Update on readme file

mihuzx · Sep 14, 2023 · 5f11b85 · 5f11b85
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diff --git a/.Rproj.user/02275A6F/sources/prop/FDB464DC b/.Rproj.user/02275A6F/sources/prop/FDB464DC
@@ -1,6 +1,6 @@
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diff --git a/README.Rmd b/README.Rmd
@@ -20,9 +20,12 @@ knitr::opts_chunk$set(
   ggPlantmap is an open-source R package with the goal of facilitating the generation of informative ggplot maps from plant images to explore quantitative cell-type specific data. When combined with external quantitative data, ggPlantmap can be used for the visualization and displaying of spatial profiles in distinct parts/cells of the plant (Figure 1). The conceptual workflow is like other ggplot based geographic map packages. Included in the package there is a set of pre-loaded maps created from previously published plant images that can be directly inserted into a ggplot coding workflow. ggPlantmap enables users to plot heatmap signatures of gene expression or any spatial quantitative data onto plant images providing a customizable and extensible platform for visualizing, and analyzing spatial quantitative patterns within specific plant regions. This package uses the flexibility of the well-known ggplot2 R package to allow users to tailor maps to their specific research questions.
 <img src="man/figures/ggPlantmap.example1.jpg" align="center" height="250"/>
 
-## ggPlantmap User Guide
+## ggPlantmap Fuides
+
+We  created a step-by-step[user guide](https://github.com/leonardojo/ggPlantmap/blob/main/ggPlantmap.userguide.md) to help users navigate through the package.
+We also created a  [step-by-step guide with tips](https://github.com/leonardojo/ggPlantmap/blob/main/Tutorial%20for%20XML%20file.pdf) on how to create your ggPlantmap.
+
 Below you can find general instructions on how to navigate through ggPlantmap.
-We also created a step-by-step [user guide](https://github.com/leonardojo/ggPlantmap/blob/main/ggPlantmap.userguide.md) to help you get familiar with the package.
 
 ## Installation
 
@@ -121,8 +124,6 @@ c <- ggPlantmap.plot(ggPm.At.seed.devseries,Part,linewidth = 0.5) +
 plot_grid(a,b,c,ncol=1,labels=c("a","b","c"),align = "v")
 ```
 
-Each map will have their own classification. If you would like to adjust or create your own classification, you can save the ggPlantmap as a table and modify it on to mach the degree of separation you want to show.
-
 ## How can I overlay quantitative data into my ggPlantmap?
 
   With ggPlantmap you can overlay quantitative data into your ggPlantmap to visualize it as sort of a heatmap.
@@ -149,7 +150,7 @@ ggPlantmap.heatmap(expression.sample2,SCR.expression) +
 ## How can I create my own ggPlantmap?
 
 The principle of creating a ggPlantmap is fairly simple. We generate a list of ROIs (region of interests) in the Icy open-source software (https://icy.bioimageanalysis.org/) from any image. These ROIs are saved as XML files and later be converted into ggPlantmaps by using the function XML.to.ggPlantmap() function. 
-We created step-by-step guide with tips. on how to generate xml images from plant images.
+[We created step-by-step guide with tips](https://github.com/leonardojo/ggPlantmap/blob/main/Tutorial%20for%20XML%20file.pdf). on how to generate xml images from plant images.
 
 ```{r}
 ##converting the sample file: ggPm.sample.xml into a ggPlantmap table
@@ -171,7 +172,6 @@ Not at all. ggPlantmap can also be used to produce many other type of plots. Ess
 
 YES!!! Any Plant map can be included in the package. If you create one, please email me ([email protected]) your ggPlantmap as tab-delimited table and I'll make sure to include in the package. You will be credited and your information will be displayed in the summary file. I really hope this becomes an organic package with the contribution of the plant research community.
 
-
-## Ackowledgements
-
-Soon
+## Acknowledgements
+I would like to acknowledge Kaisa Kajala, Lisa Oskam, Monica Garcia Gomez, Pierre Gautrat for testing ggPlantmap before launch. I also would like to acknowledge Andres Romanowski for providing some data for the initial tests of ggPlantmap.
+
diff --git a/README.md b/README.md
@@ -25,12 +25,17 @@ flexibility of the well-known ggplot2 R package to allow users to tailor
 maps to their specific research questions.
 <img src="man/figures/ggPlantmap.example1.jpg" align="center" height="250"/>
 
-## ggPlantmap User Guide
+## ggPlantmap Fuides
 
-Below you can find general instructions on how to navigate through
-ggPlantmap. We also created a step-by-step [user
+We created a step-by-step[user
 guide](https://github.com/leonardojo/ggPlantmap/blob/main/ggPlantmap.userguide.md)
-to help you get familiar with the package.
+to help users navigate through the package. We also created a
+[step-by-step guide with
+tips](https://github.com/leonardojo/ggPlantmap/blob/main/Tutorial%20for%20XML%20file.pdf)
+on how to create your ggPlantmap.
+
+Below you can find general instructions on how to navigate through
+ggPlantmap.
 
 ## Installation
 
@@ -192,17 +197,47 @@ ggplot(ggPm.At.roottip.longitudinal,aes(x,y)) +
 Because each polygon on ggPlantmap is characterized by specific levels
 (examples: Region,Stage,Part), you can color map them individually.
 Using ggPlantmap, you can color map based on unique layers of the
-ggPlantmap.
+ggPlantmap. Just specify the column you want to colormap in the layer
+option of the function. ggPlantmap.plot() is based on ggplot so you can
+add specific modifications to it using ggplot coding logic.
 
-    #> # A tibble: 6 × 8
-    #>   ROI.name                           Stage Part  Region ROI.id point     x     y
-    #>   <chr>                              <chr> <chr> <chr>   <int> <int> <dbl> <dbl>
-    #> 1 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     1  277. -693.
-    #> 2 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     2  280. -689.
-    #> 3 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     3  280. -685.
-    #> 4 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     4  285. -681.
-    #> 5 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     5  286. -675.
-    #> 6 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     6  286. -669.
+``` r
+library(ggplot2)
+library(cowplot)
+
+head(ggPm.At.seed.devseries)
+#> # A tibble: 6 × 8
+#>   ROI.name                           Stage Part  Region ROI.id point     x     y
+#>   <chr>                              <chr> <chr> <chr>   <int> <int> <dbl> <dbl>
+#> 1 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     1  277. -693.
+#> 2 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     2  280. -689.
+#> 3 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     3  280. -685.
+#> 4 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     4  285. -681.
+#> 5 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     5  286. -675.
+#> 6 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista…      1     6  286. -669.
+
+## Stage: Seed development stage
+## Part: Distinct parts of a seed (Seed coat, Endosperm and Embryo)
+## Region: Specific regions of each part of the Arabidopsis seed
+## Reference: Belmonte, Mark F., et al. "Comprehensive developmental profiles of gene activity in regions and subregions of the Arabidopsis seed." Proceedings of the National Academy of Sciences 110.5 (2013): E435-E444.
+
+a <- ggPlantmap.plot(ggPm.At.seed.devseries,Region,linewidth = 0.5) +
+  scale_fill_brewer(palette="Set3") +
+  ggtitle("Regions of Arabidopsis seed development") +
+   theme(legend.key.height= unit(0.25, 'cm'),
+        legend.key.width= unit(0.25, 'cm'))
+b <- ggPlantmap.plot(ggPm.At.seed.devseries,Stage,linewidth = 0.5) +
+  scale_fill_brewer(palette="Set1") +
+  ggtitle("Stages of Arabidopsis seed development") +
+   theme(legend.key.height= unit(0.25, 'cm'),
+        legend.key.width= unit(0.25, 'cm'))
+c <- ggPlantmap.plot(ggPm.At.seed.devseries,Part,linewidth = 0.5) +
+  scale_fill_brewer(palette="Set1") +
+  ggtitle("Parts of Arabidopsis seed development") +
+   theme(legend.key.height= unit(0.25, 'cm'),
+        legend.key.width= unit(0.25, 'cm'))
+plot_grid(a,b,c,ncol=1,labels=c("a","b","c"),align = "v")
+```
 
 ![](man/figures/README-unnamed-chunk-8-1.png)<!-- -->
 
@@ -211,15 +246,64 @@ or create your own classification, you can save the ggPlantmap as a
 table and modify it on to mach the degree of separation you want to
 show.
 
+## How can I overlay quantitative data into my ggPlantmap?
+
+With ggPlantmap you can overlay quantitative data into your ggPlantmap
+to visualize it as sort of a heatmap. To do so, you will need another
+table with contains quantitative data attributed to your ROIs.
+<img src="man/figures/guide/Slide7.JPG" align="center" width="1200"/><br />
+
+WiYou can combine the ggPlantmap with a external quantitative data using
+the ggPlantmap.merge() function. later, the heatmap can be generated
+using the ggPlantmap.heatmap() function.This approach can be very
+helpful for R Shiny app developers to create web interactive tools to
+visualize gene expression gene profiles.
+
+``` r
+##Quantitative sample data, the expression of SCR in distinct cell-types of the Tomato root.
+## Adapted data from: Kajala, Kaisa, et al. "Innovation, conservation, and repurposing of gene function in root cell type development." Cell 184.12 (2021): 3333-3348.
+head(ggPm.tomatoatlas.expression.sample)
+#> # A tibble: 6 × 2
+#>   Cell.layer SCR.expression
+#>   <chr>               <dbl>
+#> 1 Epidermis            1.24
+#> 2 Cortex               1.17
+#> 3 Endodermis          75.8 
+#> 4 Phloem               0.44
+#> 5 Procambium           0.95
+#> 6 Pericycle            0.95
+##important: Names in the quantitative data needs to match the ones found in the map.
+
+##Merging both datasets
+expression.sample2 <- ggPlantmap.merge(ggPm.Sl.root.crosssection,ggPm.tomatoatlas.expression.sample,id.x = "ROI.name",id.y="Cell.layer") ##Column names between tables are different, need to specify both identifiers in x and y.
+head(expression.sample2)
+#> # A tibble: 6 × 6
+#>   ROI.name  ROI.id point     x     y SCR.expression
+#>   <chr>      <int> <int> <dbl> <dbl>          <dbl>
+#> 1 Exodermis      1     1  615. -370.             NA
+#> 2 Exodermis      1     2  601. -349.             NA
+#> 3 Exodermis      1     3  598. -327.             NA
+#> 4 Exodermis      1     4  617. -312.             NA
+#> 5 Exodermis      1     5  636. -307.             NA
+#> 6 Exodermis      1     6  651. -310.             NA
+
+##Ploting
+ggPlantmap.heatmap(expression.sample2,SCR.expression) +
+  scale_fill_gradient(low="white",high="Red",na.value ="white")
+```
+
+![](man/figures/README-unnamed-chunk-9-1.png)<!-- -->
+
 ## How can I create my own ggPlantmap?
 
 The principle of creating a ggPlantmap is fairly simple. We generate a
 list of ROIs (region of interests) in the Icy open-source software
 (<https://icy.bioimageanalysis.org/>) from any image. These ROIs are
 saved as XML files and later be converted into ggPlantmaps by using the
-function XML.to.ggPlantmap() function. We created step-by-step tutorial
-on how to generate xml images from plant images, you can find the
-tutorial here.
+function XML.to.ggPlantmap() function. [We created step-by-step guide
+with
+tips](https://github.com/leonardojo/ggPlantmap/blob/main/Tutorial%20for%20XML%20file.pdf).
+on how to generate xml images from plant images.
 
 ``` r
 ##converting the sample file: ggPm.sample.xml into a ggPlantmap table
@@ -238,37 +322,6 @@ head(ggPm)
 ggPlantmap.plot(ggPm)
 ```
 
-![](man/figures/README-unnamed-chunk-9-1.png)<!-- -->
-
-## How can I overlay quantitative data into my ggPlantmap?
-
-With ggPlantmap you can overlay quantitative data into your ggPlantmap
-to visualize it as sort of a heatmap. To do so, you will need another
-table with contains quantitative data attributed to your ROIs.
-<img src="man/figures/guide/Slide7.JPG" align="center" width="1200"/><br />
-
-This approach can be very helpful for R Shiny app developers to create
-web interactive tools to visualize gene expression gene profiles.
-
-    #> # A tibble: 6 × 2
-    #>   Cell.layer SCR.expression
-    #>   <chr>               <dbl>
-    #> 1 Epidermis            1.24
-    #> 2 Cortex               1.17
-    #> 3 Endodermis          75.8 
-    #> 4 Phloem               0.44
-    #> 5 Procambium           0.95
-    #> 6 Pericycle            0.95
-    #> # A tibble: 6 × 6
-    #>   ROI.name  ROI.id point     x     y SCR.expression
-    #>   <chr>      <int> <int> <dbl> <dbl>          <dbl>
-    #> 1 Exodermis      1     1  615. -370.             NA
-    #> 2 Exodermis      1     2  601. -349.             NA
-    #> 3 Exodermis      1     3  598. -327.             NA
-    #> 4 Exodermis      1     4  617. -312.             NA
-    #> 5 Exodermis      1     5  636. -307.             NA
-    #> 6 Exodermis      1     6  651. -310.             NA
-
 ![](man/figures/README-unnamed-chunk-10-1.png)<!-- -->
 
 ## Is ggPlantmap only usefull for molecular expression data?
@@ -289,6 +342,9 @@ your information will be displayed in the summary file. I really hope
 this becomes an organic package with the contribution of the plant
 research community.
 
-## Ackowledgements
+## Acknowledgements
 
-Soon
+I would like to acknowledge Kaisa Kajala, Lisa Oskam, Monica Garcia
+Gomez, Pierre Gautrat for testing ggPlantmap before launch. I also would
+like to acknowledge Andres Romanowski for providing some data for the
+initial tests of ggPlantmap.