Mox is down

Kaitlyn’s notebook: take down day 1

Today we started breaking down the geoduck experiment at Pt. Whitney. We turned off the Apex and cleaned and stored all probes. CO2 lines were removed from conicals and secured. We moved most of our materials back up to the lab where we organized and cleaned so that the areas are available for the hatchery workers to use and the materials are safe until Sam returns. The geoduck are still in their original heath trays however only their gravity flow is left to be removed. This task will be done tomorrow when Steven and Brent arrive. I took lots of photos. Here are a few!

 

 

Roberto’s Notebook: Differential expression Analysis

After finish the analyses using Hisat program and complemented with stringtie, the differential expression analysis in R using the libraries:
library(ballgown)
library(RSkittleBrewer)
library(genefilter)
library(dplyr)
library(devtools)
and command lines like:

pheno_data = read.csv(“/Volumes/toaster/roberto/phenodata_day30.csv”)
bg_Cragi = ballgown(dataDir = “/Volumes/toaster/roberto/Hisat_results/stringtie_results/Est_abundance/ballgown30/”, samplePattern = “Os”, pData=pheno_data)
bg_Cragi_filt = subset(bg_Cragi,”rowVars(texpr(bg_Cragi)) >1″,genomesubset=TRUE)

#To look for diff expr between thermal tolerance:

results_transcripts = stattest(bg_Cragi_filt, feature=”transcript”,covariate=”thermal.tolerance”,adjustvars = c(“family”), getFC=TRUE, meas=”FPKM”)
results_genes = stattest(bg_Cragi_filt, feature=”gene”, covariate=”thermal.tolerance”, adjustvars = c(“family”), getFC=TRUE, meas=”FPKM”)

#This is to add gene names and gene IDs to the results_transcripts data frame

results_transcripts = data.frame(geneNames=ballgown::geneNames(bg_Cragi_filt), geneIDs=ballgown::geneIDs(bg_Cragi_filt), results_transcripts)

#Then, to sort the results from the smallest P value to the largest:

results_transcripts = arrange(results_transcripts,pval)
results_genes = arrange(results_genes,pval)

#To write the results to a csv file:

write.csv(results_transcripts, “Cragi_transcript_results_D30.csv”, row.names=FALSE)
write.csv(results_genes, “Cragi_gene_results_D30.csv”, row.names=FALSE)

#To identify transcripts and genes with a q value subset(results_transcripts,results_transcripts$qvalsubset(results_genes,results_genes$qvaltropical= c(‘darkorange’, ‘dodgerblue’, ‘hotpink’, ‘limegreen’, ‘yellow’)
palette(tropical)

fpkm = texpr(bg_Cragi,meas=”FPKM”)
fpkm = log2(fpkm+1)
boxplot(fpkm,col=as.numeric(pheno_data$thermal.tolerance),las=2,ylab=’log2(FPKM+1)’)

Screen Shot 2018-08-07 at 3.24.25 PM

##As an example, the observation of differential expression of particular gene (with its isoforms):

ballgown::transcriptNames(bg_Cragi)[4295]
plot(fpkm[4295,] ~ pheno_data$thermal.tolerance, border=c(1,2), main=paste(ballgown::geneNames(bg_Cragi)[4295],’ : ‘, >ballgown::transcriptNames(bg_Cragi)[4295]),pch=19, xlab=”thermal.tolerance”, ylab=’log2(FPKM+1)’)
points(fpkm[4295,] ~ jitter(as.numeric(pheno_data$thermal.tolerance)), col=as.numeric(pheno_data$thermal.tolerance))
Gene_expression

#Then, the observation of this gene differentially expressed in a single sample:

plotTranscripts(ballgown::geneIDs(bg_Cragi)[4295], bg_Cragi, main=c(‘Gene MSTRG.3053 in sample Os15’), sample=c(‘Os15’))
Gene_and_isoforms
For this it was necessary to look for the gene id (MSTRG.3053) in matrix file and add it to the command line.

#As a las step. The visualization of expression of isoforms between resistant and susceptible families and the position of these in the genome:

plotMeans(‘MSTRG.3053’, bg_Cragi_filt,groupvar=”thermal.tolerance”,legend=FALSE)
Gene_expressed_resistantvssusceptible

I am still working in the comparison of data obtained with Hisat/stringtie and the data from Trinity.