Testis 1

Here I explore clustering using Seurat v2 for clustering.

Normalize Data

sobj <- FindVariableGenes(
    sobj,
    mean.function = ExpMean,
    dispersion.function = LogVMR,
    x.low.cutoff = 0.0125,
    x.high.cutoff = 3,
    y.cutoff = 0.5,
    display.progress = FALSE
)

Scale Data

sobj <- ScaleData(
    sobj,
    vars.to.regress = "nUMI",
    display.progress = FALSE
)

Dimensionality Reduction

Dimensional Gene Loadings

Top Genes Heatmap

JackStraw

## An object of class seurat in project testis1 
##  11575 genes across 2710 samples.

Elbow Plot

Dimension Selection

Clustering

## [1] "res.0.4:  9"
## [1] "res.0.5:  9"
## [1] "res.0.6:  9"
## [1] "res.0.7:  9"
## [1] "res.0.8:  13"
## [1] "res.0.9:  13"
## [1] "res.1:  13"
## [1] "res.1.1:  13"
## [1] "res.1.2:  15"

Look at marker genes

Spermatogonia

Spermatocytes

CySC

TE

PC

hub

Cluster Markers

Top 12 Genes Per Cluster

Cluster 0

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Cluster 8

Cluster 9

Cluster 10

Cluster 11

Cluster 12