UNDER CONSTRUCTION
Testis 2 Individual Clustering
Normalize Data
sobj <- FindVariableFeatures(
sobj,
selection.method = "vst",
nfeatures = 2000,
verbose = FALSE
)
top20 <- head(VariableFeatures(sobj), 20)
Scale Data
all_genes <- rownames(sobj)
sobj <- ScaleData(
sobj,
features = all_genes,
vars.to.regress = "nCount_RNA",
verbose = FALSE
)Dimensionality Reduction
Dimensional Gene Loadings

Top Genes Heatmap

JackStraw

Elbow Plot

Dimension Selection
Clustering
| Resolution | Num of Clusters |
|---|---|
| 0.4 | 10 |
| 0.5 | 12 |
| 0.6 | 14 |
| 0.7 | 15 |
| 0.8 | 16 |
| 0.9 | 17 |
| 1 | 18 |
| 1.1 | 20 |
| 1.2 | 20 |

Look at marker genes
Spermatogonia


Spermatocytes


CySC


TE


PC


hub


Cluster Markers
| cluster | Num Biomarkers per Cluster |
|---|---|
| 0 | 359 |
| 1 | 392 |
| 2 | 683 |
| 3 | 1,375 |
| 4 | 556 |
| 5 | 377 |
| 6 | 1,615 |
| 7 | 501 |
| 8 | 1,088 |
| 9 | 1,918 |
| 10 | 286 |
| 11 | 570 |
| 12 | 1,933 |
| 13 | 74 |