Biology subjects
Publications and source records attributed to Lina Zheng.
We have assessed the performance of seven normalization methods for single cell RNA-seq using data generated from dilution of RNA samples. Our analyses showed that methods considering spike-in ERCC RNA molecules significantly outperformed those not considering ERCCs. This work provides a guidance of selecting normalization methods to remove technical noise in single cell RNA-seq data.
Recently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.
Many disease-related genotype variations (GVs) reside in non-gene coding regions and the mechanisms of their association with diseases are largely unknown. A possible impact of GVs on disease formation is to alter the spatial organization of chromosome. However, the relationship between GVs and 3D genome structure has not been studied at the chromosome scale. The kilobase resolution of chromosomal structures measured by Hi-C have provided an unprecedented opportunity to tackle this problem. Here we proposed a network-based method to capture global properties of the chromosomal structure. We uncovered that genome organization is scale free and the genomic loci interacting with many other loci in space, termed as hubs, are critical for stabilizing local chromosomal structure. Importantly, we found that cancer-specific GVs target hubs to drastically alter the local chromosomal interactions. These analyses revealed the general principles of 3D genome organization and provided a new direction to pinpoint genotype variations in non-coding regions that are critical for disease formation.