Browse code

Merge branch 'devel'

Keniajin authored on 11/12/2022 19:50:44
Showing 44 changed files

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@@ -1,7 +1,7 @@
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 Package: protGear
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 Type: Package
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 Title: Protein Micro Array Data Management and Interactive Visualization
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-Version: 1.3.3
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+Version: 1.3.31
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 Authors@R: c(person("Kennedy", "Mwai", role = c("cre", "aut"), email = "keniajin@gmail.com"),
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              person(c("James", "Mburu"), role = "aut" , email = "mburuwanja@gmail.com") ,
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              person(c("Jacqueline", "Waeni"), role = "ctb" , email = "jacqwaeni@gmail.com"))
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@@ -16,7 +16,7 @@ Imports:
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     ggpubr (>= 0.4.0), gtools (>= 3.8.2) , tibble (>= 3.1.0) ,  rmarkdown (>= 2.9) , knitr (>= 1.33),
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     utils (>= 3.6), genefilter (>= 1.74.0), readr (>= 2.0.1) , Biobase (>= 2.52.0), 
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     plyr (>= 1.8.6) , Kendall (>= 2.2)  , shiny (>= 1.0.0) ,  purrr (>= 0.3.4),  plotly (>= 4.9.0) , MASS (>= 7.3) , 
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-    htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2) , shinydashboard (>= 0.7.1) , kableExtra (>= 1.3.4), 
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+    htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2) , shinydashboard (>= 0.7.1) , 
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     GGally (>= 2.1.2) , pheatmap (>= 1.0.12) ,  grid(>= 4.1.1),
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      styler (>= 1.6.1) , factoextra (>= 1.0.7) ,FactoMineR (>= 2.4) , rlang (>= 0.4.11),
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      remotes (>= 2.4.0) 
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@@ -27,5 +27,5 @@ Suggests:
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 biocViews: Microarray, OneChannel, Preprocessing , BiomedicalInformatics , Proteomics , BatchEffect, Normalization , Bayesian, Clustering, Regression,SystemsBiology, ImmunoOncology
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 Encoding: UTF-8
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 LazyData: false
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-RoxygenNote: 7.2.2
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+RoxygenNote: 7.2.3
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 VignetteBuilder: knitr
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@@ -64,7 +64,6 @@ importFrom(flexdashboard,valueBoxOutput)
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 importFrom(genefilter,rowSds)
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 importFrom(ggplot2,ggplot)
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 importFrom(gtools,mixedsort)
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-importFrom(kableExtra,text_spec)
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 importFrom(magrittr,"%>%")
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 importFrom(plotly,ggplotly)
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 importFrom(plyr,.)
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@@ -37,3 +37,9 @@
37 37
  + Updated shiny app to load
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  + Added missing paths for launch_protGear_interactive 
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+
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+## protGear 1.3.33 (2022-12-11) 
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+
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+ + Removed kableExtra due to build error 
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+
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+
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@@ -7,7 +7,6 @@
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 #' @importFrom flexdashboard renderValueBox valueBoxOutput valueBox
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 #' @importFrom shinydashboard renderInfoBox
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 #' @importFrom  dplyr group_rows between first last
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-#' @importFrom  kableExtra text_spec
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 #' @export
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 #' @examples
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 #' app <- system.file("shiny-examples", "protGear_interactive",
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@@ -32,7 +32,7 @@
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       </button>
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       <span class="navbar-brand">
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         <a class="navbar-link" href="index.html">protGear</a>
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-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
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+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
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       </span>
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     </div>
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@@ -17,7 +17,7 @@
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       </button>
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       <span class="navbar-brand">
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         <a class="navbar-link" href="../index.html">protGear</a>
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-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
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+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
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       </span>
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     </div>
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@@ -33,7 +33,7 @@
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       </button>
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       <span class="navbar-brand">
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         <a class="navbar-link" href="../index.html">protGear</a>
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-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
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+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
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       </span>
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     </div>
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@@ -82,7 +82,7 @@
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                         <h4 data-toc-skip class="author">Kennedy
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 Mwai</h4>
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-            <h4 data-toc-skip class="date">27 November, 2022</h4>
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+            <h4 data-toc-skip class="date">11 December, 2022</h4>
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       <small class="dont-index">Source: <a href="https://github.com/Keniajin/protGear/blob/HEAD/../vignettes/vignette.Rmd" class="external-link"><code>../vignettes/vignette.Rmd</code></a></small>
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       <div class="hidden name"><code>vignette.Rmd</code></div>
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@@ -98,7 +98,6 @@ Mwai</h4>
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 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://scales.r-lib.org" class="external-link">scales</a></span><span class="op">)</span></span>
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 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">pheatmap</span><span class="op">)</span></span>
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 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-datatable.com" class="external-link">data.table</a></span><span class="op">)</span></span>
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-<span><span class="fu"><a href="https://rdrr.io/r/base/warning.html" class="external-link">suppressWarnings</a></span><span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://haozhu233.github.io/kableExtra/" class="external-link">kableExtra</a></span><span class="op">)</span><span class="op">)</span> <span class="co">## kable extra casusing warnings</span></span>
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 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">gridExtra</span><span class="op">)</span></span>
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 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://www.rforge.net/png/" class="external-link">png</a></span><span class="op">)</span></span>
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 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
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@@ -116,7 +115,24 @@ Mwai</h4>
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 <span><span class="fu">knitr</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/knitr/man/opts_chunk.html" class="external-link">opts_chunk</a></span><span class="op">$</span><span class="fu">set</span><span class="op">(</span>echo <span class="op">=</span> <span class="cn">TRUE</span>, message<span class="op">=</span><span class="cn">FALSE</span>,warning <span class="op">=</span> <span class="cn">FALSE</span>,</span>
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 <span>                      fig.align <span class="op">=</span> <span class="st">'center'</span>,</span>
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 <span>                      dev <span class="op">=</span> <span class="st">"png"</span>, </span>
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-<span>                       tidy<span class="op">=</span><span class="st">'styler'</span>, tidy.opts<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>strict<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
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+<span>                       tidy<span class="op">=</span><span class="st">'styler'</span>, tidy.opts<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>strict<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
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+<span></span>
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+<span></span>
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+<span><span class="co">## Color Format</span></span>
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+<span><span class="va">colFmt</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>,<span class="va">color</span><span class="op">)</span> <span class="op">{</span></span>
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+<span>  </span>
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+<span>  <span class="va">outputFormat</span> <span class="op">&lt;-</span> <span class="fu">knitr</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/knitr/man/opts_knit.html" class="external-link">opts_knit</a></span><span class="op">$</span><span class="fu">get</span><span class="op">(</span><span class="st">"rmarkdown.pandoc.to"</span><span class="op">)</span></span>
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+<span>  </span>
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+<span>  <span class="kw">if</span><span class="op">(</span><span class="va">outputFormat</span> <span class="op">==</span> <span class="st">'latex'</span><span class="op">)</span> <span class="op">{</span></span>
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+<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"\\textcolor{"</span>,<span class="va">color</span>,<span class="st">"}{"</span>,<span class="va">x</span>,<span class="st">"}"</span>,sep<span class="op">=</span><span class="st">""</span><span class="op">)</span></span>
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+<span>  <span class="op">}</span> <span class="kw">else</span> <span class="kw">if</span><span class="op">(</span><span class="va">outputFormat</span> <span class="op">==</span> <span class="st">'html'</span><span class="op">)</span> <span class="op">{</span></span>
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+<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"&lt;font color='"</span>,<span class="va">color</span>,<span class="st">"'&gt;"</span>,<span class="va">x</span>,<span class="st">"&lt;/font&gt;"</span>,sep<span class="op">=</span><span class="st">""</span><span class="op">)</span></span>
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+<span>  <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
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+<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="va">x</span></span>
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+<span>  <span class="op">}</span></span>
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+<span></span>
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+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">ret</span><span class="op">)</span></span>
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+<span><span class="op">}</span></span></code></pre></div>
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 <style type="text/css">
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 .custom-inline {
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   color: red;
... ...
@@ -257,8 +273,8 @@ from the quantification software under <code>Wavelengths</code>.</p>
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 <span><span class="co">## [30] "ScanArea=1\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"                                                                                          </span></span>
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 <span><span class="co">## [31] "Supplier=\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"                                                                                           </span></span>
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 <span><span class="co">## [32] "Flags\tNormalize\tAutoflag\tBlock\tColumn\tRow\tName\tID\tX\tY\tF635 Median\tB635\tB635 Median\tSNR 635\tF635 Median - B635\tIndex"</span></span></code></pre>
260
-<p>The function <span style="     color: red !important;">check_sampleID_files()</span> helps
261
-to check whether each micro array file has a corresponding sample
276
+<p>The function <font color="red">check_sampleID_files()</font> helps to
277
+check whether each micro array file has a corresponding sample
262 278
 identifier file. The sample identifier files are generated from the lab
263 279
 plate maps to match the corresponding samples on a specific slide.If the
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 sample identifier file is missing, protGear automatically generates the
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@@ -270,13 +286,16 @@ id’s.</p>
270 286
 strong spatial biases when the scan image is not available. However, we
271 287
 recommend using the scanned image to visualize the spatial artefacts
272 288
 that might not be recorded in the <code>.gpr</code> file. We include the
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-functions <span style="     color: red !important;">visualize_slide()</span> and <span style="     color: red !important;">visualize_slide_2d()</span> to check
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-the spatial structure. The functions are build on <span style="     color: red !important;">structure_plot()</span> which shows
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-the block and mini-array structure of a slide.</p>
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+functions <font color="red">visualize_slide()</font> and
290
+<font color="red">visualize_slide_2d()</font> to check the spatial
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+structure. The functions are build on
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+<font color="red">structure_plot()</font> which shows the block and
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+mini-array structure of a slide.</p>
276 294
 <div class="section level4">
277 295
 <h4 id="visualize-the-foreground-mfi">Visualize the foreground MFI<a class="anchor" aria-label="anchor" href="#visualize-the-foreground-mfi"></a>
278 296
 </h4>
279
-<p>Here we visualize the foreground MFI using the <span style="     color: red !important;">visualize_slide</span> function</p>
297
+<p>Here we visualize the foreground MFI using the
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+<font color="red">visualize_slide</font> function</p>
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 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
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 <code class="sourceCode R"><span><span class="fu"><a href="../reference/visualize_slide.html">visualize_slide</a></span><span class="op">(</span></span>
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 <span>  infile <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span><span class="st">"extdata/array_data/machine1/KK2-06.txt"</span>, package <span class="op">=</span> <span class="st">"protGear"</span><span class="op">)</span>,</span>
... ...
@@ -300,19 +319,21 @@ the block and mini-array structure of a slide.</p>
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 <div class="section level3">
301 320
 <h3 id="import--gprtxt-data">Import .gpr/txt data<a class="anchor" aria-label="anchor" href="#import--gprtxt-data"></a>
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 </h3>
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-<p>Microarray data is imported using the <span style="     color: red !important;">read_array_files()</span> function.
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-The function accepts the following mandatory arguments;</p>
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+<p>Microarray data is imported using the
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+<font color="red">read_array_files()</font> function. The function
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+accepts the following mandatory arguments;</p>
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 <ul>
306 326
 <li>
307 327
 <code>filename</code> - the name of the file which the data are to
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 be read from. In this example a list of multiple files from a folder is
309
-used and passed to using <span style="     color: red !important;">purrr</span>.</li>
329
+used and passed to <font color="red">read_array_files()</font> using
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+<font color="red">purrr</font>.</li>
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 <li>
311 332
 <code>data_path</code> - The path where the file with the data is
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 located</li>
313 334
 <li>
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 <code>genepix_vars</code> - A list of specific definitions of the
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-experiment design. See <span style="     color: red !important;">array_vars()</span>
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+experiment design. See <font color="red">array_vars()</font>
316 337
 </li>
317 338
 </ul>
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 <p>For this example I use the sub-folder 1 specified using
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@@ -357,12 +378,14 @@ approaches;</p>
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 <p>In ‘<code>.gpr</code>’ files the Background column starts with a
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 ‘<code>B</code>’ followed by the wavelength or channel. In order to
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 perform background correction, we extract the background mean
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-fluorescent intensities (MFI’s) using the function <span style="     color: red !important;">extract_bg()</span> . The function
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-accepts the arguments <code>iden</code> which is the file identifier,
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+fluorescent intensities (MFI’s) using the function
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+<font color="red">extract_bg()</font> . The function accepts the
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+arguments <code>iden</code> which is the file identifier,
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 <code>data_files</code> a list of data objects with names utilised by
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-<code>iden</code> and <code>genepix_vars</code> defined using <span style="     color: red !important;">array_vars()</span> function. We
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-utilise the <code>purr::map</code> function to extract the background
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-MFI of multiple data files.</p>
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+<code>iden</code> and <code>genepix_vars</code> defined using
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+<font color="red">array_vars()</font> function. We utilise the
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+<code>purr::map</code> function to extract the background MFI of
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+multiple data files.</p>
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 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
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 <code class="sourceCode R"><span><span class="co">## utilising the map package we process a number of files  under data_files list</span></span>
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 <span><span class="va">dfs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">data_files</span><span class="op">)</span></span>
... ...
@@ -374,11 +397,10 @@ MFI of multiple data files.</p>
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 </h3>
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 <p>Before selecting the best background correction approach the MFI
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 signals are be inspected visually. In protGear we first utilise the
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-function <span style="     color: red !important;">plot_FB()</span> that
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-graphs the background, <em>BG_Median</em> and foreground values,
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-<em>FBG_Median</em>. On the protGear Shiny platform the visuals are
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-interactive and you can identify the features or blocks with strong
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-bias.</p>
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+function <font color="red">plot_FB()</font> that graphs the background,
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+<em>BG_Median</em> and foreground values, <em>FBG_Median</em>. On the
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+protGear Shiny platform the visuals are interactive and you can identify
403
+the features or blocks with strong bias.</p>
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 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
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 <code class="sourceCode R"><span><span class="va">p1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/plot_FB.html">plot_FB</a></span><span class="op">(</span><span class="va">allData_bg</span>,</span>
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 <span>  antigen_name <span class="op">=</span> <span class="st">"antigen"</span>,</span>
... ...
@@ -405,13 +427,13 @@ bias.</p>
405 427
 </h3>
406 428
 <p>After background visualization and selecting the best approach the
407 429
 array data are merged with their specific sample identifier files.</p>
408
-<p><em><span style="     color: red !important;">Note:</span></em> Each
409
-array file must have its own corresponding sample identifier
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-<code>.csv</code> file stored in <span style="     color: red !important;">array_vars()</span> function under
430
+<p><em><font color="red">Note:</font></em> Each array file must have its
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+own corresponding sample identifier <code>.csv</code> file stored in
432
+<font color="red">array_vars()</font> function under
411 433
 <code>sampleID_path</code>. Check General information section.</p>
412 434
 <p>The method of background subtraction selected is specified under
413
-<span style="     color: red !important;">method</span> below. The
414
-background correction is performed by <span style="     color: red !important;">bg_correct()</span> function.</p>
435
+<font color="red">method</font> below. The background correction is
436
+performed by <font color="red">bg_correct()</font> function.</p>
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 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
416 438
 <code class="sourceCode R"><span><span class="va">sample_ID_merged_dfs</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/map.html" class="external-link">map</a></span><span class="op">(</span></span>
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 <span>  .x <span class="op">=</span> <span class="va">dfs</span>, .f <span class="op">=</span> <span class="va">merge_sampleID</span>, data_files <span class="op">=</span> <span class="va">data_files</span>,</span>
... ...
@@ -428,8 +450,8 @@ included. Buffer spots are used to check for unexpected scanning
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 artefacts. The buffer spots should have similar values in different
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 slides. Some outliers are expected for buffer spots close sample spots
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 or landmark. However you can specify the name of your control antigens
431
-here <span style="     color: blue !important;">buffer_spot="buffer"</span> if you
432
-do not have buffer spots.</p>
453
+here <font color="blue">buffer_spot=“buffer”</font> if you do not have
454
+buffer spots.</p>
433 455
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
434 456
 <code class="sourceCode R"><span><span class="va">buffer_transp</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/map.html" class="external-link">map</a></span><span class="op">(</span>.x <span class="op">=</span> <span class="va">sample_ID_merged_dfs</span>, .f <span class="op">=</span> <span class="va">buffer_spots</span>, buffer_spot <span class="op">=</span> <span class="st">"buffer"</span><span class="op">)</span></span>
435 457
 <span></span>
... ...
@@ -442,8 +464,9 @@ do not have buffer spots.</p>
442 464
 <div class="section level2">
443 465
 <h2 id="coefficient-of-variation-cv">Coefficient of Variation (CV)<a class="anchor" aria-label="anchor" href="#coefficient-of-variation-cv"></a>
444 466
 </h2>
445
-<p>To calculate the CV’s we utilise the <span style="     color: red !important;">cv_estimation()</span> function with
446
-a <code>cv_cut_off</code> specified , sample identifier variable and
467
+<p>To calculate the CV’s we utilise the
468
+<font color="red">cv_estimation()</font> function with a
469
+<code>cv_cut_off</code> specified , sample identifier variable and
447 470
 antigen specified under <code>sampleID_var</code> and
448 471
 <code>antigen</code> respectively. The <code>replicate_var</code> and
449 472
 <code>mfi_var</code> identifies the variable with the replicate rank
... ...
@@ -471,7 +494,7 @@ generated and MFI’s values.</p>
471 494
 <h3 id="summary-of-cv-values">Summary of CV values<a class="anchor" aria-label="anchor" href="#summary-of-cv-values"></a>
472 495
 </h3>
473 496
 <p>Here we create a summary of the CV values for each sample/slide
474
-utilising the <span style="     color: red !important;">cv_by_sample_estimation()</span>
497
+utilising the <font color="red">cv_by_sample_estimation()</font>
475 498
 function. This helps to identify samples with high values of CV. On the
476 499
 protGear dashboard an interactive table is created to show the summary
477 500
 for each sample.</p>
... ...
@@ -504,9 +527,10 @@ for each sample.</p>
504 527
 <div class="section level3">
505 528
 <h3 id="best-replicates">Best replicates<a class="anchor" aria-label="anchor" href="#best-replicates"></a>
506 529
 </h3>
507
-<p>We have implemented a function <span style="     color: red !important;">best_CV_estimation()</span> to
508
-select the best replicates if an experiment has more than 2 technical
509
-replicates. This helps to select the less variable replicates.</p>
530
+<p>We have implemented a function
531
+<font color="red">best_CV_estimation()</font> to select the best
532
+replicates if an experiment has more than 2 technical replicates. This
533
+helps to select the less variable replicates.</p>
510 534
 <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
511 535
 <code class="sourceCode R"><span><span class="co">#' @________________________________data_with_selected_best_2_CV_______________________________________</span></span>
512 536
 <span><span class="co">#' data with the selected mean for the best 2 CVs</span></span>
... ...
@@ -688,7 +712,7 @@ CD4TAG
688 712
 <div class="section level3">
689 713
 <h3 id="subtracting-the-tag-values">Subtracting the TAG values<a class="anchor" aria-label="anchor" href="#subtracting-the-tag-values"></a>
690 714
 </h3>
691
-<p>The function <span style="     color: red !important;">tag_subtract()</span> implements the
715
+<p>The function <font color="red">tag_subtract()</font> implements the
692 716
 TAG subtration by matching the TAG names with the corresponding TAG
693 717
 values.</p>
694 718
 <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
... ...
@@ -780,11 +804,10 @@ normalisation techniques;</p>
780 804
 <span>  <span class="fu">dplyr</span><span class="fu">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">sample_array_ID</span>, <span class="va">sample_index</span>, <span class="va">slide</span><span class="op">)</span></span>
781 805
 <span></span>
782 806
 <span><span class="va">control_antigens</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CommercialHumanIgG"</span>, <span class="st">"CD4TAG"</span><span class="op">)</span></span></code></pre></div>
783
-<p>The <span style="     color: red !important;">matrix_normalise()</span> function
784
-is used to normalise the data and returns a list of plots and a matrix
785
-of normalised values. A plot is returned if
786
-<code>return_plot = TRUE</code> otherwise only a matrix of normalised
787
-values will be returned.</p>
807
+<p>The <font color="red">matrix_normalise()</font> function is used to
808
+normalise the data and returns a list of plots and a matrix of
809
+normalised values. A plot is returned if <code>return_plot = TRUE</code>
810
+otherwise only a matrix of normalised values will be returned.</p>
788 811
 <div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
789 812
 <code class="sourceCode R"><span><span class="va">normlise_df</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/matrix_normalise.html">matrix_normalise</a></span><span class="op">(</span><span class="va">matrix_antigen</span>,</span>
790 813
 <span>  method <span class="op">=</span> <span class="st">"vsn"</span>, array_matrix <span class="op">=</span> <span class="va">array_matrix</span>,</span>
... ...
@@ -797,8 +820,8 @@ values will be returned.</p>
797 820
 <h3 id="compare-normalisation-methods">Compare normalisation methods<a class="anchor" aria-label="anchor" href="#compare-normalisation-methods"></a>
798 821
 </h3>
799 822
 <p>On the dashboard we compare the different normalisation techniques
800
-using the <span style="     color: red !important;">mutiple_plot()</span> function after
801
-loading the data.</p>
823
+using the <font color="red">mutiple_plot()</font> function after loading
824
+the data.</p>
802 825
 <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
803 826
 <code class="sourceCode R"><span><span class="va">control_antigens</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CommercialHumanIgG"</span>, <span class="st">"CD4TAG"</span><span class="op">)</span></span>
804 827
 <span><span class="co">## no normalisation</span></span>
... ...
@@ -987,9 +1010,9 @@ Install R: Open an internet browser and go to www.r-project.org.</p>
987 1010
 <span><span class="co">## [10] GGally_2.1.2        rlang_1.0.6         magick_2.7.3       </span></span>
988 1011
 <span><span class="co">## [13] factoextra_1.0.7    FactoMineR_2.6      styler_1.8.1       </span></span>
989 1012
 <span><span class="co">## [16] knitr_1.41          png_0.1-7           gridExtra_2.3      </span></span>
990
-<span><span class="co">## [19] kableExtra_1.3.4    data.table_1.14.6   pheatmap_1.0.12    </span></span>
991
-<span><span class="co">## [22] scales_1.2.1        purrr_0.3.5         gtools_3.9.3       </span></span>
992
-<span><span class="co">## [25] ggpubr_0.5.0        ggplot2_3.4.0      </span></span>
1013
+<span><span class="co">## [19] data.table_1.14.6   pheatmap_1.0.12     scales_1.2.1       </span></span>
1014
+<span><span class="co">## [22] purrr_0.3.5         gtools_3.9.3        ggpubr_0.5.0       </span></span>
1015
+<span><span class="co">## [25] ggplot2_3.4.0      </span></span>
993 1016
 <span><span class="co">## </span></span>
994 1017
 <span><span class="co">## loaded via a namespace (and not attached):</span></span>
995 1018
 <span><span class="co">##   [1] backports_1.4.1        systemfonts_1.0.4      plyr_1.8.8            </span></span>
... ...
@@ -999,44 +1022,44 @@ Install R: Open an internet browser and go to www.r-project.org.</p>
999 1022
 <span><span class="co">##  [13] fansi_1.0.3            magrittr_2.0.3         memoise_2.0.1         </span></span>
1000 1023
 <span><span class="co">##  [16] cluster_2.1.4          tzdb_0.3.0             readr_2.1.3           </span></span>
1001 1024
 <span><span class="co">##  [19] Biostrings_2.66.0      annotate_1.76.0        Kendall_2.2.1         </span></span>
1002
-<span><span class="co">##  [22] R.utils_2.12.2         sandwich_3.0-2         svglite_2.1.0         </span></span>
1003
-<span><span class="co">##  [25] pkgdown_2.0.6          colorspace_2.0-3       blob_1.2.3            </span></span>
1004
-<span><span class="co">##  [28] rvest_1.0.3            ggrepel_0.9.2          textshaping_0.3.6     </span></span>
1025
+<span><span class="co">##  [22] R.utils_2.12.2         svglite_2.1.0          sandwich_3.0-2        </span></span>
1026
+<span><span class="co">##  [25] pkgdown_2.0.6          colorspace_2.0-3       rvest_1.0.3           </span></span>
1027
+<span><span class="co">##  [28] blob_1.2.3             ggrepel_0.9.2          textshaping_0.3.6     </span></span>
1005 1028
 <span><span class="co">##  [31] xfun_0.35              RCurl_1.98-1.9         crayon_1.5.2          </span></span>
1006 1029
 <span><span class="co">##  [34] jsonlite_1.8.3         genefilter_1.80.0      survival_3.4-0        </span></span>
1007
-<span><span class="co">##  [37] zoo_1.8-11             glue_1.6.2             gtable_0.3.1          </span></span>
1008
-<span><span class="co">##  [40] XVector_0.38.0         zlibbioc_1.44.0        emmeans_1.8.2         </span></span>
1009
-<span><span class="co">##  [43] webshot_0.5.4          R.cache_0.16.0         car_3.1-1             </span></span>
1010
-<span><span class="co">##  [46] abind_1.4-5            mvtnorm_1.1-3          DBI_1.1.3             </span></span>
1011
-<span><span class="co">##  [49] rstatix_0.7.1          Rcpp_1.0.9             viridisLite_0.4.1     </span></span>
1012
-<span><span class="co">##  [52] xtable_1.8-4           gridGraphics_0.5-1     flashClust_1.01-2     </span></span>
1013
-<span><span class="co">##  [55] bit_4.0.5              preprocessCore_1.60.0  stats4_4.2.2          </span></span>
1014
-<span><span class="co">##  [58] DT_0.26                htmlwidgets_1.5.4      httr_1.4.4            </span></span>
1015
-<span><span class="co">##  [61] RColorBrewer_1.1-3     ellipsis_0.3.2         farver_2.1.1          </span></span>
1016
-<span><span class="co">##  [64] pkgconfig_2.0.3        reshape_0.8.9          XML_3.99-0.12         </span></span>
1017
-<span><span class="co">##  [67] R.methodsS3_1.8.2      multcompView_0.1-8     sass_0.4.4            </span></span>
1018
-<span><span class="co">##  [70] utf8_1.2.2             labeling_0.4.2         tidyselect_1.2.0      </span></span>
1019
-<span><span class="co">##  [73] later_1.3.0            AnnotationDbi_1.60.0   munsell_0.5.0         </span></span>
1020
-<span><span class="co">##  [76] tools_4.2.2            cachem_1.0.6           cli_3.4.1             </span></span>
1021
-<span><span class="co">##  [79] generics_0.1.3         RSQLite_2.2.19         broom_1.0.1           </span></span>
1022
-<span><span class="co">##  [82] evaluate_0.18          stringr_1.4.1          fastmap_1.1.0         </span></span>
1023
-<span><span class="co">##  [85] yaml_2.3.6             ragg_1.2.4             bit64_4.0.5           </span></span>
1024
-<span><span class="co">##  [88] fs_1.5.2               KEGGREST_1.38.0        nlme_3.1-160          </span></span>
1025
-<span><span class="co">##  [91] mime_0.12              R.oo_1.25.0            leaps_3.1             </span></span>
1026
-<span><span class="co">##  [94] xml2_1.3.3             compiler_4.2.2         rstudioapi_0.14       </span></span>
1027
-<span><span class="co">##  [97] plotly_4.10.1          affyio_1.68.0          ggsignif_0.6.4        </span></span>
1028
-<span><span class="co">## [100] tibble_3.1.8           bslib_0.4.1            stringi_1.7.8         </span></span>
1029
-<span><span class="co">## [103] highr_0.9              desc_1.4.2             lattice_0.20-45       </span></span>
1030
-<span><span class="co">## [106] Matrix_1.5-3           vctrs_0.5.1            pillar_1.8.1          </span></span>
1031
-<span><span class="co">## [109] lifecycle_1.0.3        BiocManager_1.30.19    jquerylib_0.1.4       </span></span>
1032
-<span><span class="co">## [112] estimability_1.4.1     bitops_1.0-7           httpuv_1.6.6          </span></span>
1033
-<span><span class="co">## [115] R6_2.5.1               affy_1.76.0            promises_1.2.0.1      </span></span>
1034
-<span><span class="co">## [118] IRanges_2.32.0         codetools_0.2-18       boot_1.3-28.1         </span></span>
1035
-<span><span class="co">## [121] MASS_7.3-58.1          assertthat_0.2.1       rprojroot_2.0.3       </span></span>
1036
-<span><span class="co">## [124] withr_2.5.0            GenomeInfoDbData_1.2.9 S4Vectors_0.36.0      </span></span>
1037
-<span><span class="co">## [127] multcomp_1.4-20        mgcv_1.8-41            hms_1.1.2             </span></span>
1038
-<span><span class="co">## [130] rmarkdown_2.18         carData_3.0-5          scatterplot3d_0.3-42  </span></span>
1039
-<span><span class="co">## [133] shiny_1.7.3</span></span></code></pre>
1030
+<span><span class="co">##  [37] zoo_1.8-11             glue_1.6.2             kableExtra_1.3.4      </span></span>
1031
+<span><span class="co">##  [40] gtable_0.3.1           zlibbioc_1.44.0        emmeans_1.8.2         </span></span>
1032
+<span><span class="co">##  [43] XVector_0.38.0         webshot_0.5.4          R.cache_0.16.0        </span></span>
1033
+<span><span class="co">##  [46] car_3.1-1              abind_1.4-5            mvtnorm_1.1-3         </span></span>
1034
+<span><span class="co">##  [49] DBI_1.1.3              rstatix_0.7.1          Rcpp_1.0.9            </span></span>
1035
+<span><span class="co">##  [52] viridisLite_0.4.1      xtable_1.8-4           gridGraphics_0.5-1    </span></span>
1036
+<span><span class="co">##  [55] flashClust_1.01-2      bit_4.0.5              preprocessCore_1.60.0 </span></span>
1037
+<span><span class="co">##  [58] stats4_4.2.2           DT_0.26                htmlwidgets_1.5.4     </span></span>
1038
+<span><span class="co">##  [61] httr_1.4.4             RColorBrewer_1.1-3     ellipsis_0.3.2        </span></span>
1039
+<span><span class="co">##  [64] farver_2.1.1           pkgconfig_2.0.3        reshape_0.8.9         </span></span>
1040
+<span><span class="co">##  [67] XML_3.99-0.12          R.methodsS3_1.8.2      multcompView_0.1-8    </span></span>
1041
+<span><span class="co">##  [70] sass_0.4.4             utf8_1.2.2             labeling_0.4.2        </span></span>
1042
+<span><span class="co">##  [73] tidyselect_1.2.0       later_1.3.0            AnnotationDbi_1.60.0  </span></span>
1043
+<span><span class="co">##  [76] munsell_0.5.0          tools_4.2.2            cachem_1.0.6          </span></span>
1044
+<span><span class="co">##  [79] cli_3.4.1              generics_0.1.3         RSQLite_2.2.19        </span></span>
1045
+<span><span class="co">##  [82] broom_1.0.1            evaluate_0.18          stringr_1.4.1         </span></span>
1046
+<span><span class="co">##  [85] fastmap_1.1.0          yaml_2.3.6             ragg_1.2.4            </span></span>
1047
+<span><span class="co">##  [88] bit64_4.0.5            fs_1.5.2               KEGGREST_1.38.0       </span></span>
1048
+<span><span class="co">##  [91] nlme_3.1-160           mime_0.12              R.oo_1.25.0           </span></span>
1049
+<span><span class="co">##  [94] xml2_1.3.3             leaps_3.1              compiler_4.2.2        </span></span>
1050
+<span><span class="co">##  [97] rstudioapi_0.14        plotly_4.10.1          affyio_1.68.0         </span></span>
1051
+<span><span class="co">## [100] ggsignif_0.6.4         tibble_3.1.8           bslib_0.4.1           </span></span>
1052
+<span><span class="co">## [103] stringi_1.7.8          highr_0.9              desc_1.4.2            </span></span>
1053
+<span><span class="co">## [106] lattice_0.20-45        Matrix_1.5-3           vctrs_0.5.1           </span></span>
1054
+<span><span class="co">## [109] pillar_1.8.1           lifecycle_1.0.3        BiocManager_1.30.19   </span></span>
1055
+<span><span class="co">## [112] jquerylib_0.1.4        estimability_1.4.1     bitops_1.0-7          </span></span>
1056
+<span><span class="co">## [115] httpuv_1.6.6           R6_2.5.1               affy_1.76.0           </span></span>
1057
+<span><span class="co">## [118] promises_1.2.0.1       IRanges_2.32.0         codetools_0.2-18      </span></span>
1058
+<span><span class="co">## [121] boot_1.3-28.1          MASS_7.3-58.1          assertthat_0.2.1      </span></span>
1059
+<span><span class="co">## [124] rprojroot_2.0.3        withr_2.5.0            GenomeInfoDbData_1.2.9</span></span>
1060
+<span><span class="co">## [127] multcomp_1.4-20        S4Vectors_0.36.0       mgcv_1.8-41           </span></span>
1061
+<span><span class="co">## [130] hms_1.1.2              rmarkdown_2.18         carData_3.0-5         </span></span>
1062
+<span><span class="co">## [133] scatterplot3d_0.3-42   shiny_1.7.3</span></span></code></pre>
1040 1063
 </div>
1041 1064
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1042 1065
 
1043 1066
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... ...
@@ -17,7 +17,7 @@
17 17
       </button>
18 18
       <span class="navbar-brand">
19 19
         <a class="navbar-link" href="index.html">protGear</a>
20
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
20
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
21 21
       </span>
22 22
     </div>
23 23
 
... ...
@@ -82,13 +82,13 @@
82 82
     <p>Mwai K, James
83 83
 Mburu (2022).
84 84
 <em>protGear: Protein Micro Array Data Management and Interactive Visualization</em>.
85
-R package version 1.3.3, <a href="https://github.com/Keniajin/protGear" class="external-link">https://github.com/Keniajin/protGear</a>. 
85
+R package version 1.3.31, <a href="https://github.com/Keniajin/protGear" class="external-link">https://github.com/Keniajin/protGear</a>. 
86 86
 </p>
87 87
     <pre>@Manual{,
88 88
   title = {protGear: Protein Micro Array Data Management and Interactive Visualization},
89 89
   author = {Kennedy Mwai and {James Mburu}},
90 90
   year = {2022},
91
-  note = {R package version 1.3.3},
91
+  note = {R package version 1.3.31},
92 92
   url = {https://github.com/Keniajin/protGear},
93 93
 }</pre>
94 94
 
... ...
@@ -33,7 +33,7 @@
33 33
       </button>
34 34
       <span class="navbar-brand">
35 35
         <a class="navbar-link" href="index.html">protGear</a>
36
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
36
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
37 37
       </span>
38 38
     </div>
39 39
 
... ...
@@ -17,7 +17,7 @@
17 17
       </button>
18 18
       <span class="navbar-brand">
19 19
         <a class="navbar-link" href="../index.html">protGear</a>
20
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
20
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
21 21
       </span>
22 22
     </div>
23 23
 
... ...
@@ -3,5 +3,5 @@ pkgdown: 2.0.6
3 3
 pkgdown_sha: ~
4 4
 articles:
5 5
   vignette: vignette.html
6
-last_built: 2022-11-27T10:59Z
6
+last_built: 2022-12-11T18:34Z
7 7
 
... ...
@@ -17,7 +17,7 @@
17 17
       </button>
18 18
       <span class="navbar-brand">
19 19
         <a class="navbar-link" href="../index.html">protGear</a>
20
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
20
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
21 21
       </span>
22 22
     </div>
23 23
 
... ...
@@ -18,7 +18,7 @@ duplicates. The function has been build for up to to 3 replicates so far"><!-- m
18 18
       </button>
19 19
       <span class="navbar-brand">
20 20
         <a class="navbar-link" href="../index.html">protGear</a>
21
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
21
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
22 22
       </span>
23 23
     </div>
24 24
 
... ...
@@ -17,7 +17,7 @@
17 17
       </button>
18 18
       <span class="navbar-brand">
19 19
         <a class="navbar-link" href="../index.html">protGear</a>
20
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
20
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
21 21
       </span>
22 22
     </div>
23 23
 
... ...
@@ -19,7 +19,7 @@
19 19
       </button>
20 20
       <span class="navbar-brand">
21 21
         <a class="navbar-link" href="../index.html">protGear</a>
22
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span>
22
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span>
23 23
       </span>
24 24
     </div>
25 25
 
... ...
@@ -21,7 +21,7 @@ a file with the identifiers for the samples in array file."><!-- mathjax --><scr
21 21
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33 33
 library(scales)
34 34
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35 35
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36
-suppressWarnings(library(kableExtra)) ## kable extra casusing warnings
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 library(gridExtra)
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 library(png)
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 library(knitr)
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54 53
                        tidy='styler', tidy.opts=list(strict=TRUE))
55 54
 
56 55
 
56
+## Color Format
57
+colFmt <- function(x,color) {
58
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59
+  outputFormat <- knitr::opts_knit$get("rmarkdown.pandoc.to")
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+    ret <- paste("\\textcolor{",color,"}{",x,"}",sep="")
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57 74
 ```
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98 115
 
99 116
 ## Importing data
100 117
 
101
-```{r call_protGear}
118
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102 119
 ## load the package 
103
-library(protGear)
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 ```
105 122
 
106 123
 The first part is to specify the parameters of the micro-array experiment to assist in processing the data.  The parameters specified are 
... ...
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139 156
 ```
140 157
 
141 158
 
142
-The function  `r text_spec("check_sampleID_files()",color="red")` helps to check whether each micro array file has a corresponding sample identifier file. The sample identifier files are generated from the lab plate maps to match the corresponding samples on a specific slide.If the sample identifier file is missing, protGear automatically generates the id's. 
159
+The function  `r colFmt("check_sampleID_files()",color="red")` helps to check whether each micro array file has a corresponding sample identifier file. The sample identifier files are generated from the lab plate maps to match the corresponding samples on a specific slide.If the sample identifier file is missing, protGear automatically generates the id's. 
143 160
 
144 161
 ### Spatial structure of slide
145 162
 
146
-protGear offers a functionality to inspect the slide visually for any strong spatial biases when the scan image is not available. However, we recommend using the scanned image to visualize the spatial artefacts that might not be recorded in the `.gpr` file. We include the functions `r text_spec("visualize_slide()",color="red")` and `r text_spec("visualize_slide_2d()",color="red")`  to check the spatial structure. The functions are build on `r text_spec("structure_plot()",color="red")`  which shows the block and mini-array structure of a slide. 
163
+protGear offers a functionality to inspect the slide visually for any strong spatial biases when the scan image is not available. However, we recommend using the scanned image to visualize the spatial artefacts that might not be recorded in the `.gpr` file. We include the functions `r colFmt("visualize_slide()",color="red")` and `r colFmt("visualize_slide_2d()",color="red")`  to check the spatial structure. The functions are build on `r colFmt("structure_plot()",color="red")`  which shows the block and mini-array structure of a slide. 
147 164
 
148 165
 
149 166
 
150 167
 #### Visualize the foreground MFI 
151 168
 
152
-Here we visualize the foreground MFI using the `r text_spec("visualize_slide",color="red")` function
169
+Here we visualize the foreground MFI using the `r colFmt("visualize_slide",color="red")` function
153 170
 
154 171
 ```{r slide_struct, fig.align='left'}
155 172
 visualize_slide(infile=system.file("extdata/array_data/machine1/KK2-06.txt", package="protGear"),
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@@ -169,11 +186,11 @@ visualize_slide_2d(infile =system.file("extdata/array_data/machine1/KK2-06.txt",
169 186
 
170 187
 ### Import .gpr/txt data
171 188
 
172
-Microarray data is imported using the `r text_spec("read_array_files()",color="red")` function. The function accepts the following mandatory arguments; 
189
+Microarray data is imported using the `r colFmt("read_array_files()",color="red")` function. The function accepts the following mandatory arguments; 
173 190
 
174
- - `filename` - the name of the file which the data are to be read from. In this example a list of multiple files from a folder is used and passed to `r text_spec("read_array_files()","red")` using `r text_spec("purrr",color="red")`. 
191
+ - `filename` - the name of the file which the data are to be read from. In this example a list of multiple files from a folder is used and passed to `r colFmt("read_array_files()","red")` using `r colFmt("purrr",color="red")`. 
175 192
  - `data_path` - The path where the file with the data  is located 
176
- - `genepix_vars` -  A list of specific definitions of the experiment design. See  `r text_spec("array_vars()",color="red")`  
193
+ - `genepix_vars` -  A list of specific definitions of the experiment design. See  `r colFmt("array_vars()",color="red")`  
177 194
 
178 195
 For this example I use the sub-folder 1 specified using `genepix_vars$paths[[1]]` which is this path under vignette folder `r genepix_vars$paths[[1]]`. 
179 196
  
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@@ -203,7 +220,7 @@ Background noise is caused by non-specific fluorescence such as auto-fluorescenc
203 220
   4) Normal and exponential model (normexp) 
204 221
   5) Log-linear background correction (Edwards)
205 222
 
206
-In  '`.gpr`' files the Background column starts with a '`B`' followed by the wavelength or channel. In order to perform background correction, we extract the background mean fluorescent intensities (MFI's) using the function `r text_spec("extract_bg()",color="red")` . The function accepts the arguments `iden` which is the file identifier,  `data_files` a list of data objects with names utilised by `iden` and `genepix_vars` defined using  `r text_spec("array_vars()",color="red")` function. We utilise the `purr::map` function to extract the background MFI of multiple data files. 
223
+In  '`.gpr`' files the Background column starts with a '`B`' followed by the wavelength or channel. In order to perform background correction, we extract the background mean fluorescent intensities (MFI's) using the function `r colFmt("extract_bg()",color="red")` . The function accepts the arguments `iden` which is the file identifier,  `data_files` a list of data objects with names utilised by `iden` and `genepix_vars` defined using  `r colFmt("array_vars()",color="red")` function. We utilise the `purr::map` function to extract the background MFI of multiple data files. 
207 224
 
208 225
 ```{r bg_data}
209 226
 ## utilising the map package we process a number of files  under data_files list
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@@ -215,7 +232,7 @@ allData_bg <- plyr::ldply(allData_bg)
215 232
 
216 233
 ### Foreground vs Background
217 234
 
218
-Before selecting the best background correction approach the MFI signals are be inspected visually. In protGear we first utilise the function `r text_spec("plot_FB()",color="red")` that graphs the background, _BG_Median_ and foreground values, _FBG_Median_. On the protGear Shiny platform the visuals are interactive and you can identify the features or blocks with strong bias. 
235
+Before selecting the best background correction approach the MFI signals are be inspected visually. In protGear we first utilise the function `r colFmt("plot_FB()",color="red")` that graphs the background, _BG_Median_ and foreground values, _FBG_Median_. On the protGear Shiny platform the visuals are interactive and you can identify the features or blocks with strong bias. 
219 236
 
220 237
 ```{r bg_vs_fg}
221 238
 p1 <- plot_FB(allData_bg,antigen_name="antigen",
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@@ -238,10 +255,10 @@ p2
238 255
 
239 256
 After background visualization and selecting the best approach the array data are merged with their specific sample identifier files. 
240 257
 
241
-_`r text_spec("Note:",color="red")`_ Each array file must have its own corresponding sample identifier `.csv` file stored in `r text_spec("array_vars()",color="red")` function under `sampleID_path`. Check General information section.  
258
+_`r colFmt("Note:",color="red")`_ Each array file must have its own corresponding sample identifier `.csv` file stored in `r colFmt("array_vars()",color="red")` function under `sampleID_path`. Check General information section.  
242 259
 
243 260
 
244
-The method of background subtraction selected is specified under `r text_spec("method",color="red")` below. The background correction is performed by  `r text_spec("bg_correct()",color="red")` function.
261
+The method of background subtraction selected is specified under `r colFmt("method",color="red")` below. The background correction is performed by  `r colFmt("bg_correct()",color="red")` function.
245 262
 
246 263
 
247 264
 
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@@ -254,7 +271,7 @@ sample_ID_merged_dfs <- set_names(sample_ID_merged_dfs, purrr::map(filenames, na
254 271
 
255 272
 ## Buffer spots
256 273
 
257
-Buffer spots are specific to the experiment design and are not always included. Buffer spots are used to check for unexpected scanning artefacts. The buffer spots should have similar values in  different slides. Some outliers are expected for buffer spots close sample spots or landmark. However you can specify the name of your control antigens here `r text_spec('buffer_spot="buffer"',color="blue")` if you do not have buffer spots.
274
+Buffer spots are specific to the experiment design and are not always included. Buffer spots are used to check for unexpected scanning artefacts. The buffer spots should have similar values in  different slides. Some outliers are expected for buffer spots close sample spots or landmark. However you can specify the name of your control antigens here `r colFmt('buffer_spot="buffer"',color="blue")` if you do not have buffer spots.
258 275
 
259 276
 ```{r buffer_spots_data}
260 277
 buffer_transp <- purrr::map(.x=sample_ID_merged_dfs, .f=buffer_spots ,  buffer_spot="buffer")
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@@ -268,7 +285,7 @@ plot_buffer(buffers,buffer_names="antigen",buffer_mfi="FMedianBG_correct",slide_
268 285
 
269 286
 ## Coefficient of Variation (CV)
270 287
 
271
-To calculate the CV's we utilise the  `r text_spec("cv_estimation()",color="red")` function with a `cv_cut_off` specified , sample identifier variable and antigen specified under `sampleID_var` and `antigen` respectively. The `replicate_var` and `mfi_var` identifies the variable with the replicate rank generated and MFI's values. 
288
+To calculate the CV's we utilise the  `r colFmt("cv_estimation()",color="red")` function with a `cv_cut_off` specified , sample identifier variable and antigen specified under `sampleID_var` and `antigen` respectively. The `replicate_var` and `mfi_var` identifies the variable with the replicate rank generated and MFI's values. 
272 289
 
273 290
 
274 291
 ```{r replicates_plot}
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@@ -291,7 +308,7 @@ GGally::ggpairs(aa,aes(color=cvCat_all) ,
291 308
 
292 309
 ### Summary of CV values
293 310
 
294
-Here we create a summary of the CV values for each sample/slide utilising the `r text_spec("cv_by_sample_estimation()",color="red")` function.  This helps to identify samples with high values of CV. On the protGear dashboard an interactive table is created to show the summary for each sample. 
311
+Here we create a summary of the CV values for each sample/slide utilising the `r colFmt("cv_by_sample_estimation()",color="red")` function.  This helps to identify samples with high values of CV. On the protGear dashboard an interactive table is created to show the summary for each sample. 
295 312
 
296 313
 ```{r cv_summary}
297 314
 
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@@ -326,7 +343,7 @@ ggplot(all_cv_sample)+
326 343
 
327 344
 ### Best replicates
328 345
 
329
-We have implemented a function `r text_spec("best_CV_estimation()",color="red")` to select the best replicates if an experiment has more than 2 technical replicates. This helps to select the less variable replicates.
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+We have implemented a function `r colFmt("best_CV_estimation()",color="red")` to select the best replicates if an experiment has more than 2 technical replicates. This helps to select the less variable replicates.
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 ```{r best_reps}
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 #' @________________________________data_with_selected_best_2_CV_______________________________________ 
... ...
@@ -375,14 +392,13 @@ batch_vars <- list(machine="m1", day="0520")
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 ```{r tag_glimpse}
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 tb1 <- data.frame(head(tag_file, n=10))
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 tb1 %>% 
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-  knitr::kable() #%>%
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-  #kable_styling()
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+  knitr::kable() 
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 ```
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 ### Subtracting the TAG values
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-The function `r text_spec("tag_subtract()",color="red")` implements the TAG subtration by matching the TAG names with the corresponding TAG values. 
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+The function `r colFmt("tag_subtract()",color="red")` implements the TAG subtration by matching the TAG names with the corresponding TAG values. 
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 ```{r tag_subtract}
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 #' @________________________________subtract_the_tag_values_______________________________________ 
... ...
@@ -468,7 +484,7 @@ control_antigens <- c("CommercialHumanIgG","CD4TAG")
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 ```
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-The `r text_spec("matrix_normalise()",color="red")`  function is used to normalise the data and returns a list of plots and a matrix of normalised values. A plot is returned if ` return_plot = TRUE` otherwise only a matrix of normalised values will be returned. 
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+The `r colFmt("matrix_normalise()",color="red")`  function is used to normalise the data and returns a list of plots and a matrix of normalised values. A plot is returned if ` return_plot = TRUE` otherwise only a matrix of normalised values will be returned. 
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 ```{r normalised_df}
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 normlise_df <- matrix_normalise(matrix_antigen, method = "vsn", array_matrix=array_matrix,
... ...
@@ -481,7 +497,7 @@ normlise_df$plot_normalisation
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 ### Compare normalisation methods
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-On the dashboard we compare the different normalisation techniques using the `r text_spec("mutiple_plot()",color="red")` function after loading the data.
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+On the dashboard we compare the different normalisation techniques using the `r colFmt("mutiple_plot()",color="red")` function after loading the data.
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 ```{r compare_methods}