Single Cell (Seurat, Spatial Inference)

All the functions that take place within a cell are performed through proteins. These proteins are coded within the DNA (Deoxyribonucleic acid) of the cell. A gene is a sequence of DNA that encodes for a particular protein. In order to make the necessary proteins, the transcriptional machinary of a cell makes special copies of the respective genes that can be translated to protein sequences. These special copies are called messenger RNAs (Ribonucleic acids).

The amount of mRNA produced by a specific gene is used as a surrogate marker for quantification of the gene activity. With Current highthroughput sequencing technologies (such as in this case RNAseq), all (fragmented) RNA molecules from a biological sample are sequenced. These sequences are then matched to the annotated genome sequence to identify to which gene each sequenced fragment belongs. More sequenced fragments mapping to a gene in one samples as compared to another, means that the specific gene had a higher activity. In addition, single cell sequencing technologies have allowed us to identify the heterogeneous nature of phenotypically pure populations and identity new sub populations of cells based on difference in gene expression.

In this workflow developed by Satija lab imaging data is used together with single cell transcrptomic data to resolve the spatial position of the sequenced cells based on their expression profile.

Packages and Dependencies

There are 4 packages used in this workflow, which depend on 68 additional packages (dependencies)

Used packages:

• Github: Seurat (satijalab/seurat)
• CRAN: XLConnect, rgl, knitr

Package dependencies:

• CRAN: ggplot2, reshape2, useful, gridExtra, gplots, gdata, XLConnectJars, ROCR, stringr, mixtools, lars, fastICA, tsne, Rtsne, fpc, ape, VGAM, jackstraw, XLConnect, methods, rJava, plyr, scales, gtable, digest, MASS, proto, Rcpp, dichromat, labeling, RColorBrewer, munsell, colorspace, magrittr, stringi, dplyr, assertthat, R6, DBI, lazyeval, gtools, KernSmooth, caTools, bitops, boot, segmented, diptest, mvtnorm, flexmix, mclust, trimcluster, kernlab, robustbase, class, cluster, prabclus, nnet, lattice, modeltools, DEoptimR, nlme, corpcor, evaluate, markdown, yaml, highr, formatR, mime

Data

Single cell transcriptome data from Satija*, Farrell* et al., 2015

License

Copyright (c) 2015 Rahul Satija

based on code from Tutorial - Spatial Inference of single cell data

Copyright (c) 2015-2016 BeDataDriven B.V.

License: GPL version 2 or higher