Monthly Archives: September 2017

STAR Methods

Here is the STAR Methods format (Structured, Transparent, Accessible Reporting) introduced last year by Cell, marketed as a structure that “promotes transparent reporting of experimental design and
methodological details.” Details are in the links below.

Editorial: http://www.cell.com/cell/fulltext/S0092-8674(16)31072-8 Instructions: http://www.cell.com/star-authors-guide
Website: http://www.cell.com/star-methods

Two CBB events: Rafael Irizarry, Computational Biology and Bioinformatics speaker on 10-4-17

Dr. Rafael Irizarry from Harvard University will be the guest speaker.

Title: “Overcoming Bias and Batch Effects in single cell and bulk RNAseq Data”

Date: Wednesday, October 4, 2017

Time: 4:00 seminar 5:00 refreshments

Place: BML Auditorium, 310 Cedar Street

Hosted by: Steven Kleinstein, PhD

Flyer is attached for your review.

cbb_seminar_series_Oct.doc.pdf

farnam pi_gerstein diskusage

total usage: 368 TB of 446 TB

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Biostatistics Seminar: 9/26/17 – 12:00 Noon – LEPH 115

https://medicine.yale.edu/events/ysph.aspx?from=2017-09-26&to=2017-10-03#single-event_9691

Good morning,

Please join the Biostatistics Seminar scheduled for Tuesday, September 26, 2017 at 12:00 Noon in LEPH 115.

Yale calendar Link: YSPH Biostatistics Seminar: “Constructing Tumor-Specific Gene Regulatory Networks Based on Samples with Tumor Purity Heterogeneity”

Thank you,

Speaker: Pei Wang, PhD

Institution: Icahn School of Medicine at Mount Sinai

Time & Place: 12:00 Noon in LEPH 115, 60 College St.

11:45 AM Lunch in LEPH 108

Title: “Constructing Tumor-Specific Gene Regulatory Networks Based on Samples with Tumor Purity Heterogeneity”

Abstract:

Tumor tissue samples often contain an unknown fraction of normal cells. This problem well known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between normal and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose TSNet a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample. The advantage of TSNet over existing methods ignoring TPH is illustrated through extensive simulation examples. We then apply TSNet to estimate tumor specific co-expression networks based on breast cancer expression profiles. We identify novel co-expression modules and hub structure specific to tumor cells.
BIS Seminar Notice Sept 26_2017.pdf