The reproducibility of research and the misinterpretation of P values, bioRxiv, 2017-06-01

AbstractWe wish to answer this question If you observe a “significant” P value after doing a single unbiased experiment, what is the probability that your result is a false positive?. The weak evidence provided by P values between 0.01 and 0.05 is explored by exact calculations of false positive risks.When you observe P = 0.05, the odds in favour of there being a real effect (given by the likelihood ratio) are about 31. This is far weaker evidence than the odds of 19 to 1 that might, wrongly, be inferred from the P value. And if you want to limit the false positive risk to 5 %, you would have to assume that you were 87% sure that there was a real effect before the experiment was done.If you observe P =0.001 in a well-powered experiment, it gives a likelihood ratio of almost 1001 odds on there being a real effect. That would usually be regarded as conclusive, But the false positive risk would still be 8% if the prior probability of a real effect were only 0.1. And, in this case, if you wanted to achieve a false positive risk of 5% you would need to observe P = 0.00045.It is recommended that the terms “significant” and “non-significant” should never be used. Rather, P values should be supplemented by specifying the prior probability that would be needed to produce a specified (e.g. 5%) false positive risk. It may also be helpful to specify the minimum false positive risk associated with the observed P value.Despite decades of warnings, many areas of science still insist on labelling a result of P < 0.05 as “statistically significant”. This practice must contribute to the lack of reproducibility in some areas of science. This is before you get to the many other well-known problems, like multiple comparisons, lack of randomisation and P-hacking. Precise inductive inference is impossible and replication is the only way to be sure,Science is endangered by statistical misunderstanding, and by senior people who impose perverse incentives on scientists.

biorxiv scientific-communication-and-education 200-500-users 2017

Bias, robustness and scalability in differential expression analysis of single-cell RNA-seq data, bioRxiv, 2017-05-29

AbstractBackgroundAs single-cell RNA-seq (scRNA-seq) is becoming increasingly common, the amount of publicly available data grows rapidly, generating a useful resource for computational method development and extension of published results. Although processed data matrices are typically made available in public repositories, the procedure to obtain these varies widely between data sets, which may complicate reuse and cross-data set comparison. Moreover, while many statistical methods for performing differential expression analysis of scRNA-seq data are becoming available, their relative merits and the performance compared to methods developed for bulk RNA-seq data are not sufficiently well understood.ResultsWe present conquer, a collection of consistently processed, analysis-ready public single-cell RNA-seq data sets. Each data set has count and transcripts per million (TPM) estimates for genes and transcripts, as well as quality control and exploratory analysis reports. We use a subset of the data sets available in conquer to perform an extensive evaluation of the performance and characteristics of statistical methods for differential gene expression analysis, evaluating a total of 30 statistical approaches on both experimental and simulated scRNA-seq data.ConclusionsConsiderable differences are found between the methods in terms of the number and characteristics of the genes that are called differentially expressed. Pre-filtering of lowly expressed genes can have important effects on the results, particularly for some of the methods originally developed for analysis of bulk RNA-seq data. Generally, however, methods developed for bulk RNA-seq analysis do not perform notably worse than those developed specifically for scRNA-seq.

biorxiv bioinformatics 100-200-users 2017

Continuity and admixture in the last five millennia of Levantine history from ancient Canaanite and present-day Lebanese genome sequences, bioRxiv, 2017-05-27

The Canaanites inhabited the Levant region during the Bronze Age and established a culture which became influential in the Near East and beyond. However, the Canaanites, unlike most other ancient Near Easterners of this period, left few surviving textual records and thus their origin and relationship to ancient and present-day populations remain unclear. In this study, we sequenced five whole-genomes from ~3,700-year-old individuals from the city of Sidon, a major Canaanite city-state on the Eastern Mediterranean coast. We also sequenced the genomes of 99 individuals from present-day Lebanon to catalogue modern Levantine genetic diversity. We find that a Bronze Age Canaanite-related ancestry was widespread in the region, shared among urban populations inhabiting the coast (Sidon) and inland populations (Jordan) who likely lived in farming societies or were pastoral nomads. This Canaanite-related ancestry derived from mixture between local Neolithic populations and eastern migrants genetically related to Chalcolithic Iranians. We estimate, using linkage-disequilibrium decay patterns, that admixture occurred 6,600-3,550 years ago, coinciding with massive population movements in the mid-Holocene triggered by aridification ~4,200 years ago. We show that present-day Lebanese derive most of their ancestry from a Canaanite-related population, which therefore implies substantial genetic continuity in the Levant since at least the Bronze Age. In addition, we find Eurasian ancestry in the Lebanese not present in Bronze Age or earlier Levantines. We estimate this Eurasian ancestry arrived in the Levant around 3,750-2,170 years ago during a period of successive conquests by distant populations such as the Persians and Macedonians.

biorxiv genetics 0-100-users 2017

 

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