StoreyLab/superSeq

Stars
13
Rank 1,512,713 (Top 30 %)
Language
R
License
MIT License
Created over 5 years ago
Updated over 5 years ago

StoreyLab/superSeq

StoreyLab

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Assessing the increase in power from increasing read depth in a given RNA-Seq study

qvalue

R package to estimate q-values and false discovery rate quantities.

biobroom

Tidy up computational biology objects

reading

A collection of papers and books that Storey Lab members should read and others may find useful

terastructure

TeraStructure is a new algorithm to fit Bayesian models of genetic variation in human populations on tera-sample-sized data sets (10^12 observed genotypes, i.e., 1M individuals at 1M SNPs). This package provides a scalable, multi-threaded C++ implementation that can be run on a single computer.

edge

R package for identifying differentially expressed genes from genome-wide gene expression profiling studies.

subSeq

Subsampling of high-throughput sequencing count data

popkin

R package to estimate kinship and FST from SNP data

lfa

logistic factor analysis

alstructure

An R package that implements the ALStructure algorithm for estimating the admixture model of genetic population structure

bnpsd

R package to model and simulate admixed populations

gcatest

genotype conditional association test

fFDR

Functional FDR package

gcat

This package has been renamed gcatest: https://github.com/StoreyLab/gcatest

sparta

Dividing RNA-Seq reads based on their ancestral genotype

human-differentiation-manuscript

Details on the data analysis for "New kinship and F_ST estimates reveal higher levels of differentiation in the global human population" by Ochoa and Storey

superSeq-manuscript

ALStructure-manuscript

Data and code utilized in "A likelihood-free estimator of population structure bridging admixture models and principal components analysis", by Cabreros and Storey

factorial-MA-RS

Supplementary Materials for the manuscript "A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays" by Robinson, Wang, and Storey