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Hello, I am currently using Partek Genomic Suite Software to analyze smallRNA Seq data for miRNA analysis. I unfortunately do not have biological replicates of my two experimental groups (disease and control) and was wondering what statistical options I have when it comes to detecting differential expression. Partek automatically utilizes an ANOVA, so without any replicates I get a ? for each p-value. I know that going simply on fold-change is not enough so I would like to employ some statistical test to get a better idea of which of my miRNAs are significant.
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Hi,
It'll be very hard to expect any biological significance for any of your detected miRNAs without the presence of biological replicates (if you are planning to publish this data, you'll need biological replicates). Check the guidelines from the ENCODE project regarding the "best practices" for RNA-seq (most ideas should be the same for miRNA-seq) experiments.
As far as getting some p-values from the data you already have (no biological replicates), you could try using DESeq:
Just be cautious in making any significant claims about your experiment based on this.
I hope this helps! -
Hi leeor,
In Partek Genomics Suite, during the Quantification step and in the absence of replicates a p-value is calculated for differential expression using a Log Likelihood Ratio test, but this is a very basic statistic as there is no variance component (which comes from having replicates).
Our tool for Differential Expression Analysis uses an ANOVA model which, in the absence of replicates, can calculate ratios and fold-changes, but not p-value as it cannot estimate variance.
If you have no chance to obtain replicates your only option is to perform the Quantification in Genomics Suite, filter the table on p-value (from the Log Likelihood Ratio test) and read count (raw or normalised), and validate expression via qPCR/dPCR or similar.
However at Partek we always recommend the use of true biological replicates as this is the only way to have confidence in your data.
Good luck!
Kind regards,
Scott (European FAS @ Partek)Comment
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Thanks for the help! Yes, I did initially plan on having biological replicates but my future steps would involve experimental validation .. so I'll take your suggestions on filtering on the p-value from the Log Likelihood Ratio test.Comment
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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