Congrats to Xiaoqiao Chen for her new paper on using active support vector machines (activeSVM) to discover minimal gene sets

Check out the new paper here!

Xiaoqiao’s method assembles minimal lists of highly informative genes using an iterative SVM approach. Each iteration uses only a small numbers of cells and genes for computation. She uses a clever ‘active’ strategy to prioritize cells and genes that are most commonly misclassified, thus ensuring that selected genes improve classification on outliers. Because only a small number of cells and genes are used at each step, the process is very memory efficient, and allows her to compute over huge datasets (>1M cells) in a little over an hour (compared to days for other algorithms).


Congrats to David and Tatyana for publishing their paper on profiling AAV tropism with single-cell RNA-seq

Paper can be found at:

What’s cool about this paper: They developed a single-cell RNA sequencing (scRNA-seq) pipeline for in vivo characterization of barcoded rAAV pools at high resolution. They were able to faithfully identify cell-type specific tropisms in a pool of up to 7 variants simultaneously, paving the way for future highly multiplexed screens.


Congrats to Tatyana Dobreva, David Brown , Jeff Park and Matt Thomson for their paper on single-cell profiling of capillary blood!

Fresh off the press at Scientific Reports!

What’s cool about this paper: capillary blood sampling devices (TAP devices) allowed the team to collect blood at high temporal frequencies (morning and nigh). They used this system to study diurnal variability in a cell type-specific and patient-specific manner.


Single-cell Superusers Meeting: Dr. Colt Egelston and Dr. Weihua Guo (City of Hope)

Dr. Colt Egelston and Dr. Weihua Guo (City of Hope) present at the single-cell superusers meeting about immuno-oncology, breast cancer, and signatures of T cell exhaustion.
Talk title: Utilization of single-cell transcriptomics to interrogate immune cell composition in clinical tumor samples
Single-cell RNA sequencing technologies offer unique and powerful solutions towards greater understanding of cellular biology. Here we will discuss implementation of a successful single-cell RNA sequencing analysis pipeline for evaluation of clinical patient samples. We will discuss recent advances in understanding composition and function of tumor infiltrating lymphocytes from clinical patient samples at the City of Hope using our current approaches. Finally, we will discuss harnessing single-cell transcriptomic derived data towards evaluating responders in immunotherapy-based clinical trials and designing new immunotherapy approaches via advanced computational approaches.