Update README.md

README.md

@@ -195,7 +195,7 @@ The processed data provided so far opens a wide range of analytical possibilitie
 Two common basic analyses are presented here as examples.
 
 ## 1. Checking data structure and replicability in the data set. Multivariate analysis and correlogram.
-With **sRNA.correlogram_plotter.r** it is possible to analyse the table **total_table.size_24.genes.txt** to plot the correlation between particular sRNA expression values across all the samples. A scatterplot in the lower diagonal panel is presented and Pearson correlation coefficients in the upper panel (Figure 2B). The script **sRNA.ordination_analyser.r** performs a multivariate analysis using the same  input table to evaluate the similarities between samples. By default the samples are ordinated with Nonmetric multidimensional scaling (NMDS) as implemented in the R library vegan (Figure 2C). Principal component analysis (PCA) and redundancy analysis (RDA) are also available as alternative ordination approaches.
+With **sRNA.correlogram_plotter.r** it is possible to analyse the table **total_table.size_24.genes.txt** to plot the correlation between particular sRNA expression values across all the samples. This is a good way to check for deviant samples and assess replicability. A scatterplot in the lower diagonal panel is presented and Pearson correlation coefficients in the upper panel (Figure 2B). The script **sRNA.ordination_analyser.r** performs a multivariate analysis using the same  input table to evaluate the similarities between samples. By default the samples are ordinated with Nonmetric multidimensional scaling (NMDS) as implemented in the R library vegan (Figure 2C). Principal component analysis (PCA) and redundancy analysis (RDA) are also available as alternative ordination approaches.
 
 ![This is an image](/images/figure1.png)