This R Markdown aims to provide a brief summary of all diagnostic plots for your TREx experiment.
FASTQ files are available for download upon request.
Users receiving files from RSCshare are advised to delete all files once they have secure copied them to their own respective drives.
A copy of your data will be securely archived on our end and may incur a fee for future retrievals.
Pre-Processing
The raw fastq reads were first processed with fastp package to:
- Trim for low quality reads;
- 2 color chemistry bias (next-seq);
- Trim for noisy short fragments;
- Trim for adapter sequence;
The filtered reads were then aligned to GRCh38 reference genome with ENSEMBL annotations
The multiqc html (separate file), summarises the alignment statistics along with the summary of raw counts generated via STAR
For default parameters used within TREx pipeline’s, please refer to the code modules available on Github at this link.
Counts Distribution
Post-normalization, the medians should be consistent across samples and more similar between biological replicates.
geneBodyCoverage
A good library should indicate little to no bias across the entire gene body.
Sample Clustering
An euclidean distance is computed between samples, and the dendrogram is built upon the Ward criterion. We expect this dendrogram to group replicates and separate biological conditions.
Principal Components Analysis
Another way of visualizing the experiment variability is to look at the first principal components of the PCA. On this figure, the first principal component (PC1) is expected to separate samples from the different biological conditions, meaning that the biological variability is the main source of variance in the data.
MA-Plots
