Listing of RNA-seq Normalization Methods
The normalization of RNA-seq data is to eliminate unavoidable technical biases in experiments and biological variations between samples to ensure the comparability of gene expression data. Here are some common RNA-seq data normalization methods:
1. RPKM/FPKM (Reads/Fragments Per Kilobase of transcript per Million mapped reads):
Considers the effect of transcript length and sequencing depth.
2. TPM (Transcripts Per Million):
Similar to FPKM, but performs length normalization before normalizing transcript abundance.
3. CPM (Counts Per Million):
Normalizes the read counts of each sample to a per million mapped reads basis.
4. TMM (Trimmed Mean of M-values):
Calculated using the edgeR package, used for correcting compositional effects between samples.
5. Quantile normalization:
Adjusts the expression distribution of all samples to be the same, giving them identical statistical properties.
6. DESeq/DESeq2:
Uses a negative binomial distribution model to normalize read counts, calculating changes in gene expression.
7. Upper Quartile normalization:
Normalizes counts by adjusting the upper quartile of samples.
8. Z-score normalization:
Calculates the standard score (z-score) for each gene across all samples.
9. GC-content normalization:
Corrects for the influence of GC content on sequencing coverage.
10. Batch effects normalization:
Methods like Combat are used to correct for batch effects in experiments.
Different normalization methods are suitable for different datasets and analysis goals, and choosing the appropriate method is crucial for obtaining accurate and reproducible results.
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