souporcell Robust clustering of single cell RNAseq by genotype and ambient RNA inference without reference genotypes, bioRxiv, 2019-07-14
Methods to deconvolve single-cell RNA sequencing (scRNAseq) data are necessary for samples containing a natural mixture of genotypes and for scRNAseq experiments that multiplex cells from different donors1. Multiplexing across donors is a popular experimental design with many benefits including avoiding batch effects2, reducing costs, and improving doublet detection. Using variants detected in the RNAseq reads, it is possible to assign cells to the individuals from which they arose. These variants can also be used to identify and remove cross-genotype doublet cells that may have highly similar transcriptional profiles precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA in the system. Ambient RNA is caused by cell lysis prior to droplet partitioning and is an important confounder of scRNAseq analysis3. Souporcell is a novel method to cluster cells using only the genetic variants detected within the scRNAseq reads. We show that it achieves high accuracy on genotype clustering, doublet detection, and ambient RNA estimation as demonstrated across a wide range of challenging scenarios.
biorxiv bioinformatics 0-100-users 2019Increasing the efficiency of long-read sequencing for hybrid assembly with k-mer-based multiplexing, bioRxiv, 2019-06-25
AbstractHybrid genome assembly has emerged as an important technique in bacterial genomics, but cost and labor requirements limit large-scale application. We present Ultraplexing, a method to improve per-sample sequencing cost and hands-on-time of Nanopore sequencing for hybrid assembly by at least 50%, compared to molecular barcoding while maintaining high assembly quality (Quality Value; QV ≥ 42). Ultraplexing requires the availability of Illumina data and uses inter-sample genetic variability to assign reads to isolates, which obviates the need for molecular barcoding. Thus, Ultraplexing can enable significant sequencing and labor cost reductions in large-scale bacterial genome projects.
biorxiv bioinformatics 100-200-users 2019Towards a gold standard for benchmarking gene set enrichment analysis, bioRxiv, 2019-06-19
AbstractBackgroundAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected data sets and biological reasoning on the relevance of resulting enriched gene sets. However, this is typically incomplete and biased towards the goals of individual investigations.ResultsWe present a general framework for standardized and structured benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization, and detection of relevant processes. This framework incorporates a curated compendium of 75 expression data sets investigating 42 different human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GOKEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods on the benchmark compendium, identifying significant differences in (i) runtime and applicability to RNA-seq data, (ii) fraction of enriched gene sets depending on the type of null hypothesis tested, and (iii) recovery of the a priori defined relevance rankings. Based on these findings, we make practical recommendations on (i) how methods originally developed for microarray data can efficiently be applied to RNA-seq data, (ii) how to interpret results depending on the type of gene set test conducted, and (iii) which methods are best suited to effectively prioritize gene sets with high relevance for the phenotype investigated.ConclusionWe carried out a systematic assessment of existing enrichment methods, and identified best performing methods, but also general shortcomings in how gene set analysis is currently conducted. We provide a directly executable benchmark system for straightforward assessment of additional enrichment methods.Availability<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpbioconductor.orgpackagesGSEABenchmarkeR>httpbioconductor.orgpackagesGSEABenchmarkeR<jatsext-link>
biorxiv bioinformatics 100-200-users 2019Modular and efficient pre-processing of single-cell RNA-seq, bioRxiv, 2019-06-17
AbstractAnalysis of single-cell RNA-seq data begins with the pre-processing of reads to generate count matrices. We investigate algorithm choices for the challenges of pre-processing, and describe a workflow that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near-optimal in speed and memory. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses.
biorxiv bioinformatics 200-500-users 2019CLIJ GPU-accelerated image processing for everyone, bioRxiv, 2019-06-09
AbstractGraphics processing units (GPU) allow image processing at unprecedented speed. We present CLIJ, a Fiji plugin enabling end-users with entry level experience in programming to benefit from GPU-accelerated image processing. Freely programmable workflows can speed up image processing in Fiji by factor 10 and more using high-end GPU hardware and on affordable mobile computers with built-in GPUs.
biorxiv bioinformatics 200-500-users 2019RNA velocity and protein acceleration from single-cell multiomics experiments, bioRxiv, 2019-06-07
AbstractThe simultaneous quantification of protein and RNA makes possible the inference of past, present and future cell states from single experimental snapshots. To enable such temporal analysis from multimodal single-cell experiments, we introduce an extension of the RNA velocity method that leverages estimates of unprocessed transcript and protein abundances to extrapolate cell states. We apply the model to four datasets and demonstrate consistency among landscapes and phase portraits.
biorxiv bioinformatics 100-200-users 2019