SMLG (Statistical Machine Learning Group) Discussion Forum

Good evening everyone,
Continuing with the downstream analysis, I will represent the log2 transformed data into discrete numbers. The design I chose includes numbers such as (+1, 0, -1), from which +1 is up-regulated, 0 has no change, and -1 is down-regulated. Make sure to leave a comment if you encounter any inconvenience opening or understanding how the data has been processed. Thanks

Juan Morales

Hello everybody!,
Let continue analyzing our data. On my previous post, I was able to identify the up and down regulated genes from the data from the log2 transformations. We then discretized the log2 transformed dataset to (-1, 0, and 1) which reflected in up, no change and down regulation.
Today, I will begin the statistical comparison involving the treatment conditions compared with the control reference sample data using Linear Models for Microarray Analysis. Attached you will find the summary statistics along with the reference used in order to follow along with my procedure.
This step is important because we need a statistical comparison of genes between our arsenic and control samples for different time points (8, 24, 48,and 96 hours).

Differentially Expressed Genes --> Control vs Treatment <-- (8,24,48,96)

Limma is a package from the Bioconductor project which provides the researcher with tools to compare our data. The limma (Linear Models for Microarray Analysis) R package has emerged as one of the most widely used statistical tests for identifying differentially expressed genes. It handles and applies multiple-testing corrections on P-values to help correct for the occurrence of false positives.

Best,

Juan Morales

Hello everyone,

Today's post contains the introduction of my research question:

-->How many nuclear respiratory factor 1 (nrf1) target genes are present in zebrafish microarray from metal studies? <--

In order to achieve my goal, I downloaded a list containing 11280 target genes of the NRF1 transcription factor predicted using known transcription factor binding site motifs from the TRANSFAC Predicted Transcription Factor Targets dataset -->https://amp.pharm.mssm.edu/Harmonizome/gene_set/NRF1/ENCODE+Transcription+Factor+Targets<--
I then formatted an excel sheet to isolate and match the NRF1 targets in the arsenic study identifying possible NRF1 targets in different times.

In the attached files the reader will be able to view a list of NRF1 targets along with the statistically correlated DEGs found by using the General Linear Models for Microarray Analysis summary table. The table has a filter option which matches the corresponding NRF1 known target gene with the genes found in the arsenic treated and control samples. More, the statistical comparison of genes between arsenic and control samples for different time points was performed using the heterocedastic t-test and the resulting P values were adjusted for Benjamini and Hochberg false discovery rate (FDR). The FDR adjustment is introduced in order to minimize the number of false positive genes that can be identified by chance because of multiple hypothesis testing of large datasets.

Please open and review the excel table along with the references used to understand the procedure used in this design.

Thank you,

Juan Morales

Good afternoon everybody,
Today I want to share the methods I am developing towards my experimental design using zebrafish microarray raw data. Attached I included step by step downstream analysis using data from NCBI Gene Expression Omnibus outlining important concepts for data interpretation.
If you have any questions feel free to leave a comment below.

Thanks,

Juan Morales