17 votes
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Confirm success or failure of RNA-Seq normalization

You should use box plots and PCA plot. Let's take a look at the RUV paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4404308/ Before normalization and after UQ normalization: Libraries do ...
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  • 2,649
12 votes
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How can I extract normalized read count values from DESeq2 results?

The normalized counts themselves can be accessed with counts(dds, normalized=T). Now as to what the baseMean actually means, that will depend upon whether an "...
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  • 19.2k
9 votes
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Normalization methods with RNA-Seq ERCC spike in?

You may consider using RUVSeq. Here is an excerpt from the 2013 Nature Biotechnology publication: We evaluate the performance of the External RNA Control Consortium (ERCC) spike-in controls and ...
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9 votes
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How to read and interpret a gene expression quantification file?

The first column contains Ensembl gene identifiers, and the suffix is a version number that can be used to track changes to the gene annotations over time. From the Ensembl Stable IDs documentation: ...
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8 votes
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How to apply upperquartile normalization on RSEM expected counts?

You can use the quantile function in base R to get the value of a particular quantile (e.g. 0.75 for the upper quartile). This can then be used as a factor for ...
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  • 11.7k
8 votes

Confirm success or failure of RNA-Seq normalization

Visual inspection with histograms, boxplots, or some other distribution visualization is the way to go. Prior to normalization, your abundances may look something like this. Post-normalization, they ...
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6 votes
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What are some good practices to follow during EPIC DNA methylation data analysis?

EPIC data can be processed in the same manner as the previous iteration of methylation array data from Illumina (450k). This means that starting with .idat files, normalization should be performed (...
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  • 397
6 votes

How can I extract normalized read count values from DESeq2 results?

It depends what you mean by “normalised”. As Devon said, the normalized = TRUE argument to the count function gives you ...
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5 votes
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Normalizing microarray data for clustering heat map

You see negative values with your function because you're setting the average of each row to 0 and its standard deviation to 1. In general, I would trust a standard normalization method (rma in this ...
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  • 19.2k
5 votes
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Analyzing Illumina Counts

The counts files for GSE89225 is the output of HTSeq-count as a large matrix. Unless you are developing a differential expression package yourself you should not attempt to directly use this. Rather, ...
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  • 19.2k
5 votes

Normalization for two bulk RNA-Seq samples to enable reliable fold-change estimation between genes

You have only two samples? You aren't going to be able to draw strong conclusions from that no matter what you do. Clever statistics don't work without replicates.
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  • 1,662
5 votes
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Calculating most abundant transcript from RNA-Seq data

Transcript abundance quantification is a tricky topic since a read often could belong to several transcripts, so any "count" is a best guess as to which transcript it actually originates ...
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4 votes
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qPCR: Why is fold change and standard deviation calculated after transformation?

The functions look correct, but calculate a few by hand and ensure they match. One thing I should note is that the subtraction of the Ct values usually happens before an average is made, since ...
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  • 19.2k
4 votes

Normalization methods with RNA-Seq ERCC spike in?

We have added ERCC spike-ins to all our RNASeq data, just in case other people might find it useful in the future. However, I have never used it in my own analyses because I can't think of a ...
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4 votes
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Drawbacks of upper quartile normalization for scRNA-seq data

My primary concern with using the top ~1% or so in upper quantile normalization is that it's going to be prone to the same robustness issues that RPKM/FPKMs run in to. That is, if for whatever ...
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  • 19.2k
4 votes

Input normalization in ChIP-seq

I strongly suggest that you not try to come up with your own package for this when things like CSAW already exist in bioconductor and provide a number of useful ...
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  • 19.2k
4 votes
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How to quantile normalization on RNA seq counts

On google there are many tutorials about quantile normalzation, for example here. In that tutorial they made a function to calculate quantile normalization. Here an example with that function on your ...
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  • 3,541
4 votes

Doubt about using TPM for statistics

This is a common question, and the answer is while you can calculate this, it's not statistically robust. You're likely to arrive at false conclusions. Why is this? Because while TPM is something you ...
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  • 1,142
4 votes
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Normalization methods for single cell RNA sequencing that take read count into account

The default normalisation methods of DESeq2 have a quantile-like normalisation process (based on raw read counts), and this default process should be able to handle datasets with different read depths....
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  • 11.7k
3 votes

How to quantile normalization on RNA seq counts

Ma be CQN from Bioconductor will be useful, though it doesn't perform just quantile normalisation.
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  • 321
3 votes

How to normalise scRNASeq data for differential expression analysis

I would suggest using a likelihood ratio test for differential expression using logistic regression with batch as a latent variable. In Seurat you can do: ...
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  • 674
3 votes
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RNAseq: Z score, Intensity, and Resources

It depends on what test or analysis you want to do, whether you need intensities (expression values) or z-scores. If you want to do statistical analysis, such as finding differentially expressed ...
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  • 3,541
3 votes

Order of batch effects removal, data imputation and library size normalization in scRNA-seq data

MAGIC assumes input data has been both library-size normalized, and either log or sqrt transformed prior to imputation (see also: MAGIC tutorial). Additionally, any graph-based methods (MAGIC, PHATE, ...
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3 votes
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Large dataset normalization for PCA

I normalized a high throughput dataset for a school project using DESeq library using the script bellow. The code is based on a lesson I had. My goal was determine ...
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  • 158
3 votes

Normalization for two bulk RNA-Seq samples to enable reliable fold-change estimation between genes

It's not a good idea to do tpm normalisation prior to differential expression analysis, because the actual read counts are useful to determine shot noise and statistical significance. DESeq2 includes ...
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  • 11.7k
3 votes

Normalization for two bulk RNA-Seq samples to enable reliable fold-change estimation between genes

What I have generally done in the past is to process the data using voom in the limma package for bulk RNASeq. Inside ...
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  • 776
3 votes

Discordance in gene signature behavior between bulk and single-cell RNASeq

One explanation could be that your mapping of clusters to timepoints is not accurate. There are other methods you could look at for doing this, for example scMap, scPred, or Seurat v3 (disclosure: I ...
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  • 674
3 votes
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Cluster is split in 2-3 locations on tsne plot - Suerat

Your cluster labels come from graph clustering implemented in the FindClusters() function. The resulting clusters are then visualised with a 2D tSNE plot (via ...
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  • 3,432
3 votes
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Differential expression analysis for a subset of transcripts

It's safer to import everything. You'll want all the data for normalization and dispersion estimates.
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  • 1,662

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