Hello,

Please help me with understanding my boxplot. 

Here is the boxplot, I got for my RNA-Seq data.

![Box Plot][1]

My data is 

```r
head(rawCountTable)
                   con-1  con-2    a-1    a-2    b-1    b-2     c-1    c-2    d-1    d-2
ENSMUSG0000000000     0      0      0      0      0      0       0      0      0      0
ENSMUSG00000000028    854    937   1143   1029    912    856    809    754    513    520
ENSMUSG00000000031 822918 817451 716860 691396 763705 829274 838094 819312 717935 730879
```

The code for Boxplot is below: 

```r
pseudoCount = log2(rawCountTable + 1)
df = melt(pseudoCount, variable.name = "Samples", 
      value.name = "count") # reshape the matrix 
df = data.frame(df, Condition = substr(df$Samples, 1, 4))
```

Here is my code for the density plot. 

```r
ggplot(df, aes(x = count, colour = Samples, fill = Samples)) +
  ylim(c(0, 0.17)) +
  geom_density(alpha = 0.2, size = 1.25) +
  facet_wrap(~ Condition) + theme(legend.position = "top") +
  xlab(expression(log[2](count + 1)))
```

The density Plot is

![Density Plot][2]

So, my question is I want to know how to interpret these plots? How is my data quality? If you can recommend me an article about understanding these plots and assess my data, I would appreciate it. 

Thank you very much!

 [1]: https://i.ibb.co/TtQRfw6/Boxplot.png
 [2]: https://i.ibb.co/gMdSsqN/Density-Plot.png
I don't think those plots are necessarily too informative about quality. If you want a general idea about the quality of the sequencing reads, use a program like [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The alignment statistics from your aligner will then give you a good idea of the complexity of your library. If you plan on running differential expression on your data, you can generate PCA and heatmap plots, which will be a good first indicator of replicate concordance, and from those plots you can sometimes start seeing the difference between conditions. The [DESeq2](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) is a good resource for making these plots.