Differential-transcript-expression
unawaz1996
2023-03-26
Last updated: 2023-05-11
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Introduction
Set-up
Transcript-level counts were retrieved using the
catchSalmon() function from the edgeR package. Once
retrieved, the counts underwent rigourous QC.
Removal of outlier sample
According to the quality control analysis, it was revealed that one of the samples was not clustering with its condition group. Investigation into GC content, transcript length and library size revealed no contribution of these factors. To ensure we are getting results that are not skewed we will remove the sample from the transcript level analysis.
Principal component analysis of transcript level data. PCA was performed on log2 transformed CPM after filtering lowly expressed genes. The PCA shows that samples are clustering close to their conditions based on PC1, however one of the samples of the UPF3A OE in UPF3B KD cell line (sample 223), seems to deviate from its condition group and the rest of the data, so needs to be further investigated
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| 421e1d5 | unawaz1996 | 2023-05-10 |
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| 421e1d5 | unawaz1996 | 2023-05-10 |
Analysis
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| 421e1d5 | unawaz1996 | 2023-05-10 |
[1] 0.01177187
Biological coeficcient of variation plot against the average abundance of each transcript. The plot shows the square-root estimates of the common, trended and tagwise NB dispersions.
| Version | Author | Date |
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| 421e1d5 | unawaz1996 | 2023-05-10 |
Quasi-likelihood dispersion aganist gene abundance. Estimates are shown for raw, tended and squeezed dispersions
| Version | Author | Date |
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| 421e1d5 | unawaz1996 | 2023-05-10 |
Min. 1st Qu. Median Mean 3rd Qu. Max.
14.32 14.32 14.32 14.32 14.32 14.32 
Distribution of results
Fold change distributions
Distribution of p-values and log2Fold changes across conditions after running edgeR
| Version | Author | Date |
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| 421e1d5 | unawaz1996 | 2023-05-10 |
MA plot of comprisons of KD/OE with Controls. Pink highlights significantly upregulated transcripts whereas blue highlights significantly downregulated transcripts
| Version | Author | Date |
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| 421e1d5 | unawaz1996 | 2023-05-10 |
Ratio of up/down differentiallt expressed transcripts
UPF3B transcripts
UPF3B transcripts with NMD features
NIF in upregulated transcripts in UPF3B
| Version | Author | Date |
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| 421e1d5 | unawaz1996 | 2023-05-10 |
UPF3A transcripts
UPF3A transcripts with NMD features
NIF in upregulated transcripts in UPF3B
Comparison of DEGs with DETs
UPF3 dKD
Comparison of DEGs with DETs
Transcripts with NIFs
NIF in upregulated transcripts in UPF3B
UPF3A OE
UPF3A OE in UPF3B KD
Are DET shared between UPF3B KD and UPF3A KD?
In UPF3B KD, there are 1860 upregulated genes aand 1328 downregulated genes
sign.lfc
-1 1
816 1504 In UPF3B KD, there are 1504 upregulated genes and 816 downregulated genes
What is the distribution between transcripts in UPF3B KD and UPF3A OE?
** All conditions - transcript and gene level comparison
** Capacity to do genes with isoforms with different NIFs - try in UPF3B
** Is NMD a cell-type specific thing? –? Maybe UPF1 work with brain coexp networks
Any transcript that is expression - 40k transcripts that have multiple transcripts and have a look at the CPM - Just do it in UPF3B - average of the three samples
R version 4.2.2 Patched (2022-11-10 r83330)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
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[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats4 tools stats graphics grDevices utils
[8] datasets methods base
other attached packages:
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