tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))

tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))

log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM <- as.data.frame(log.tdata.FPKM)

log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])

log.tdata.FPKM.sample.info <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.subset <- log.tdata.FPKM[,colMeans(log.tdata.FPKM != 0) > 0.5] 

log.tdata.FPKM.sample.info.subset <- cbind(log.tdata.FPKM.subset,tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info.subset <- log.tdata.FPKM.sample.info.subset %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.sample.info.subset.small.intestine <- log.tdata.FPKM.sample.info.subset %>% rownames_to_column() %>% filter(Tissue == "Small Intestine") %>% column_to_rownames()

Wrangle Data

I will use Mouse IDs, Treatment, and Time to keep track of the values in the matrices. All other covariates will be discarded.

# Set rownames by mouse ID and tissue
rownames(log.tdata.FPKM.sample.info.subset.small.intestine) <- paste0(rownames(log.tdata.FPKM.sample.info.subset.small.intestine),":", log.tdata.FPKM.sample.info.subset.small.intestine$Time, ":", log.tdata.FPKM.sample.info.subset.small.intestine$Treatment)

# Discard covariates from columns 17333-17336
log.tdata.FPKM.sample.info.subset.small.intestine <- log.tdata.FPKM.sample.info.subset.small.intestine[,-(17333:17335)]

head(log.tdata.FPKM.sample.info.subset.small.intestine[,1:5])
##                 ENSMUSG00000000001 ENSMUSG00000000028 ENSMUSG00000000031
## A006:4 wks:None           3.984158           1.754404         0.16551444
## A015:96 hrs:2DG           4.654532           1.680828         0.06765865
## A024:4 wks:2DG            4.597037           1.930071         0.12221763
## A033:4 wks:2DG            4.557659           1.821318         0.27763174
## A042:4 wks:None           4.325721           1.912501         0.11332869
## A051:4 wks:None           4.350020           1.888584         0.36464311
##                 ENSMUSG00000000037 ENSMUSG00000000049
## A006:4 wks:None          0.1484200          0.7839015
## A015:96 hrs:2DG          0.3646431          0.5306283
## A024:4 wks:2DG           1.0152307          1.3480731
## A033:4 wks:2DG           0.1484200          0.9707789
## A042:4 wks:None          0.1043600          1.1052568
## A051:4 wks:None          0.2926696          0.9122827

Check Data for Missing Values

WGCNA will have poor results if the data have too many missing values. I checked if any metabolites fall into this category.

log.tdata.FPKM.sample.info.subset.small.intestine.missing <- missing(log.tdata.FPKM.sample.info.subset.small.intestine)
cat("logFPKM: ", goodSamplesGenes(log.tdata.FPKM.sample.info.subset.small.intestine.missing, verbose=0)$allOK, "\n")
## logFPKM:  TRUE

WGCNA reports that all data are good! I now use hierarchical clustering to detect any obvious outliers. I did not see any particularly egregious outliers.

sampleclustering(log.tdata.FPKM.sample.info.subset.small.intestine.missing)

saveRDS(log.tdata.FPKM.sample.info.subset.small.intestine.missing, here("Data","Small Intestine","log.tdata.FPKM.sample.info.subset.small.intestine.missing.WGCNA.RData"))

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org