
Similar Threads  
Thread  Thread Starter  Forum  Replies  Last Post 
Advice on timeseries RNAseq analysis tool  pkstarstorm05  Bioinformatics  0  03092017 08:08 PM 
Patient Tutor to teach Time Series Cell Cycle RNA Seq. analysis to construct coexpre  tfh4  Academic/NonProfit Jobs  0  02102017 10:45 PM 
Comparative Time Series Analysis of RNASeq data?  ashuchawla  Bioinformatics  8  07252013 01:12 PM 
EdgeR GLM linear regression of time series  bsuac6  Bioinformatics  3  03282013 03:50 PM 
How to analyse timeseries data with edgeR  ca85  RNA Sequencing  0  10062011 06:04 AM 

Thread Tools 
02132020, 05:21 AM  #1 
Junior Member
Location: Argentina Join Date: Feb 2020
Posts: 1

RNASeq Time series analysis in EdgeR
Hello,
I have an RNASeq experiment from insect antennae across different data points (0,2,4,6 and 8 days) with 7 replicates per data point (except t6 with 6 replicates). I am analysing using EdgeR to identify differentially expressed genes. I have used the following EdgeR script in my analysis: 1) I created the DGEList object, data normalization and created the design matrix of my experiment Code:
samples_matrix group < factor(c('t0','t0','t0','t0','t0','t0','t0','t2','t2','t2','t2','t2','t2','t2','t4','t4','t4','t4','t4','t4','t4','t6','t6','t6','t6','t6','t6','t8','t8','t8','t8','t8','t8','t8')) y < DGEList(counts=samples_matrix, group=group) y < calcNormFactors(y) design < model.matrix(~0+group, data=y$samples) colnames(design) < levels(y$samples$group) 2) I estimated the dispersion and fitted to Quasilikehood ratio F model Code:
y < estimateDisp(y,design) fit < glmQLFit(y,design, robust=TRUE) Code:
my.contrasts1 < makeContrasts(t2vst0=t2t0, t4vst2=t4t2, t6vst4=t6t4, t8vst6=t8t6, levels=design) my.contrasts2 < makeContrasts(t2vst0=t2t0, t4vst0=t4t0, t6vst0=t6t0, t8vst0=t8t0, t4vst2=t4t2, t6vst2=t6t2, t8vst2=t8t2, t6vst4=t6t4, t8vst4=t8t4, t8vst6=t8t6, levels=design) Code:
res1 < glmQLFTest(fit, contrast=my.contrasts1) res_est1 < topTags(res1, n=Inf, adjust.method="BH", sort.by="PValue") res2 < glmQLFTest(fit, contrast=my.contrasts2) res_est2 < topTags(res2, n=Inf, adjust.method="BH", sort.by="PValue") The information in both "rest_est" objects was "Coefficient: LR test on 4 degrees of freedom", however, the number of comparisons were 4 and 10 and then the degrees of freedom has to be different. When I check rest_est tables, observed that logCPM, F, PValue and FDR values are the same in both approaches and then the differentially expressed genes obtained using decideTestsDGE was also the same (5066 not sig and 6120 were sig). I do not know if I have something wrong in my script or I missed some step in my analysis. Besides, I would like to know which approach would be more correct, because including all the possible time comparisons increases the number of comparisons and it will affect the statistical analysis (more corrections will be necessary) and maybe I lost some genes with small changes. Thanks a lot for your help Jose 
Tags 
differentially expressed, edger, glm, time point data 
Thread Tools  

