mRNA EDA: PCA and correlation plots of postnormalized expression data, BT642Year2
This document has last been compiled on 2021-12-14 21:03:34.
## Reading in metadata from file: results/BT642Year2/data/root_meta.txt
## Reading in data from file: results/BT642Year2/data/root_normCounts.txt## Total number of root samples: 133
## Total number of genes in root samples: 23916
## No additional bad samples to remove (probably removed during normalization)
## Removing the 13 samples identified as not part of the main experiment, from the root samples
## Variance filter for root samples: Remove genes in the bottom 0.25 quantile of variability
## Total number of genes after limiting to high variable genes : 17937
## After filtering root samples: 120 samples, 17937 genes## Reading in metadata from file: results/BT642Year2/data/leaf_meta.txt
## Reading in data from file: results/BT642Year2/data/leaf_normCounts.txt## Total number of leaf samples: 125
## Total number of genes in leaf samples: 22181
## No additional bad samples to remove (probably removed during normalization)
## Removing the 13 samples identified as not part of the main experiment, from the leaf samples
## Variance filter for leaf samples: Remove genes in the bottom 0.25 quantile of variability
## Total number of genes after limiting to high variable genes : 16635
## After filtering leaf samples: 112 samples, 16635 genesNote that when we are using NORMALIZED data (leaf and root separately), we are using the leaf (root) data file that is the default of bringInDataFunction, the NORMALIZED fpkm values; for this data (leaf and root separately) we are filtering by expression and also on variance within those subsets of samples.
Note that the comments for Year2 say that we find mistaken genotypes like Year1. But on rerunning this code fresh, they do not appear, so it is likely this was some initial mismatch of using Year1 data by accident? It’s unclear, since the barcodes were clearly from Year2, but we can’t recreate that result, so we are not switching any samples, despite comments here.
RLE plots
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PCA of Expression Values
PCA on Leaves
PCA on Roots
Correlation Matrices of mRNA data
Correlation of all samples, Leaf
Correlation of all samples, Root
