Molik, D. | bioRxiv Science

bioRxiv2018

The Effects of Normalization, Transformation, and Rarefaction on Clustering of OTU Abundance

Introduction Introduction Methods Discussion Data and Code Availability References Metagenomic clustering presents a unique opportunity to associate and understand communities. Working with Operational Taxonomic Units (OTUs), however, often requires a strategy for handling OTUs that may be over or under represented in a given sample, which is thought of as \"erroneous\". PCR amplification, for example, is known to sometimes non-linearly over-represent more common species Gonzalez et al. 2012. Strategies dealing with this bias include Normalization, Rarefaction, and Log Transformation. Here, we examine how methods to handle potential outlier observations affect de novo estimation of groups using both clustering and matrix factorization methods.\n\nWhile log transformations may affect parametric tests ...

ecology↗

bioRxiv2017

SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation) Identifies Markers of Resistance to Targeted BRAF Inhibitors in Melanoma Cell Populations

Single-cell RNA-Seqs (scRNA-Seq) unprecedented cellular resolution at a genome wide scale enables us to address questions about cellular heterogeneity that are inaccessible using methods that average over bulk tissue extracts. However, scRNA-Seq datasets also present additional challenges such as high transcript dropout rates, stochastic transcription events, and complex population substructures. Here, we present SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation): a robust method for scRNA-Seq analysis that provides quantitative statistical metrics at each step of the scRNA-Seq analysis pipeline including metrics for: the determination of the number of clusters present, the likelihood that each cell belongs to a given cluster, and the association of each gene marker in determining cluster membership. Comparing SAKE to multiple single-cell analysis methods shows that most methods perform similarly across a wide range cellular contexts, with SAKE outperforming these methods in the case of large complex populations. We next applied the SAKE algorithms to identify drug-resistant cellular populations as human melanoma cells respond to targeted BRAF inhibitors. Single-cell RNA-Seq data from both the Fluidigm C1 and 10x Genomics platforms were analyzed with SAKE to dissect this problem at multiple scales. Data from both platforms indicate that BRAF inhibitor resistant cells can emerge from rare populations already present before drug application, with SAKE identifying both novel and known markers of resistance. In addition, we compare integrated genomic and transcriptomic markers to show that resistance can arise stochastically within multiple distinct clonal populations.

genomics↗