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BEGIN:VEVENT
UID:5eb4b397469445468fc337724d32b15c
DTSTAMP:20240116T180418Z
DTSTART:20240424T170000Z
DTEND:20240426T210000Z
LOCATION:Virtual meeting. Webconferencing URL will be sent out after registration.
SUMMARY:RNASeq Data Analysis at OSC
DESCRIPTION:OSC will host a threeday virtual workshop "RNASeq Data Analysis at OSC" on April 24\, 25\, & 26\, 2024\, from 1:00 to 5:00pm. The workshop will focus on analysis of RNASeq data using the R Statistical and Programming environment available at OSC. Topics that will be covered include: Preprocessing of RNASeq 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\, handson exercises\, and open discussion.
XALTDESC;FMTTYPE=text/html:OSC will host a threeday virtual workshop "RNASeq Data Analysis at OSC" on April 24\, 25\, &\; 26\, 2024\, from 1:00 to 5:00pm. The workshop will focus on analysis of RNASeq data using the R Statistical and Programming environment available at OSC. \;
Topics that will be covered include:

Preprocessing of RNASeq 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\, handson exercises\, and open discussion. \;
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