set-up

Dimensional reduction

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Warning: Removed 2 rows containing missing values (geom_text).

## Warning: Removed 2 rows containing missing values (geom_text).

Proportion KO-WT

##   Genotype cluster Proportion
## 1       KO       1      99.56
## 2       KO       4      58.39
## 3       KO       2      50.30
## 4       KO       3      39.31
## 5       KO       5        NaN
## 6       KO       6        NaN

visualise in a plot

## Warning: Removed 4 rows containing missing values (position_stack).

Markers for each cluster

We compute here a pairwise differential expression between all the clusters. The results are saved in a list of dataframes, one for each cluster. A .csv file (that can be opened with a spreadsheet program) for each cluster is available in “markers_oligo_cluster”.

The default philosophy of findMarkers() from scran is to identify a combination of marker genes that - together - uniquely define one cluster against the rest. To this end, we collect the top DE genes from each pairwise comparison involving a particular cluster to assemble a set of candidate markers for that cluster. Of particular interest is the Top field. The set of genes with Top ≤X is the union of the top X genes (ranked by p-value) from each pairwise comparison involving the cluster of interest. For example, the set of all genes with Top values of 1 contains the gene with the lowest p-value from each comparison. Similarly, the set of genes with Top values less than or equal to 10 contains the top 10 genes from each comparison.

Specifically finding markers for cluster 1 (very different from KO and WT)

With Seurat we can find markers that define a cluster by computing the differential expression between that cluster and all other cells.