Combining P-values

TFisher

We have come a long way from the original simple p-value integration methods of Fisher and Stouffer. Hong Zhang, a talented grad student from the Worcester Polytechnic
Institute, and his colleagues have developed a novel method, called TFisher, for dealing with p-value integration in a wide range of test scenarios.

I quote from their abstract, available here: https://arxiv.org/abs/1801.04309

For testing a group of hypotheses, tremendous p-value combination methods have been developed and widely applied since 1930’s. Some methods (e.g., the minimal p-value) are optimal for sparse signals, and some others (e.g., Fisher’s combination) are optimal for dense signals. To address a wide spectrum of signal patterns, this paper proposes a unifying family of statistics, called TFisher, with general p-value truncation and weighting schemes. Analytical calculations for the p-value and the statistical power of TFisher under general hypotheses are given. Optimal truncation and weighting parameters are studied based on Bahadur Efficiency (BE) and the proposed Asymptotic Power Efficiency (APE), which is superior to BE for studying the signal detection problem. A soft-thresholding scheme is shown to be optimal for signal detection in a large space of signal patterns. When prior information of signal pattern is unavailable, an omnibus test, oTFisher, can adapt to the given data. Simulations evidenced the accuracy of calculations and validated the theoretical properties. The TFisher tests were applied to analyzing a whole exome sequencing data of amyotrophic lateral sclerosis. Relevant tests and calculations have been implemented into an R package TFisher and published on the CRAN.

The methods are implemented in R and available on CRAN:

https://cran.r-project.org/web/packages/TFisher/index.html

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RNASeq aligners

books aligned.jpgI would say the match has now four competitors:

Tophat2:

  • Pros: the classic, the first universally used, still widely adopted in pipelines all over the World, basically people keep using it so their new results are comparable to the old ones
  • Cons: slow (several CPU hours per alignment on a human genome with 10M reads), limited to 4Gbases genomes (so, no complex metatranscriptomics for him) and on their very website they say to use HISAT2

STAR:

  • Pros: super, wicked fast, the standard used by ENCODE and the big RNASeq projects
  • Cons: uses a LOT of RAM, like really a lot (64GB for a human index)

HISAT2:

  • Pros: fast and low RAM requirements. If you start from scratch, this is the aligner to pick
  • Cons: it’s still new and so many people don’t trust it yet

Salmon/Kallisto:

These are actually not strictly aligners, but rather transcript counters. I put them together for simplicity, but they are different softwares

  • Pros: high speed and low RAM requirements. Ideal for quick RNA-Seq gene expression measurements
  • Cons: they cannot do de novo transcript detection, sad. They don’t produce counts, which are the expected input for many downstream analysis tools. However, some tools are starting to accept Salmon/Kallisto outputs (in R you can use the transcript abundance import package tximport)

So… USE HISAT! 😀

Quantifying RNA-Seq Transcripts

About ten years ago, when RNA-Seq was young, we struggled to make sense of the huge quantity of data that came out of Next-Generation Sequencers. The RNA-Seq pipelines were founded on the simple scheme:

Reads -> Alignments -> Quantification

The most popular RNA-Seq alignment tool, Tophat (now Tophat2) was actually built on the Bowtie aligner to focus on transcribed genomic regions (the Transcriptome), with the optional feature of aligning reads in the whole Genome, for de-novo transcript discovery.

Continue reading “Quantifying RNA-Seq Transcripts”