I have RNAseq data made with selection by PolyA and other RNA-seq Total data. I want to join this data to increase the sampling within some subtypes I have few samples.
How can I normalize these two dates in just one? Is there any method or process that makes this joining of RNA-seq PolyA and Total possible?
I looked for this information in many articles that work with multiple types of data, but they don´t detail how they did.
Thanks
Joining RNA-seq data from different sample preparation methods, such as polyA and total RNA, can be challenging due to differences in the sequencing depth and representation of certain RNA species. However, there are several normalization methods that can be used to account for these differences and enable the integration of the two datasets.
One common approach is to use a normalization method that adjusts for sequencing depth, such as reads per million (RPM) or fragments per kilobase of transcript per million mapped reads (FPKM). These methods normalize the read counts based on the total number of reads in each sample, which can help account for differences in sequencing depth between the two datasets. It's important to carefully evaluate the normalization methods and software tools to determine which is most appropriate for your specific dataset and research question.
Normalizing RNA-seq data generated from PolyA and Total RNA can be challenging, as they have different properties that can affect their library preparation and sequencing, resulting in differences in the sequencing depth and composition of the resulting reads.
One common method for normalizing RNA-seq data is to use the reads per kilobase per million mapped reads (RPKM) or transcripts per million (TPM) approach. These methods normalize for sequencing depth and gene length and provide a measure of the expression level of each gene relative to other genes within the same sample. However, RPKM and TPM normalization may not be suitable for comparing expression levels across samples with different RNA types, as they assume that the total transcriptome is being sequenced uniformly, which may not be true in the case of Total RNA.
Another method that can be used for normalizing RNA-seq data is to use the trimmed mean of M values (TMM) normalization approach, which is based on the assumption that most genes are not differentially expressed across the samples being compared. TMM normalization estimates a scaling factor for each sample based on the relative expression of a set of housekeeping genes, which are assumed to be stably expressed across all samples.
A more advanced method for normalizing RNA-seq data is to use a normalization technique that takes into account the differences between the RNA types, such as the "DESeq2" package in R, which uses a generalized linear model to estimate size factors that adjust for the differences in sequencing depth and composition between PolyA and Total RNA samples.
In summary, while RPKM or TPM normalization can be used for normalizing RNA-seq data generated from PolyA and Total RNA, it may not be suitable for comparing expression levels across different RNA types. TMM normalization can be used as a general normalization method, while advanced techniques such as DESeq2 can be used to specifically address the normalization of RNA-seq data generated from different RNA types.
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