09.velocity_markers.Rmd

---
title: "Marker identification for velocity seurat"
author: "Sergey Naumenko"
date: "`r Sys.Date()`"
output:
    html_document:
        code_folding: hide
        df_print: paged
        highlight: tango
        theme: default
        number_sections: true
        toc: true
        toc_float:
            collapsed: true
            smooth_scroll: false
params:
    variable_features: 3000
    npcs: 30
    flowcell: FC_merged
---

```{r setup, include=FALSE}
library(knitr)
library(tidyverse)
library(readxl)
library(writexl)
library(Seurat)

ggplot2::theme_set(theme_light(base_size = 14))

opts_chunk[["set"]](
    cache = FALSE,
    dev = c("png", "pdf"),
    error = TRUE,
    highlight = TRUE,
    message = FALSE,
    prompt = FALSE,
    tidy = FALSE,
    warning = FALSE)

```

- marker analysis for velocity seurat

# All markers

Description of the columns:

- cluster: cluster ID
- gene: ENSEMBL gene id
- external_gene_name: gene name
- description: gene_description
- p_val_adj: Bonferroni-corrected p-value
- p_val: raw p-value
- avg_logFC: average log2 fold change. Positive values indicate that the gene is more highly expressed in the cluster.
- pct.1: The percentage of cells where the gene is detected in the cluster
- pct.2: The percentage of cells where the gene is detected on average in the other clusters

```{r, rows.print = 25}
clusters_file <- paste0(paste0("data/velocity/seurat.velocity.RDS"))
seurat <- readRDS(clusters_file)
Idents(seurat) <- "spliced_snn_res.1"
markers_file <- "tables/velocity_markers.xlsx"
markers <- FindAllMarkers(object = seurat,
                          only.pos = TRUE,
                          logfc.threshold = 0.1,
                          min.pct = 0.25
                          )
ensembl_w_description <- read_csv("tables/ensembl_w_description.mouse.protein_coding.csv")

#combine markers with gene descriptions
ann_markers <- inner_join(x = markers,
                  y = ensembl_w_description,
                  by = c("gene" = "ensembl_gene_id")) %>% unique()

# Order the rows by p-adjusted values
ann_markers <- ann_markers %>%
    dplyr::arrange(cluster, p_val_adj) %>% 
    dplyr::select(cluster, gene, external_gene_name, description, p_val_adj, p_val, avg_logFC, pct.1, pct.2)                      

write_xlsx(list(ann_markers), markers_file)
```

# GADD45
```{r}
FeaturePlot(seurat, features = "ENSMUSG00000036390") + ggtitle("Gadd45")
```
```{r}
DimPlot(seurat,
        reduction = "umap",
        group.by = "age"
        ) +
        ggtitle("Clusters by condition")
```


# R session information
```{r}
sessionInfo()
```