BEGIN:VCALENDAR VERSION:2.0 PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VEVENT UID:7f62c6de-8714-47dd-85af-3449e9d8bef6 DTSTAMP:20230316T145306Z DTSTART:20230426T170000Z DTEND:20230428T210000Z LOCATION:Virtual meeting. Webconferencing URL will be sent out after registration. SUMMARY:RNA-Seq Data Analysis at OSC DESCRIPTION:OSC will host a three-day virtual workshop "RNA-Seq Data Analysis at OSC" on April 26\, 27\, & 28\, 2023\, from 1:00 to 5:00pm. The workshop will focus on analysis of RNA-Seq data using the R Statistical and Programming environment available at OSC. Topics that will be covered include: Preprocessing of RNA-Seq count data Fitting multivariate Generalized Linear Models to normalized count data Hypothesis testing to identify differentially expressed genes Who Should Attend: While any OSC user is welcomed to attend\, materials covered in this workshop will be relevant to researchers (PIs\, research scientists\, postdocs\, staff\, graduate students) involved in biological and biomedical research.Prerequisites: Prior knowledge of R Statistical program\, or a basic understanding of programming concepts such as variables and objects. A basic understanding of statistical concepts such as probability distributions and linear regression models. Method of Delivery: Slide presentation\, hands-on exercises\, and open discussion. X-ALT-DESC;FMTTYPE=text/html:
OSC will host a three-day virtual workshop "RNA-Seq Data Analysis at OSC" on April 26\, 27\, &\; 28\, 2023\, from 1:00 to 5:00pm. The workshop will focus on analysis of RNA-Seq data using the R Statistical and Programming environment available at OSC. \;
Topics that will be covered include:
Preprocessing of RNA-Seq count data
Fitting multivariate Generalized Linear Models to normalized count data
Hypothesis testing to identify differentially expressed genes
Who Should Attend: While any OSC user is welcomed to attend\, materials covered in this workshop will be relevant to researchers (PIs\, research scientists\, postdocs\, staff\, graduate students) involved in biological and biomedical research.
Prerequisites: \;
Prior knowledge of R Statistical program\, or a basic understanding of programming concepts such as variables and objects.
A basic understanding of statistical concepts such as probability distributions and linear regression models.
Method of Delivery: Slide presentation\, hands-on exercises\, and open discussion. \;
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