## ----style, echo=FALSE, results='asis', message=FALSE------------------------- BiocStyle::markdown() knitr::opts_chunk$set(tidy = FALSE, warning = FALSE, message = FALSE) ## ----setup-------------------------------------------------------------------- library(ewceData) ## ----------------------------------------------------------------------------- ensembl_transcript_lengths_GCcontent <- ensembl_transcript_lengths_GCcontent() mouse_to_human_homologs <- mouse_to_human_homologs() all_mgi_wtEnsembl <- all_mgi_wtEnsembl() all_mgi <- all_mgi() all_hgnc_wtEnsembl <- all_hgnc_wtEnsembl() all_hgnc <- all_hgnc() example_genelist <- example_genelist() tt_alz <- tt_alzh() tt_alzh_BA36 <- tt_alzh_BA36() tt_alzh_BA44 <- tt_alzh_BA44() ctd <- ctd() schiz_genes <- schiz_genes() hpsd_genes <- hpsd_genes() rbfox_genes <- rbfox_genes() id_genes <- id_genes() cortex_mrna <- cortex_mrna() hypothalamus_mrna <- hypothalamus_mrna() alzh_gwas_top100 <- alzh_gwas_top100() mgi_synonym_data <- mgi_synonym_data() ## ----setup2------------------------------------------------------------------- library(ggplot2) library(cowplot) theme_set(theme_cowplot()) ## ----------------------------------------------------------------------------- cortex_mrna_dt <- cortex_mrna() gene="Necab1" cellExpDist = data.frame(e=cortex_mrna_dt$exp[gene,], l1=cortex_mrna_dt$annot[ colnames(cortex_mrna_dt$exp),]$level1class) ggplot(cellExpDist) + geom_boxplot(aes(x=l1,y=e)) + xlab("Cell type") + ylab("Unique Molecule Count") + theme(axis.text.x = element_text(angle = 90, hjust = 1))