News

• 9 2024

  BMC Bioinformatics published our collaborative work on gene correlations in single cell data spearheaded by the Lander labScott Atwood

  Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations. Read More PDF

• 6 2024

  Journal of Investigative Dermatology published our commentary on the multiomics of basosquamous carcinomaScott Atwood

  Basosquamous carcinoma (BSC) is a rare form of skin cancer defined by combined phenotypes of 2 common skin cancers: basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC). The heterogeneity of BSC provides tumors an intrinsic resistance to drugs that only target one phenotype, making standard therapy ineffective. Jussila et al (2023) utilize single-cell transcriptomics, spatial transcriptomics, and whole-exome sequencing to interrogate and spatially map phenotypic differences in a drug-resistant tumor from a patient with Gorlin syndrome. Defining the molecular mechanisms driving BSCs in this way provides insight into phenotypic switching within tumors as well as a framework to study other types of heterogeneous drug-resistant cancers. Read More PDF

• 6 2024

  Nicholas Bradbury has joined the lab! Scott Atwood

  Nick, a brilliant master's student from the Biotechnology program, has decided to pursue his graduate work in a co-mentorship between the Atwood and Dai labs. Nick received his B.S. in Microbiology from UC Davis where he worked on bifidobacteria and how gasoline leaks affect microbial ecosystems. He went on to perform research at UC San Diego where he investigated mast cells in skin immune function and received his M.S. in Biotechnology from UC Irvine where he worked on the interplay between primary cilia and psoriasis. Nick is excited to continue his interests by defining immune regulation during epidermal hyperplasia. Welcome to the lab!

• Older posts