Better results?

I discussed some of my concerns using the CV filtering with Steven. We decided to look at the dataset I was using to make my boxplots when we found that some samples were missing way more transitions than others. The annotated dataset can be found here.

Figure 1. Poor technical replication of certain samples. There are 111 transitions, and some samples were missing close to all of them.

The number of missing transitions in samples ranged from 4 to 100. Because there seemed to be certain samples with poor technical replication, I decided to remove samples missing more than half of the transitions from my dataset. I removed samples O06, O103, O122, O128, O14, O145, O49, O52, and O71 and saved this as a new file.

Finally, I remade my boxplots using data with NAs instead of zeros so it wouldn’t skew my graphs. I also found this handy dandy way to plot the individual data points on top of the boxplot!

boxplot(boxplotData[,i] ~ boxplotData$Site, xlab = "Sites", ylab = "Abundance") #Create the boxplot
stripchart(boxplotData[,i] ~ boxplotData$Site, vertical = TRUE, method = "jitter", add = TRUE, pch = 20, col = 'blue') #Add each data point

My final boxplots can be found in this folder. Hopefully this will provide some insight into my data issues!