Autism and Polychlorinated Biphenyls Exposure

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Autism and Polychlorinated Biphenyls Exposure

Postby cpere117 » Fri Nov 17, 2017 9:01 pm

Hello all,

First off let me introduce myself my name is Christian Perez and I'm a doctorate student in the Environmental Health Sciences department. My major adviser is Dr. Quentin Felty and we currently are focusing on the effect of molecular determinants of brain vascular disorders due to exposure too polychlorinated biphenyls. This forum is ideal for my research because it will be grounded in computational biology, Bayesian Network gene analysis for generation of novel genetic pathways involved with ID3. Anyways I look forward to working with all of you moving forward over time and also in contributing my help to your own research topics. I listed the activities and data collection processes in an action plan below and I will report on each Friday to update my weekly progress.

1) Over the past few weeks, I have downloaded datasets from GEO Omnibus where either a population study or blood cell line study where PCB exposure was studied in the development of brain disorders or vascular-related disorders. The datasets I have compiled so far focus primarily on raw gene expression microarray data but I do intend to expand to RNA datasets as I develop further as a doctorate student.
2) The datasets are four in total at the moment and are primarily concerned with the same cohort of children in Slovakia exposed to PCB's in two distinct regions: Michalovce (High PCB exposure) and Svidnik (Low PCB exposure). The exposure levels were determined by the average output of PCB's from nearby chemical treatment plants in the area. Their ascension numbers are as follows: GSE22868, GSE22667, GSE32420, GSE288051.
3) After downloading the raw data I had to normalize it into z-scores and discretized excel files. In order, to do this I utilized Efraim Gonzalez help in R and ran his code for cleaning GEO datasets across my four GSE studies. Due to all of my studies having the same GPL platform (GPL570) it was not necessary to gather a list of common genes.
4) Then I had to determine which genes were significantly differentially expressed genes across the high and low exposure samples from my datasets. In order to test for this, I used a Pearson Correlation Analysis using Correl function on Excel for high and low PCB exposure effect on gene expression across my samples.
5) Finally, I tried to eliminate confounders by grouping my samples into High PCB exposure and Low PCB Exposure groups delineated first by gender, and then by age (time point of blood sample collection).
This is where my data progress currently stands and in the future, I would like to compare my significant differentially expressed genes determined by the Pearson correlation analysis (>0.5 correlation score) to data from lab Chip-Sequence ID3 experiments using immunoprecipitation. I know I wrote a fairly large amount so please feel free to ask me for any clarifications or study suggestions. Thank you for your time and attached below are my pearson correlation analysis example for females.
Attachments
GSE22667aftexcel.txt
(3.23 MiB) Downloaded 250 times
GSE22667dscrt.txt
(1.13 MiB) Downloaded 228 times
GSE22667zscore.txt
(3.65 MiB) Downloaded 224 times
FEMALE CORRELATE SOT.xlsx
(20.47 MiB) Downloaded 223 times
cpere117
 
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