BME 580.447/647: Computational Stem Cell Biology Learn about mechanisms underpinning multipotency and self-renewal of stem cells. Emphasis on seminal studies and bleeding edge technologies, and the critical contributions of computational approaches to both. Homeworks involve analysis of real single cell omics data and final project requires development of novel analysis approaches in Python.
Academic Job Search Seminar Part of a series of seminars to demystify the process of applying for academic positions. My presentations cover a lot of 'nuts and bolts' details. The first presentation has tips on the application process, the second covers the interview(s).
Each box below points to resources associated with one of our papers. Some papers describe new computational methods, some papers describe omic data, and some papers describe both new computational methods and new omic data. Some boxes have links to web applications that we created to broaden accessibility to our data or methods. More recently, we have started to create repositories that contain the analyses steps required to reproduce the results in our papers.
scRNA-seq of Drosophila embryos at stages 10-12 (20,585 cells) and stages 13-16 (42,727 cells). There are four samples in total.
Epoch Epoch infers gene regulatory networks (GRNs) that are dynamic, in that their topologies change over time, from scRNA-seq data. It is fast and performs well based on the Beeline GRN benchmarking platform.
PySCN enables comparions of embryo models such as embryoids, gastruloids, embryoid bodies, to in vivo embryos with scRNA-seq. PySCN includes curated reference data, and it allows the user to perform classification and enrichment analysis with curated, development-specific gene signatures.
SingleCellNet is a computational tool that classifies scRNA-Seq data across platforms and across species. It transforms query and reference data with the top-scoring pair, and then uses a Random forest for classification.
Embryoid body transcriptional states
In this paper, we described the transcriptional states in mouse embryoid body differentiation at bulk and single cell levels. We generated bulk RNA-seq for day 0, 2, 4, and 6. And we generated scRNA-seq for days 4 and 6.
Bulk RNA-Seq CellNet protocol
A protocol for applying the CellNet method to bulk RNA-seq data. This protocol starts with raw sequencing reads so that they are processed in the same way as the training data.
Deconstructing transcriptional heterogeneity in pluripotent stem cells
We used single-cell molecular profiling of mouse pluripotent stem cells subjected to a range of perturbatio factors to infer mechanisms that contribute to pluripotency and self-renewal.
CellNet: Network Biology Applied to Stem Cell Engineering
We designed a network biology computational method (CellNet) to assess the fidelity of cell fate engineering and to generate hypotheses for improving cell fate engineering protocols.
Dissecting Engineered Cell Types and Enhancing Cell Fate Conversion via CellNet
We used CellNet-predictions to improve B cell to macrophage direct conversion. CellNet also uncovered an unanticipated intestinal program in induced hepatocytes (iHeps), validated by long-term functional engraftment of mouse colon by iHeps.
Transcriptional landscape of hematopoietic stem cell ontogeny
Transcriptional profiling of hematopoietic progenitors from the AGM, Placenta, Yolk Sac, fetal liver and bone marrow, as well as ESC-derived hematopoietic stem-like cells. Cells were sorted with surface markers that enrich functionally for hematopoietic repopulation.
The impact of copy number variation on local gene expression in mouse hematopoietic stem and progenitor cells.
We used high-density aCGH to measure DNA copy number variation in the genomes of 19 commonly used inbred mouse strains. Based on association between CNV occurrence and expression in cis, we estimate that up to 28% of strain-dependent expression variation is associated with copy number variation in hematopoietic stem and progenitor cells.
We invented wuHMM, an algorithm based on Hidden Markov Models for calling DNA copy number variants from array comparative genomic hybridization (aCGH) data. wuHMM takes advantage of SNP data to infer regions of high sequence divergence to reduce the false positives.