Fixing user guide

mihuzx · Sep 19, 2023 · 06e67a6 · 06e67a6
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diff --git a/ggPlantmap.userguide.Rmd b/ggPlantmap.userguide.Rmd
@@ -19,7 +19,7 @@ ggPlantmap is an open-source R package with the goal of facilitating the generat
 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.
 
 
-## 1. Installing ggPlant map
+## 1. Installing ggPlantmap
 
 ```{r, eval = FALSE}
 ## ggPlantmap installation requires devtools.
@@ -176,7 +176,9 @@ ggPlantmap.plot(ggPm.At.leaf.topview,show.legend = F) +
 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 />
-<b>IMPORTANT!!!!
+
+<b>
+### IMPORTANT!!!!
 
 Note that the name of the ROI levels on your map should exactly match the ones on your quantitative data!!!</b>
 
@@ -219,7 +221,7 @@ ggPlantmap.heatmap(quant.data,AT5G47670.expression) +
 
 ### 4 Create your own ggPlantmap
 Hopefully by now you should be a little familiar with the package.
-But we encourage you to create your own ggPlantmap.
+But I encourage you to create your own ggPlantmap.
 To do so, we will require a specific open-source imaging software called Icy (https://icy.bioimageanalysis.org/).
 Source image: https://www.mdpi.com/ijms/ijms-16-13989/article_deploy/html/images/ijms-16-13989-g007.png
 

diff --git a/ggPlantmap.userguide.md b/ggPlantmap.userguide.md
@@ -19,7 +19,7 @@ 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.
 
-## 1. Installing ggPlant map
+## 1. Installing ggPlantmap
 
 ``` r
 ## ggPlantmap installation requires devtools.
@@ -316,7 +316,8 @@ 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 />
-<b>IMPORTANT!!!!
+
+<b> \#\#\# IMPORTANT!!!!
 
 Note that the name of the ROI levels on your map should exactly match
 the ones on your quantitative data!!!</b>
@@ -433,9 +434,9 @@ ggPlantmap.heatmap(quant.data,AT5G47670.expression) +
 
 ### 4 Create your own ggPlantmap
 
-Hopefully by now you should be a little familiar with the package. But
-we encourage you to create your own ggPlantmap. To do so, we will
-require a specific open-source imaging software called Icy
+Hopefully by now you should be a little familiar with the package. But I
+encourage you to create your own ggPlantmap. To do so, we will require a
+specific open-source imaging software called Icy
 (<https://icy.bioimageanalysis.org/>). Source image:
 <https://www.mdpi.com/ijms/ijms-16-13989/article_deploy/html/images/ijms-16-13989-g007.png>