| n_clust <- 1:(max(as.numeric(Idents(seurat_obj)))) | |
| mcFindMarkers <- function(i){ | |
| ident1 <- i | |
| ident2 <- n_clust[n_clust != i] | |
| table <- FindMarkers(seurat_obj, | |
| ident.1 = ident1, ident.2 = ident2, only.pos = TRUE) | |
| table$Gene.name.uniq <- rownames(table) | |
| table$cluster <- rep(i, nrow(table)) | |
| return(table) | |
| } |
| Collections of scripts harvested mainly from Pete, but also picked up from the forums | |
| TOC | |
| Accessing dynamic measurements.groovy - Most annotation measurements are dynamically created when you click on the annotation, and | |
| are not accessible through the standard getMeasurement function. This is a way around that. | |
| Affine transformation.groovy - access more accurate measurements for the affine transformation used in the image alignment (m5/m6+) | |
| Alignment of local cells.groovy - check neighborhood for similarly aligned cells |
| # ------------------------------------------------------------------ | |
| # Updating TCGAbiolinks to work with GDC data | |
| # -------------------------------------------------------------------- | |
| # Install last version from the github (this is a development version) | |
| devtools::install_github("BioinformaticsFMRP/TCGAbiolinks") | |
| library(TCGAbiolinks) | |
| ####################### Working harmonized data ########################### | |
| # Data.category: clinical and biospecimen |
| #' Convert counts to transcripts per million (TPM). | |
| #' | |
| #' Convert a numeric matrix of features (rows) and conditions (columns) with | |
| #' raw feature counts to transcripts per million. | |
| #' | |
| #' Lior Pachter. Models for transcript quantification from RNA-Seq. | |
| #' arXiv:1104.3889v2 | |
| #' | |
| #' Wagner, et al. Measurement of mRNA abundance using RNA-seq data: | |
| #' RPKM measure is inconsistent among samples. Theory Biosci. 24 July 2012. |
This is a tutorial I have presented for the class Genomics and Systems Biology at the University of Chicago. In this course the students learn about study design, normalization, and statistical testing for genomic studies. This is meant to introduce them to how these ideas are implemented in practice. The specific example is a differential expression analysis with edgeR starting with a table of counts and ending with a list of differentially expressed genes.
Past versions: