<p><a href="https://www.rdocumentation.org/packages/Seurat/versions/3.1.1/topics/FindMarkers" rel="nofollow noreferrer" > Seurat FindMarker Doc </a>
Yes based upon the R documentation here it seems possible.</p>

<p>This <a href="https://satijalab.org/seurat/v3.1/de_vignette.html" rel="nofollow noreferrer" > Differential expression article </a> seems to show this is how.</p>

<pre><code>library(Seurat)
pbmc &lt;- readRDS(file = "../data/pbmc3k_final.rds")
# list options for groups to perform differential expression on
levels(pbmc)
# Find differentially expressed features between CD14+ and FCGR3A+ Monocytes
monocyte.de.markers &lt;- FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono")
# view results
head(monocyte.de.markers)
# Find differentially expressed features between CD14+ Monocytes and all other cells, only
# search for positive markers
monocyte.de.markers &lt;- FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = NULL, only.pos = TRUE)
# view results
head(monocyte.de.markers)
# Pre-filter features that are detected at &lt;50% frequency in either CD14+ Monocytes or FCGR3A+
# Monocytes
head(FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono", min.pct = 0.5))
#...
# Test for DE features using the MAST package
head(FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono", test.use = "MAST"))
head(FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono", test.use = "DESeq2", max.cells.per.ident = 50))
</code></pre>

<p>Maybe something like this would work for you. <a href="https://rdrr.io/bioc/monocle/man/exportCDS.html" rel="nofollow noreferrer" > Monocle export </a></p>

<pre><code>  #replace the monocle_cds with your monocle
  seurat &lt;-exportCDS(monocle_cds, export_to = c("Seurat", "Scater"))
  #This bellow will list the options for ident.1 and ident.2
  levels(seurat) 
  # insert name from levels(seurat) command in parentheses
  head(FindMarkers(seurat, ident.1 = "Double-KO+ Mono", ident.2 = "WT+ Mono", test.use = "DESeq2", max.cells.per.ident = 50))
</code></pre>

<p>Hopefully you are able to separate the two groups Double-KO and WT into ident.1 and ident.2 so you can use the function on them. Good luck. As long as the exportCDS function works on your data type you input for cds.</p>

CC BY-SA 4.0
2019-11-13T02:03:25.490Z
this kind of findmarkers isn't monocle function and I was curious about the last code you give me that it may include all of the Double-KO and the WT not the specific cells in the bottom right corner.
hua
CC BY-SA 4.0
2019-11-13T02:17:07.533Z
As long as you can create the file it should work. library(Seurat)
seurat <- readRDS(file = "../data/pbmc3k_final.rds")
Michael Hearn
CC BY-SA 4.0
2019-11-13T02:35:39.203Z
Did you mean that if I can export the data from the monocle to the Seurat?
hua
CC BY-SA 4.0
2019-11-13T02:39:17.967Z
Yes you need to readRDS or exportCDS to get the data you need then use levels to list ident to use.
Michael Hearn
CC BY-SA 4.0
2019-11-13T04:02:49.397Z
I have no idea whether I have use the correct code "seurat <- exportCDS(CM_cds,export_to = c("Seurat", "Scater"))
levels(seurat)
head(FindMarkers(seurat, ident.1 = "Double-KO+ Mono", ident.2 = "WT+ Mono", test.use = "DESeq2", max.cells.per.ident = 50))
" Thank you very much for your advice!!!
hua
CC BY-SA 4.0
2019-11-12T22:32:15.940Z
2019-11-13T02:14:52.810Z
2019-11-13T02:14:52.810Z
Michael Hearn
<p><a href="https://i.sstatic.net/kFC32.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/kFC32.jpg" alt="enter image description here"></a> As it is shown in the picture that we can get the pseudotime plot by using Monocle. I was wondering whether we can use some function like the Seurat FindMarker to find out the differential expression genes between the Double-KO and WT in the  bottom right corner?</p>

CC BY-SA 4.0
2019-11-13T17:11:24.937Z
If you already have the `monocle` object I wouldn't convert it to `Seurat` as to keep the scaling and normalisation that led to the pseudotime. Have a look at their documentation as there is a section exactly dedicated to what you want (http://cole-trapnell-lab.github.io/monocle-release/docs/#differential-expression-analysis)
fra
CC BY-SA 4.0
2019-11-14T05:14:33.223Z
Hi fra, thank you for your advice. But I still cannot get the marker gene out easily as Seurat do
hua
<seurat><single-cell>
different gene expression in the monocle
# different gene expression in the monocle

(Asked by: username_0 on 2019-11-12)

[![enter image description here](https://i.sstatic.net/kFC32.jpg)](https://i.sstatic.net/kFC32.jpg) As it is shown in the picture that we can get the pseudotime plot by using Monocle. I was wondering whether we can use some function like the Seurat FindMarker to find out the differential expression genes between the Double-KO and WT in the bottom right corner?

**Tags:** seurat, single-cell
> **Comments:**
> **username_1**: If you already have the `monocle` object I wouldn't convert it to `Seurat` as to keep the scaling and normalisation that led to the pseudotime. Have a look at their documentation as there is a section exactly dedicated to what you want (http://cole-trapnell-lab.github.io/monocle-release/docs/#differential-expression-analysis)  
> **username_0**: Hi fra, thank you for your advice. But I still cannot get the marker gene out easily as Seurat do  

---

## 1 answers

### Answer by username_2 on 2019-11-12. Score: 1

Seurat FindMarker Doc 
Yes based upon the R documentation here it seems possible.
This  Differential expression article  seems to show this is how.
library(Seurat)
pbmc <- readRDS(file = "../data/pbmc3k_final.rds")
# list options for groups to perform differential expression on
levels(pbmc)
# Find differentially expressed features between CD14+ and FCGR3A+ Monocytes
monocyte.de.markers <- FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono")
# view results
head(monocyte.de.markers)
# Find differentially expressed features between CD14+ Monocytes and all other cells, only
# search for positive markers
monocyte.de.markers <- FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = NULL, only.pos = TRUE)
# view results
head(monocyte.de.markers)
# Pre-filter features that are detected at <50% frequency in either CD14+ Monocytes or FCGR3A+
# Monocytes
head(FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono", min.pct = 0.5))
#...
# Test for DE features using the MAST package
head(FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono", test.use = "MAST"))
head(FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono", test.use = "DESeq2", max.cells.per.ident = 50))

Maybe something like this would work for you.  Monocle export 
  #replace the monocle_cds with your monocle
  seurat <-exportCDS(monocle_cds, export_to = c("Seurat", "Scater"))
  #This bellow will list the options for ident.1 and ident.2
  levels(seurat) 
  # insert name from levels(seurat) command in parentheses
  head(FindMarkers(seurat, ident.1 = "Double-KO+ Mono", ident.2 = "WT+ Mono", test.use = "DESeq2", max.cells.per.ident = 50))

Hopefully you are able to separate the two groups Double-KO and WT into ident.1 and ident.2 so you can use the function on them. Good luck. As long as the exportCDS function works on your data type you input for cds.

> **Comments:**
> **username_0**: this kind of findmarkers isn't monocle function and I was curious about the last code you give me that it may include all of the Double-KO and the WT not the specific cells in the bottom right corner.  
> **username_2**: As long as you can create the file it should work. library(Seurat) seurat <- readRDS(file = "../data/pbmc3k_final.rds")  
> **username_0**: Did you mean that if I can export the data from the monocle to the Seurat?  
> **username_2**: Yes you need to readRDS or exportCDS to get the data you need then use levels to list ident to use.  
> **username_0**: I have no idea whether I have use the correct code "seurat <- exportCDS(CM_cds,export_to = c("Seurat", "Scater")) levels(seurat) head(FindMarkers(seurat, ident.1 = "Double-KO+ Mono", ident.2 = "WT+ Mono", test.use = "DESeq2", max.cells.per.ident = 50)) " Thank you very much for your advice!!!  

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