of various plot applications and thus are suitable for users at different levels of coding skills.
+
+# Quick plot
+
+This inherits the spirit from ggplot's `Quick plot`, which simplies the plotting job by hiding more details from users and taking more assumptions for the plot.
+
+* see [autoplot](vignettes/autoplot.md)
+
+# More flexibility with **ggcyto** wrapper
+
+`ggcyto` constructor along with overloaded `+` operator gives user more flexibility to fine-tune the plot yet still encapsulates lots of details that might be tedious and intimidating for many users.
+
+See more examples of `ggcyto` constructor here:
+
+* [ggcyto + flowSet](vignettes/ggcyto.flowSet.md)
+* [ggcyto + GatingSet](vignettes/ggcyto.GatingSet.md)
+
+# Use `ggplot` directly to have more controls. 
+The package overloads ggplot's `fortify` S3 method so that `Cytometry` data structures (e.g. `flowSet/flowFrame`) are fully compatible with `ggplot`. More examples of using `ggplot` directly on `flowSet`:
+
+* [ggplot + flowSet1d](vignettes/advanced/ggplot.flowSet.1d.md)
+* [ggplot + flowSet2d](vignettes/advanced/ggplot.flowSet.2d.md)
+* [ggplot + flowSet + gate](vignettes/advanced/ggplot.flowSet.gate.md)
+* [ggplot + flowSet + overlay](vignettes/advanced/ggplot.flowSet.overlay.md)
+
+# quick demos of some most used features 
 
 ```{r}
 library(ggcyto)
 dataDir <- system.file("extdata",package="flowWorkspaceData")
-gs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))
-fs <- getData(gs, "CD3+")
+gs <- load_gs(list.files(dataDir, pattern = "gs_bcell_auto",full = TRUE))
 ```
 
-### Quick plot with [autoplot](vignettes/autoplot.md) 
-```{r}
-#1d
-autoplot(fs, "CD4")
-#2d
-autoplot(fs, "CD4", "CD8", bins = 64)
-
-autoplot(gs, c("CD4", "CD8"), bins = 64)
-#plot all channels
-autoplot(fs[[1]]) + labs_cyto("marker")
+```{r echo=FALSE}
+gs@transformation <- transformerList(colnames(gs)[-(1:2)], flowJo_biexp_trans())
 ```
 
-### More flexibility with **ggcyto** wrapper
-
-#### [ggcyto + flowSet](vignettes/ggcyto.flowSet.md)
 ```{r}
-# support fuzzy-matching of aes to the data
-# with flowJo-type of default color fills
-# facet on `name` by default
-ggcyto(fs,aes(x = CD4, y = CD8)) + geom_hex(bins = 64) + xlim(0, 3600)
+#plot a gate by specifying the population node name (here it is 'CD3')
+autoplot(gs, "CD3")
+#change the resolution
+p <- autoplot(gs, "CD3", bins = 64)
+p
+#display the transformed value at breaks label by turning off the inverse transform
+autoplot(gs, "CD3", axis_inverse_trans = FALSE)
+#you can switch the limits from default `instrument` to the actual `data` range
+p + ggcyto_par_set(limits = "data")
+# Choose between `marker` and `channel` names for axis label text
+p + labs_cyto("channel") #default is "both"
+# overlay another population 'IgD-CD27-' as dots on top of the existing plot
+p + geom_overlay("IgD-CD27-", alpha = 0.5, size = 0.1, color = "purple")
+# plot a population without gate
+fs <- getData(gs, "CD20") #extract the gated data as a flowSet
+autoplot(fs, "CD20", "CD19") #plot 2D
 ```
 
-#### [ggcyto + GatingSet](vignettes/ggcyto.GatingSet.md)
-```{r}
-ggcyto(gs,aes(x = CCR7, y = CD45RA), subset = "CD4") + geom_hex(bins = 64) + geom_gate("CD4/CCR7+ 45RA+") + geom_stats(fill = "yellow", size = 4)
+```{r fig.height = 1}
+autoplot(fs, "CD20") #1d density
+```
+```{r fig.height = 4}
+#extract one sample as a flowFrame
+fr <- fs[[1]]
+#plot 1d density on all available channels
+autoplot(fr)
 ```
 
-### Use `ggplot` directly to have more controls. 
-```{r}
-# 1d
-p <- ggplot(fs, aes(x = `<B710-A>`)) + facet_wrap(~name) 
-#histogram plot
-p + geom_histogram(colour = "white")
-#density plot
-p + geom_density(fill = "black")
-
-# 2d hexbin
-ggplot(fs, aes(x = `<B710-A>`, y = `<R780-A>`)) + facet_wrap(~name) + geom_hex(bins = 64)
+ 
+```{r fig.height = 4}
+gh <- gs[[1]] # extract a `GatingHierarchy` object for one sample
+# layout multiple cell populations with their asssociated gates in the same plot.
+nodes <- getNodes(gh)[c(3:9, 14)]
+p <- autoplot(gh, nodes, bins = 64)
+p
 ```
 
-More examples of using `ggplot` directly on `flowSet`:
+```{r fig.width=8, fig.height = 2}
+#arrange it as one-row gtable object
+gt <- ggcyto_arrange(p, nrow = 1)
+plot(gt)
+```
+
+
 
-* [ggplot + flowSet1d](vignettes/advanced/ggplot.flowSet.1d.md)
-* [ggplot + flowSet2d](vignettes/advanced/ggplot.flowSet.2d.md)
-* [ggplot + flowSet + gate](vignettes/advanced/ggplot.flowSet.gate.md)
-* [ggplot + flowSet + overlay](vignettes/advanced/ggplot.flowSet.overlay.md)
diff --git a/README.md b/README.md
index 40e50f9..d6de366 100644
--- a/README.md
+++ b/README.md
@@ -1,95 +1,134 @@
-# ggcyto : Visualize `Cytometry` data with `ggplot`
+# ggcyto : Visualize `Cytometry` data with `ggplot`'
 
 
 
-### Overloaded `fortify` S3 method makes `Cytometry` data to be fully compatible with `ggplot`. 
+`ggcyto` is a cytometry data visualization tool built around ggplot and the grammar of graphics paradigm. The software extends the popular ggplot2 framework, already familiar to many data scientists, enabling it to recog-nize the core Bioconductor flow cytometry data structures for gated and annotated cytometry data. It simplifies visualization and plotting of flow data for publication quality graphics. 
+
+
+There are three ways to construct the `ggcyto` plots. Each represents a different level of complexity and flexibility.  They meet the needs of various plot applications and thus are suitable for users at different levels of coding skills.
+
+# Quick plot
+
+This inherits the spirit from ggplot's `Quick plot`, which simplies the plotting job by hiding more details from users and taking more assumptions for the plot.
+
+* see [autoplot](vignettes/autoplot.md)
+
+# More flexibility with **ggcyto** wrapper
+
+`ggcyto` constructor along with overloaded `+` operator gives user more flexibility to fine-tune the plot yet still encapsulates lots of details that might be tedious and intimidating for many users.
+
+See more examples of `ggcyto` constructor here:
+
+* [ggcyto + flowSet](vignettes/ggcyto.flowSet.md)
+* [ggcyto + GatingSet](vignettes/ggcyto.GatingSet.md)
+
+# Use `ggplot` directly to have more controls. 
+The package overloads ggplot's `fortify` S3 method so that `Cytometry` data structures (e.g. `flowSet/flowFrame`) are fully compatible with `ggplot`. More examples of using `ggplot` directly on `flowSet`:
+
+* [ggplot + flowSet1d](vignettes/advanced/ggplot.flowSet.1d.md)
+* [ggplot + flowSet2d](vignettes/advanced/ggplot.flowSet.2d.md)
+* [ggplot + flowSet + gate](vignettes/advanced/ggplot.flowSet.gate.md)
+* [ggplot + flowSet + overlay](vignettes/advanced/ggplot.flowSet.overlay.md)
+
+# quick demos of some most used features 
 
 
 ```r
 library(ggcyto)
 dataDir <- system.file("extdata",package="flowWorkspaceData")
-gs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))
-fs <- getData(gs, "CD3+")
+gs <- load_gs(list.files(dataDir, pattern = "gs_bcell_auto",full = TRUE))
+```
+
+
+
+
+```r
+#plot a gate by specifying the population node name (here it is 'CD3')
+autoplot(gs, "CD3")
 ```
 
-### Quick plot with [autoplot](vignettes/autoplot.md) 
+![](README_files/figure-html/unnamed-chunk-4-1.png)<!-- -->
 
 ```r
-#1d
-autoplot(fs, "CD4")
+#change the resolution
+p <- autoplot(gs, "CD3", bins = 64)
+p
 ```
 
-![](README_files/figure-html/unnamed-chunk-3-1.png)<!-- -->
+![](README_files/figure-html/unnamed-chunk-4-2.png)<!-- -->
 
 ```r
-#2d
-autoplot(fs, "CD4", "CD8", bins = 64)
+#display the transformed value at breaks label by turning off the inverse transform
+autoplot(gs, "CD3", axis_inverse_trans = FALSE)
 ```
 
-![](README_files/figure-html/unnamed-chunk-3-2.png)<!-- -->
+![](README_files/figure-html/unnamed-chunk-4-3.png)<!-- -->
 
 ```r
-autoplot(gs, c("CD4", "CD8"), bins = 64)
+#you can switch the limits from default `instrument` to the actual `data` range
+p + ggcyto_par_set(limits = "data")
 ```
 
-![](README_files/figure-html/unnamed-chunk-3-3.png)<!-- -->
+![](README_files/figure-html/unnamed-chunk-4-4.png)<!-- -->
 
 ```r
-#plot all channels
-autoplot(fs[[1]]) + labs_cyto("marker")
+# Choose between `marker` and `channel` names for axis label text
+p + labs_cyto("channel") #default is "both"
 ```
 
-![](README_files/figure-html/unnamed-chunk-3-4.png)<!-- -->
+![](README_files/figure-html/unnamed-chunk-4-5.png)<!-- -->
 
-### More flexibility with **ggcyto** wrapper
+```r
+# overlay another population 'IgD-CD27-' as dots on top of the existing plot
+p + geom_overlay("IgD-CD27-", alpha = 0.5, size = 0.1, color = "purple")
+```
 
-#### [ggcyto + flowSet](vignettes/ggcyto.flowSet.md)
+![](README_files/figure-html/unnamed-chunk-4-6.png)<!-- -->
 
 ```r
-# support fuzzy-matching of aes to the data
-# with flowJo-type of default color fills
-# facet on `name` by default
-ggcyto(fs,aes(x = CD4, y = CD8)) + geom_hex(bins = 64) + xlim(0, 3600)
+# plot a population without gate
+fs <- getData(gs, "CD20") #extract the gated data as a flowSet
+autoplot(fs, "CD20", "CD19") #plot 2D
 ```
 
-![](README_files/figure-html/unnamed-chunk-4-1.png)<!-- -->
+![](README_files/figure-html/unnamed-chunk-4-7.png)<!-- -->
 
-#### [ggcyto + GatingSet](vignettes/ggcyto.GatingSet.md)
 
 ```r
-ggcyto(gs,aes(x = CCR7, y = CD45RA), subset = "CD4") + geom_hex(bins = 64) + geom_gate("CD4/CCR7+ 45RA+") + geom_stats(fill = "yellow", size = 4)
+autoplot(fs, "CD20") #1d density
 ```
 
 ![](README_files/figure-html/unnamed-chunk-5-1.png)<!-- -->
 
-### Use `ggplot` directly to have more controls. 
-
 ```r
-# 1d
-p <- ggplot(fs, aes(x = `<B710-A>`)) + facet_wrap(~name) 
-#histogram plot
-p + geom_histogram(colour = "white")
+#extract one sample as a flowFrame
+fr <- fs[[1]]
+#plot 1d density on all available channels
+autoplot(fr)
 ```
 
 ![](README_files/figure-html/unnamed-chunk-6-1.png)<!-- -->
 
+ 
+
 ```r
-#density plot
-p + geom_density(fill = "black")
+gh <- gs[[1]] # extract a `GatingHierarchy` object for one sample
+# layout multiple cell populations with their asssociated gates in the same plot.
+nodes <- getNodes(gh)[c(3:9, 14)]
+p <- autoplot(gh, nodes, bins = 64)
+p
 ```
 
-![](README_files/figure-html/unnamed-chunk-6-2.png)<!-- -->
+![](README_files/figure-html/unnamed-chunk-7-1.png)<!-- -->
+
 
 ```r
-# 2d hexbin
-ggplot(fs, aes(x = `<B710-A>`, y = `<R780-A>`)) + facet_wrap(~name) + geom_hex(bins = 64)
+#arrange it as one-row gtable object
+gt <- ggcyto_arrange(p, nrow = 1)
+plot(gt)
 ```
 
-![](README_files/figure-html/unnamed-chunk-6-3.png)<!-- -->
+![](README_files/figure-html/unnamed-chunk-8-1.png)<!-- -->
+
 
-More examples of using `ggplot` directly on `flowSet`:
 
-* [ggplot + flowSet1d](vignettes/advanced/ggplot.flowSet.1d.md)
-* [ggplot + flowSet2d](vignettes/advanced/ggplot.flowSet.2d.md)
-* [ggplot + flowSet + gate](vignettes/advanced/ggplot.flowSet.gate.md)
-* [ggplot + flowSet + overlay](vignettes/advanced/ggplot.flowSet.overlay.md)
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+++ /dev/null
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-# ggcyto : Visualize `Cytometry` data with `ggplot`
-
-
-
-
-```r
-library(ggcyto)
-dataDir <- system.file("extdata",package="flowWorkspaceData")
-```
-
-## 1: suppoort `3` types of plot constructor 
-
-* represent different levels of complexity and flexibility
-* meet the needs of various plot applications
-* suitable for users at different levels of coding skills.
-
-### low level: `ggplot`
-
-The overloaded `fority` methods empower `ggplot`  to work with all the major Cytometry data structures right away, which allows users to do all kinds of highly customized and versitled plots.
-
-#### `GatingSet`
-
-```r
-gs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))
-attr(gs, "subset") <- "CD3+"
-ggplot(gs, aes(x = `<B710-A>`, y = `<R780-A>`)) + geom_hex(bins = 128) + scale_fill_gradientn(colours = gray.colors(9))
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-3-1.png)
-
-#### `flowSet/ncdfFlowSet/flowFrame` 
-
-```r
-fs <- getData(gs, "CD3+")
-ggplot(fs, aes(x = `<B710-A>`)) + geom_density(fill = "blue", alpha= 0.5)
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-4-1.png)
-
-#### `gates`
-
-```r
-gates <- filterList(getGate(gs, "CD8"))
-ggplot(gs, aes(x = `<B710-A>`, y = `<R780-A>`)) + geom_hex(bins = 128) + geom_polygon(data = gates, fill = "transparent", col = "purple")
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-5-1.png)
-
-### medium level: `ggcyto`
-
-`ggcyto` constructor along with overloaded `+` operator encapsulate lots of details that might be tedious and intimidating for many users.
-
-
-```r
-ggcyto(gs, aes(x = CD4, y = CD8)) + geom_hex(bins = 128) + geom_gate("CD8")
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-6-1.png)
-
-It simplies the plotting by:
-* add a default scale_fill_gradientn for you
-* fuzzy-matching in `aes` by either detector or fluorochromes names
-* determine the `parent` popoulation automatically
-* exact and plot the gate object by simply referring to the `child` population name
-
-### top level: `autoplot`
-Inheriting the spirit from ggplot's `Quick plot`, it further simply the plotting job by hiding more details from users and taking more assumptions for the plot.
-
-* when plotting `flowSet`, it determines `geom` type automatically by the number of `dim` supplied 
-* for `GatingSet`, it further skip the need of `dim` by guessing it from the `children` gate
-
-
-```r
-#1d
-autoplot(fs, "CD4")
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-7-1.png)
-
-```r
-#2d
-autoplot(fs, "CD4", "CD8", bins = 64)
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-7-2.png)
-
-```r
-autoplot(gs, c("CD4", "CD8"), bins = 64)
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-7-3.png)
-
-## 2: in-line transformation 
-It is done by different `scales` layers speically designed for `cytometry`
-
-```r
-data(GvHD)
-fr <- GvHD[[1]]
-p <- autoplot(fr, "FL1-H")
-p #raw scale
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-8-1.png)
-
-```r
-p + scale_x_logicle() #flowCore logicle scale
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-8-2.png)
-
-```r
-p + scale_x_flowJo_fasinh() # flowJo fasinh
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-8-3.png)
-
-```r
-p + scale_x_flowJo_biexp() # flowJo biexponential
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-8-4.png)
-
-## 3: generic `geom_gate` layer 
-It hides the complex details pf plotting different geometric shapes
-
-
-```r
-fr <- fs[[1]]
-p <- autoplot(fr,"CD4", "CD8") + ggcyto_par_set(limits = "instrument")
-#1d gate vertical
-gate_1d_v <- openCyto::gate_mindensity(fr, "<B710-A>")
-p + geom_gate(gate_1d_v)
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-9-1.png)
-
-```r
-#1d gate horizontal
-gate_1d_h <- openCyto::gate_mindensity(fr, "<R780-A>")
-p + geom_gate(gate_1d_h)
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-9-2.png)
-
-```r
-#2d rectangle gate
-gate_rect <- rectangleGate("<B710-A>" = c(gate_1d_v@min, 4e3), "<R780-A>" = c(gate_1d_h@min, 4e3))
-p + geom_gate(gate_rect)
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-9-3.png)
-
-```r
-#ellipsoid Gate
-gate_ellip <- getGate(gs[[1]], "CD4")
-class(gate_ellip)
-```
-
-```
-## [1] "ellipsoidGate"
-## attr(,"package")
-## [1] "flowCore"
-```
-
-```r
-p + geom_gate(gate_ellip)
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-9-4.png)
-
-
-
-## 4: `geom_stats` 
-
-```r
-p <- ggcyto(gs, aes(x = "CD4", y = "CD8"), subset = "CD3+") + geom_hex()
-p + geom_gate("CD4") + geom_stats()
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-10-1.png)
-
-```r
-p + geom_gate("CD4") + geom_stats(type = "count") #display cell counts 
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-10-2.png)
-
-
-## 5: `axis_inverse_trans` 
-It can display the `log` scaled data in the original value
-
-```r
-p # axis display the transformed values
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-11-1.png)
-
-```r
-p + axis_x_inverse_trans() # restore the x axis to the raw values
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-11-2.png)
-
-It currently only works with `GatingSet`.
-
-## 6: auto limits
-Optionally you can set limits by `instrument` or `data` range
-
-```r
-p <- p + ggcyto_par_set(limits = "instrument")
-p
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-12-1.png)
-
-## 7: labs_cyto
-You can choose between `marker` and `channel` names (or `both` by default)
-
-```r
-p + labs_cyto("markers")
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-13-1.png)
-
-
-## 8: `ggcyto_par_set` 
-It aggregates the different settings in one layer
-
-```r
-#put all the customized settings in one layer
-mySettings <- ggcyto_par_set(limits = "instrument"
-                             , facet = facet_wrap("name")
-                             , hex_fill = scale_fill_gradientn(colours = rev(RColorBrewer::brewer.pal(11, "Spectral")))
-                            , lab = labs_cyto("marker")
-                            )
-# and use it repeatly in the plots later (similar to the `theme` concept)
-p + mySettings
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-14-1.png)
-
-Currently we only support `4` settings, but will add more in future.
-
-## 9: `as.ggplot` 
-It allows user to convert `ggcyto` objects to pure `ggplot` objects for further the manipulating jobs that can not be done within `ggcyto` framework.
-
-```r
-class(p) # may not fully compatile with all the `ggplot` functions
-```
-
-```
-## [1] "ggcyto_GatingSet"
-## attr(,"package")
-## [1] "ggcyto"
-```
-
-```r
-p1 <- as.ggplot(p)
-class(p1) # a pure ggplot object, thus can work with all the `ggplot` features
-```
-
-```
-## [1] "gg"     "ggplot"
-```
-
-## 10: ggcyto_layout
-
-Layout many gate plots on the same page
-
-When plooting a `GatingHierarchy`, multiple cell populations with their asssociated gates can be plotted in different panels of the same plot.
-
-```r
-gh <- gs[[1]]
-nodes <- getNodes(gh, path = "auto")[c(3:9, 14)]
-nodes
-```
-
-```
-## [1] "singlets"    "CD3+"        "CD4"         "CD4/38- DR+" "CD4/38+ DR+"
-## [6] "CD4/38+ DR-" "CD4/38- DR-" "CD8"
-```
-
-```r
-autoplot(gh, nodes, bins = 64)
-```
-
-![](Top_features_of_ggcyto_files/figure-html/unnamed-chunk-16-1.png)
-
-
-More examples:
-
-* [ggplot + flowSet1d](vignettes/advanced/ggplot.flowSet.1d.md)
-* [ggplot + flowSet2d](vignettes/advanced/ggplot.flowSet.2d.md)
-* [ggplot + flowSet + gate](vignettes/advanced/ggplot.flowSet.gate.md)
-* [ggplot + flowSet + overlay](vignettes/advanced/ggplot.flowSet.overlay.md)
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