... | ... |
@@ -1,7 +1,7 @@ |
1 | 1 |
Package: protGear |
2 | 2 |
Type: Package |
3 | 3 |
Title: Protein Micro Array Data Management and Interactive Visualization |
4 |
-Version: 1.3.3 |
|
4 |
+Version: 1.3.31 |
|
5 | 5 |
Authors@R: c(person("Kennedy", "Mwai", role = c("cre", "aut"), email = "keniajin@gmail.com"), |
6 | 6 |
person(c("James", "Mburu"), role = "aut" , email = "mburuwanja@gmail.com") , |
7 | 7 |
person(c("Jacqueline", "Waeni"), role = "ctb" , email = "jacqwaeni@gmail.com")) |
... | ... |
@@ -16,7 +16,7 @@ Imports: |
16 | 16 |
ggpubr (>= 0.4.0), gtools (>= 3.8.2) , tibble (>= 3.1.0) , rmarkdown (>= 2.9) , knitr (>= 1.33), |
17 | 17 |
utils (>= 3.6), genefilter (>= 1.74.0), readr (>= 2.0.1) , Biobase (>= 2.52.0), |
18 | 18 |
plyr (>= 1.8.6) , Kendall (>= 2.2) , shiny (>= 1.0.0) , purrr (>= 0.3.4), plotly (>= 4.9.0) , MASS (>= 7.3) , |
19 |
- htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2) , shinydashboard (>= 0.7.1) , kableExtra (>= 1.3.4), |
|
19 |
+ htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2) , shinydashboard (>= 0.7.1) , |
|
20 | 20 |
GGally (>= 2.1.2) , pheatmap (>= 1.0.12) , grid(>= 4.1.1), |
21 | 21 |
styler (>= 1.6.1) , factoextra (>= 1.0.7) ,FactoMineR (>= 2.4) , rlang (>= 0.4.11), |
22 | 22 |
remotes (>= 2.4.0) |
... | ... |
@@ -27,5 +27,5 @@ Suggests: |
27 | 27 |
biocViews: Microarray, OneChannel, Preprocessing , BiomedicalInformatics , Proteomics , BatchEffect, Normalization , Bayesian, Clustering, Regression,SystemsBiology, ImmunoOncology |
28 | 28 |
Encoding: UTF-8 |
29 | 29 |
LazyData: false |
30 |
-RoxygenNote: 7.2.2 |
|
30 |
+RoxygenNote: 7.2.3 |
|
31 | 31 |
VignetteBuilder: knitr |
... | ... |
@@ -7,7 +7,6 @@ |
7 | 7 |
#' @importFrom flexdashboard renderValueBox valueBoxOutput valueBox |
8 | 8 |
#' @importFrom shinydashboard renderInfoBox |
9 | 9 |
#' @importFrom dplyr group_rows between first last |
10 |
-#' @importFrom kableExtra text_spec |
|
11 | 10 |
#' @export |
12 | 11 |
#' @examples |
13 | 12 |
#' app <- system.file("shiny-examples", "protGear_interactive", |
... | ... |
@@ -32,7 +32,7 @@ |
32 | 32 |
</button> |
33 | 33 |
<span class="navbar-brand"> |
34 | 34 |
<a class="navbar-link" href="index.html">protGear</a> |
35 |
- <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.3</span> |
|
35 |
+ <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.3.31</span> |
|
36 | 36 |
</span> |
37 | 37 |
</div> |
38 | 38 |
|
... | ... |
@@ -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 |
|
... | ... |
@@ -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 |
|
... | ... |
@@ -82,7 +82,7 @@ |
82 | 82 |
<h4 data-toc-skip class="author">Kennedy |
83 | 83 |
Mwai</h4> |
84 | 84 |
|
85 |
- <h4 data-toc-skip class="date">27 November, 2022</h4> |
|
85 |
+ <h4 data-toc-skip class="date">11 December, 2022</h4> |
|
86 | 86 |
|
87 | 87 |
<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> |
88 | 88 |
<div class="hidden name"><code>vignette.Rmd</code></div> |
... | ... |
@@ -98,7 +98,6 @@ Mwai</h4> |
98 | 98 |
<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> |
99 | 99 |
<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> |
100 | 100 |
<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> |
101 |
-<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> |
|
102 | 101 |
<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> |
103 | 102 |
<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> |
104 | 103 |
<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> |
... | ... |
@@ -116,7 +115,24 @@ Mwai</h4> |
116 | 115 |
<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">$</span><span class="fu">set</span><span class="op">(</span>echo <span class="op">=</span> <span class="cn">TRUE</span>, message<span class="op">=</span><span class="cn">FALSE</span>,warning <span class="op">=</span> <span class="cn">FALSE</span>,</span> |
117 | 116 |
<span> fig.align <span class="op">=</span> <span class="st">'center'</span>,</span> |
118 | 117 |
<span> dev <span class="op">=</span> <span class="st">"png"</span>, </span> |
119 |
-<span> tidy<span class="op">=</span><span class="st">'styler'</span>, tidy.opts<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>strict<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> |
|
118 |
+<span> tidy<span class="op">=</span><span class="st">'styler'</span>, tidy.opts<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>strict<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span> |
|
119 |
+<span></span> |
|
120 |
+<span></span> |
|
121 |
+<span><span class="co">## Color Format</span></span> |
|
122 |
+<span><span class="va">colFmt</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>,<span class="va">color</span><span class="op">)</span> <span class="op">{</span></span> |
|
123 |
+<span> </span> |
|
124 |
+<span> <span class="va">outputFormat</span> <span class="op"><-</span> <span class="fu">knitr</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/knitr/man/opts_knit.html" class="external-link">opts_knit</a></span><span class="op">$</span><span class="fu">get</span><span class="op">(</span><span class="st">"rmarkdown.pandoc.to"</span><span class="op">)</span></span> |
|
125 |
+<span> </span> |
|
126 |
+<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> |
|
127 |
+<span> <span class="va">ret</span> <span class="op"><-</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> |
|
128 |
+<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> |
|
129 |
+<span> <span class="va">ret</span> <span class="op"><-</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">"<font color='"</span>,<span class="va">color</span>,<span class="st">"'>"</span>,<span class="va">x</span>,<span class="st">"</font>"</span>,sep<span class="op">=</span><span class="st">""</span><span class="op">)</span></span> |
|
130 |
+<span> <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span> |
|
131 |
+<span> <span class="va">ret</span> <span class="op"><-</span> <span class="va">x</span></span> |
|
132 |
+<span> <span class="op">}</span></span> |
|
133 |
+<span></span> |
|
134 |
+<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> |
|
135 |
+<span><span class="op">}</span></span></code></pre></div> |
|
120 | 136 |
<style type="text/css"> |
121 | 137 |
.custom-inline { |
122 | 138 |
color: red; |
... | ... |
@@ -257,8 +273,8 @@ from the quantification software under <code>Wavelengths</code>.</p> |
257 | 273 |
<span><span class="co">## [30] "ScanArea=1\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t" </span></span> |
258 | 274 |
<span><span class="co">## [31] "Supplier=\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t" </span></span> |
259 | 275 |
<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 |
264 | 280 |
sample identifier file is missing, protGear automatically generates the |
... | ... |
@@ -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 |
273 |
-functions <span style=" color: red !important;">visualize_slide()</span> and <span style=" color: red !important;">visualize_slide_2d()</span> to check |
|
274 |
-the spatial structure. The functions are build on <span style=" color: red !important;">structure_plot()</span> which shows |
|
275 |
-the block and mini-array structure of a slide.</p> |
|
289 |
+functions <font color="red">visualize_slide()</font> and |
|
290 |
+<font color="red">visualize_slide_2d()</font> to check the spatial |
|
291 |
+structure. The functions are build on |
|
292 |
+<font color="red">structure_plot()</font> which shows the block and |
|
293 |
+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 |
|
298 |
+<font color="red">visualize_slide</font> function</p> |
|
280 | 299 |
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> |
281 | 300 |
<code class="sourceCode R"><span><span class="fu"><a href="../reference/visualize_slide.html">visualize_slide</a></span><span class="op">(</span></span> |
282 | 301 |
<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> |
300 | 319 |
<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> |
302 | 321 |
</h3> |
303 |
-<p>Microarray data is imported using the <span style=" color: red !important;">read_array_files()</span> function. |
|
304 |
-The function accepts the following mandatory arguments;</p> |
|
322 |
+<p>Microarray data is imported using the |
|
323 |
+<font color="red">read_array_files()</font> function. The function |
|
324 |
+accepts the following mandatory arguments;</p> |
|
305 | 325 |
<ul> |
306 | 326 |
<li> |
307 | 327 |
<code>filename</code> - the name of the file which the data are to |
308 | 328 |
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 |
|
330 |
+<font color="red">purrr</font>.</li> |
|
310 | 331 |
<li> |
311 | 332 |
<code>data_path</code> - The path where the file with the data is |
312 | 333 |
located</li> |
313 | 334 |
<li> |
314 | 335 |
<code>genepix_vars</code> - A list of specific definitions of the |
315 |
-experiment design. See <span style=" color: red !important;">array_vars()</span> |
|
336 |
+experiment design. See <font color="red">array_vars()</font> |
|
316 | 337 |
</li> |
317 | 338 |
</ul> |
318 | 339 |
<p>For this example I use the sub-folder 1 specified using |
... | ... |
@@ -357,12 +378,14 @@ approaches;</p> |
357 | 378 |
<p>In ‘<code>.gpr</code>’ files the Background column starts with a |
358 | 379 |
‘<code>B</code>’ followed by the wavelength or channel. In order to |
359 | 380 |
perform background correction, we extract the background mean |
360 |
-fluorescent intensities (MFI’s) using the function <span style=" color: red !important;">extract_bg()</span> . The function |
|
361 |
-accepts the arguments <code>iden</code> which is the file identifier, |
|
381 |
+fluorescent intensities (MFI’s) using the function |
|
382 |
+<font color="red">extract_bg()</font> . The function accepts the |
|
383 |
+arguments <code>iden</code> which is the file identifier, |
|
362 | 384 |
<code>data_files</code> a list of data objects with names utilised by |
363 |
-<code>iden</code> and <code>genepix_vars</code> defined using <span style=" color: red !important;">array_vars()</span> function. We |
|
364 |
-utilise the <code>purr::map</code> function to extract the background |
|
365 |
-MFI of multiple data files.</p> |
|
385 |
+<code>iden</code> and <code>genepix_vars</code> defined using |
|
386 |
+<font color="red">array_vars()</font> function. We utilise the |
|
387 |
+<code>purr::map</code> function to extract the background MFI of |
|
388 |
+multiple data files.</p> |
|
366 | 389 |
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> |
367 | 390 |
<code class="sourceCode R"><span><span class="co">## utilising the map package we process a number of files under data_files list</span></span> |
368 | 391 |
<span><span class="va">dfs</span> <span class="op"><-</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> |
374 | 397 |
</h3> |
375 | 398 |
<p>Before selecting the best background correction approach the MFI |
376 | 399 |
signals are be inspected visually. In protGear we first utilise the |
377 |
-function <span style=" color: red !important;">plot_FB()</span> that |
|
378 |
-graphs the background, <em>BG_Median</em> and foreground values, |
|
379 |
-<em>FBG_Median</em>. On the protGear Shiny platform the visuals are |
|
380 |
-interactive and you can identify the features or blocks with strong |
|
381 |
-bias.</p> |
|
400 |
+function <font color="red">plot_FB()</font> that graphs the background, |
|
401 |
+<em>BG_Median</em> and foreground values, <em>FBG_Median</em>. On the |
|
402 |
+protGear Shiny platform the visuals are interactive and you can identify |
|
403 |
+the features or blocks with strong bias.</p> |
|
382 | 404 |
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> |
383 | 405 |
<code class="sourceCode R"><span><span class="va">p1</span> <span class="op"><-</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> |
384 | 406 |
<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 |
|
410 |
-<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 |
|
431 |
+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> |
|
415 | 437 |
<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"><-</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> |
417 | 439 |
<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 |
428 | 450 |
artefacts. The buffer spots should have similar values in different |
429 | 451 |
slides. Some outliers are expected for buffer spots close sample spots |
430 | 452 |
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"><-</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"><-</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"><-</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"><-</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 |
</div> |
1042 | 1065 |
|
... | ... |
@@ -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 |
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@@ -21,7 +21,7 @@ a file with the identifiers for the samples in array file."><!-- mathjax --><scr |
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@@ -18,7 +18,7 @@ data for micro array data."><!-- mathjax --><script src="https://cdnjs.cloudflar |
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@@ -18,7 +18,7 @@ files in the same directory. Removes the file extension"><!-- mathjax --><script |
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@@ -33,7 +33,6 @@ library(purrr) |
33 | 33 |
library(scales) |
34 | 34 |
library(pheatmap) |
35 | 35 |
library(data.table) |
36 |
-suppressWarnings(library(kableExtra)) ## kable extra casusing warnings |
|
37 | 36 |
library(gridExtra) |
38 | 37 |
library(png) |
39 | 38 |
library(knitr) |
... | ... |
@@ -54,6 +53,24 @@ knitr::opts_chunk$set(echo = TRUE, message=FALSE,warning = FALSE, |
54 | 53 |
tidy='styler', tidy.opts=list(strict=TRUE)) |
55 | 54 |
|
56 | 55 |
|
56 |
+## Color Format |
|
57 |
+colFmt <- function(x,color) { |
|
58 |
+ |
|
59 |
+ outputFormat <- knitr::opts_knit$get("rmarkdown.pandoc.to") |
|
60 |
+ |
|
61 |
+ if(outputFormat == 'latex') { |
|
62 |
+ ret <- paste("\\textcolor{",color,"}{",x,"}",sep="") |
|
63 |
+ } else if(outputFormat == 'html') { |
|
64 |
+ ret <- paste("<font color='",color,"'>",x,"</font>",sep="") |
|
65 |
+ } else { |
|
66 |
+ ret <- x |
|
67 |
+ } |
|
68 |
+ |
|
69 |
+ return(ret) |
|
70 |
+} |
|
71 |
+ |
|
72 |
+ |
|
73 |
+ |
|
57 | 74 |
``` |
58 | 75 |
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59 | 76 |
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@@ -98,9 +115,9 @@ BiocManager::install('protGear') |
98 | 115 |
|
99 | 116 |
## Importing data |
100 | 117 |
|
101 |
-```{r call_protGear} |
|
118 |
+```{r call_protGear } |
|
102 | 119 |
## load the package |
103 |
-library(protGear) |
|
120 |
+suppressWarnings(library(protGear)) |
|
104 | 121 |
``` |
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 |
... | ... |
@@ -139,17 +156,17 @@ header_gpr[1:32] |
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"), |
... | ... |
@@ -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 |
|
... | ... |
@@ -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 |
... | ... |
@@ -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", |
... | ... |
@@ -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 |
|
... | ... |
@@ -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") |
... | ... |
@@ -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} |
... | ... |
@@ -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 |
|
... | ... |
@@ -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. |
|
346 |
+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. |
|
330 | 347 |
|
331 | 348 |
```{r best_reps} |
332 | 349 |
#' @________________________________data_with_selected_best_2_CV_______________________________________ |
... | ... |
@@ -375,14 +392,13 @@ batch_vars <- list(machine="m1", day="0520") |
375 | 392 |
```{r tag_glimpse} |
376 | 393 |
tb1 <- data.frame(head(tag_file, n=10)) |
377 | 394 |
tb1 %>% |
378 |
- knitr::kable() #%>% |
|
379 |
- #kable_styling() |
|
395 |
+ knitr::kable() |
|
380 | 396 |
``` |
381 | 397 |
|
382 | 398 |
|
383 | 399 |
### Subtracting the TAG values |
384 | 400 |
|
385 |
-The function `r text_spec("tag_subtract()",color="red")` implements the TAG subtration by matching the TAG names with the corresponding TAG values. |
|
401 |
+The function `r colFmt("tag_subtract()",color="red")` implements the TAG subtration by matching the TAG names with the corresponding TAG values. |
|
386 | 402 |
|
387 | 403 |
```{r tag_subtract} |
388 | 404 |
#' @________________________________subtract_the_tag_values_______________________________________ |
... | ... |
@@ -468,7 +484,7 @@ control_antigens <- c("CommercialHumanIgG","CD4TAG") |
468 | 484 |
``` |
469 | 485 |
|
470 | 486 |
|
471 |
-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. |
|
487 |
+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. |
|
472 | 488 |
|
473 | 489 |
```{r normalised_df} |
474 | 490 |
normlise_df <- matrix_normalise(matrix_antigen, method = "vsn", array_matrix=array_matrix, |
... | ... |
@@ -481,7 +497,7 @@ normlise_df$plot_normalisation |
481 | 497 |
|
482 | 498 |
### Compare normalisation methods |
483 | 499 |
|
484 |
-On the dashboard we compare the different normalisation techniques using the `r text_spec("mutiple_plot()",color="red")` function after loading the data. |
|
500 |
+On the dashboard we compare the different normalisation techniques using the `r colFmt("mutiple_plot()",color="red")` function after loading the data. |
|
485 | 501 |
|
486 | 502 |
|
487 | 503 |
```{r compare_methods} |