Biology subjects

Lun, A.

Publications and source records attributed to Lun, A..

3 recordsLinked to original sources

bioRxiv2018

Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data

Applying a log-transformation to normalized expression values is one of the most common procedures in exploratory analyses of single-cell RNA sequencing (scRNA-seq) data. Normalization removes systematic biases in sequencing coverage between cells, while the log-transformation ensures that downstream computational procedures operate on relative rather than absolute differences in expression. We show that the log-transformation can introduce systematic errors when cells vary in sequencing coverage, leading to spurious non-zero differences in expression and artificial population structure in simulations. We observe similar effects in real scRNA-seq data where the difference in transformed values between groups of cells is not an accurate proxy for the log-fold change. We provide some practical recommendations to overcome this effect and analytically derive an expression for a larger pseudo-count that controls the transformation-induced error to a specified threshold.

bioinformatics↗

bioRxiv2018

Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data

Droplet-based single-cell RNA sequencing protocols have dramatically increased the throughput and efficiency of single-cell transcriptomics studies. A key computational challenge when processing these data is to distinguish libraries for real cells from empty droplets. Existing methods for cell calling set a minimum threshold on the total unique molecular identifier (UMI) count for each library, which indiscriminately discards cell libraries with low UMI counts. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that our method has greater power than existing approaches for detecting cell libraries with low UMI counts, while controlling the false discovery rate among detected cells. We also apply our method to real data, where we show that the use of our method results in the retention of distinct cell types that would otherwise have been discarded.

bioinformatics↗

bioRxiv2017

Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis

Loss-of-function (LOF) methods, such as RNA interference (RNAi), antisense oligonucleotides or CRISPR-based genome editing, provide unparalleled power for studying the biological function of genes of interest. When coupled with transcriptomic analyses, LOF methods allow researchers to dissect networks of transcriptional regulation. However, a major concern is nonspecific targeting, which involves depletion of transcripts other than those intended. The off-target effects of each of these common LOF methods have yet to be compared at the whole-transcriptome level. Here, we systematically and experimentally compared non-specific activity of RNAi, antisense oligonucleotides and CRISPR interference (CRISPRi). All three methods yielded non-negligible offtarget effects in gene expression, with CRISPRi exhibiting clonal variation in the transcriptional profile. As an illustrative example, we evaluated the performance of each method for deciphering the role of a long noncoding RNA (lncRNA) with unknown function. Although all LOF methods reduced expression of the candidate lncRNA, each method yielded different sets of differentially expressed genes upon knockdown as well as a different cellular phenotype. Therefore, to definitively confirm the functional role of a transcriptional regulator, we recommend the simultaneous use of at least two different LOF methods and the inclusion of multiple, specifically designed negative controls.

genomics↗