update table presents in Rmarkdown

inst/IDEA/DESeqAnalysis.Rmd

@@ -48,7 +48,8 @@ were selected for differential expression analysis.
 ##Differential Expression Table
 After analysis, a table containing information of all diffientially expressed genes is presented with interactive options. Implication of nouns in header is explained in Table 1.
 Note that in different packages, same noun in header can have different implication. For example, *p*-values in DESeq are obtained by Wald test, but in edgeR *p*-values are obtained by Fisher's exact test.
-
+```{r, echo=FALSE}
+htmltools::HTML('
 <div align="center">
 <table cellpadding="10" cellspacing="0" border="1" frame=hsides rules=all style="border-color: #000000">
         <tr>
@@ -86,6 +87,8 @@ Note that in different packages, same noun in header can have different implicat
 </table>
 Table 1: Implication of headers of Differential Expression Table in DESeq2
 </div>
+')
+```
 
 ##Variance Estimation
 The variance estimates plot is for checking the result of dispersion estimates adjustment. Specifically, in DESeq2, variance estimation is plotted by executing ```plotDispEsts()``` which is built-in in the package.

inst/IDEA/Dataexploration.Rmd

@@ -60,7 +60,8 @@ if(as.character(plist[[10]]) == "uqua"){
 For normalization, four methods are provided, including the Reads per Kilobase per Million Reads (RPKM) [[6](#ref6)], the Upper Quartile (UQ) [[13](#ref13)], and the Trimmed Mean of M values (TMM) [[14](#ref14)]. User can also perform data exploration without any normalization method by choosing "None" in the drop-down menu in Normalization Method Panel. Summarized details of each normalization methods are listed in Table 1. In this report the "normalized counts" is taken as the expression unit, but inputted count data can be normalized, or not, as decided by users.   
 *Note that if user did not upload a gene length file for normalization, a default length of 1000bp/gene is used.*    
 In this case, normalization was set as `r plist[[10]]`.   
-
+```{r, echo=FALSE}
+htmltools::HTML('     
 <div align="center">
 Table 1: Summarized details for normalization methods provided in Data Exploration module<br/>
 <table cellpadding="5" cellspacing="0" border="1" frame=hsides rules=all style="border-color: #000000">
@@ -91,7 +92,8 @@ Table 1: Summarized details for normalization methods provided in Data Explorati
         </tr>
 </table>
 </div>
-
+')
+```
 
 ##Data Exploration Plots
 ###Samples Boxplot

inst/IDEA/NOIseqAnalysis.Rmd

@@ -57,7 +57,8 @@ In this case, experimental design was stated as **`r plist[[1]][1]`**. Condition
 ##Advanced Options   
 NOISeq provides three methods for normalization: RPKM, TMM and UQ, whose basic information is listed in Table 1. In this case,  is adopted as normalization method.    
 In this case, normalization method was set as **`r plist[[1]][[3]]`**.   
-
+```{r, echo=FALSE}
+htmltools::HTML('  
 <div align="center">
 Table 1 Normalization methods in NOISeq<br/>
 <table cellpadding="5" cellspacing="0" border="1" frame=hsides rules=all style="border-color: #000000">
@@ -83,7 +84,8 @@ Table 1 Normalization methods in NOISeq<br/>
         </tr>
 </table>
 </div>
-
+')
+```
 #Analysis Result   
 
 ##Differential Expression Table   

inst/IDEA/PoissonseqAnalysis.Rmd

@@ -61,7 +61,10 @@ No advanced options are provided in PoissonSeq analysis module.
 ##Differential Expression Table   
 A table containing information of all differentially expressed genes is presented with interactive options. Intepretation of all headers is explained in Table 1.    
 Note that in different packages, same header can have different implication. For example, p-values in DESeq are obtained by Wald test, but in edgeR p-values are obtained by Fisher's exact test.    
-
+
+
+```{r, echo=FALSE}
+htmltools::HTML('   
 <div align="center">
 Table 1: Interpretation of headers of differential expression table in PoissonSeq<br/>
 <table cellpadding="5" cellspacing="0" border="1" frame=hsides rules=all style="border-color: #000000">
@@ -95,7 +98,8 @@ Table 1: Interpretation of headers of differential expression table in PoissonSe
         </tr>
 </table>
 </div>   
-
+')
+```
 ##Heat Map of Differential Expressed Genes   
 
 Heat map can graphically display the differential expression table, and each square (pixel) represents the value of a feature in a sample and colored accordingly. Here, heat map of differential expressed features is plotted via R package pheatmap. Features are arranged in columns (samples) and rows (features) as in the original data matrix. Up-regulated differential expression features are colored red in heat map, while the down-regulated colored green. Hierarchical clustering results of features and samples are shown in dendrogram on the left and upper side of heat map, respectively.    

inst/IDEA/SAMseqAnalysis.Rmd

@@ -58,6 +58,8 @@ In this case, number of resampling was defined as **`r as.character(plist[[1]][3
 A table containing information of all differentially expressed genes is presented with interactive options. Intepretation of all headers is explained in Table 1.    
 Note that in different packages, same header can have different implication. For example, p-values in DESeq are obtained by Wald test, but in edgeR p-values are obtained by Fisher's exact test.    
 
+```{r, echo=FALSE}
+htmltools::HTML('  
 <div align="center">
 Table 2: Interpretation of headers of differential expression table in SAMseq (samr)<br/>
 <table cellpadding="5" cellspacing="0" border="1" frame=hsides rules=all style="border-color: #000000">
@@ -84,7 +86,9 @@ Table 2: Interpretation of headers of differential expression table in SAMseq (s
 </table>
 *T-statistic: A method used to test coefficient, which is the ratio of the coefficient to the standard error.
 </div>   
-
+')
+```
+
 ##Heat Map of Differential Expressed Genes
 
 Heat map can graphically display the differential expression table, and each square (pixel) represents the value of a feature in a sample and colored accordingly. Here, heat map of differential expressed features is plotted via R package pheatmap. Features are arranged in columns (samples) and rows (features) as in the original data matrix. Up-regulated differential expression features are colored red in heat map, while the down-regulated colored green. Hierarchical clustering results of features and samples are shown in dendrogram on the left and upper side of heat map, respectively.    

inst/IDEA/combinationAnalysis.Rmd

@@ -46,6 +46,8 @@ In combination analysis, experiment design is set as standard procedure of packa
 
 In this case, experiment type was stated as **`r as.character(plist[[1]][1])`**. And condition **`r as.character(plist[[1]][[2]])[1]`** and condition **`r as.character(plist[[1]][[2]])[2]`** were selected for differential expression analysis.
 
+```{r, echo=FALSE}
+htmltools::HTML('  
 <div align="center">
 Table 1: Package availability for different experiment design types<br/>
 <table cellpadding="5" cellspacing="0" border="1" frame=hsides rules=all style="border-color: #000000">
@@ -87,7 +89,8 @@ Table 1: Package availability for different experiment design types<br/>
         </tr>
 </table>
 </div>   
-
+')
+```
 ##Normalization Method
 Since packages typically have different choices of normalization methods, to combine the results across packages, counts data inputted by user is first normalized using method chosen in Data Exploration (Table 2) before being analyzed by packages respectively.   
 In this case, normalization method is **`r as.character(plist[[1]][[3]])`**.