Single Cell RNA Seq

Question

[scRNA2023]

Hi Members, I need to do an assignment in single cell RNA seq using the tools tagged above. I will appreciate if anyone can help me out. I have done basic scRNA analysis but need guidance on some of the tools.

Thank you in advance

[ATpoint]

Please understand that these open-ended Qs are difficult to answer. Read the manuals and help pages and then edit the Q with a specific problem.

[scRNA2023]

Hi ATpoint..

Thank you for the suggestion. I am familier with basic scRNAseq pipeline using Seurat. However, I am required to reanalyzed a published scRNA Seq data using the following tools: Doubletfinder, Harmony, Cell Typist, CellO, scGATE, Monocle, SoupX, Cellbender, scTransform

Hence, I wish to have a tutor who can help me out with this. I can be contacted at [e-mail] Thank you

[Ram]

Do not add answers unless you're answering the top level post. Use Add Comment or Add Reply instead as appropriate. I've moved your post to the right location this time.

[scRNA2023]

Thank you for this Ram. Highly appreciated.

[ATpoint]

Try something, then ask specific questions. It's not a code writing service here, nor do people actively contact you via email. That's just not how communities like this work.

[scRNA2023]

Hi ATpoint,

Yes I understand and I am trying to go through the documentations of respective packages also. However, I was just exploring if anyone might be interested to teach :-).

[Ram]

I was just exploring if anyone might be interested to teach :-).

Are you interesting in paying this someone that might be interested in teaching you?

[scRNA2023]

Yes of course..

[Ram]

There are free as well as paid courses online, why ask for this on a question you opened? Why not create a job posting with all relevant details? That will attract the users that freelance.

[scRNA2023]

yes.. you are right. I have seen similar posts here in Biostars also and though that i might find anyone willing to freelance..

[Kevin Blighe]

There are many scRNA-seq tutorials available online for these tagged 'tools'. May you please conduct a search and then report back [here] with a bullet-point list of these tutorials - this would greatly assist the community.

[scRNA2023]

Hi, Yes sure, I can do a search and list down the tutorials for these tagged tools. But mostly, I am going through the documentations of these tools only.

[Kevin Blighe]

Thank you for the follow-up

[scRNA2023]

What is the difference between the filtered and raw gene-barcode matrix?

[LChart]

This is well-earned: https://letmegooglethat.com/?q=10x+cellranger+filtered+vs+raw

Answer 1

[Kevin Blighe]

Hi scRNA2023,

I see from your comments that you're familiar with the basics in Seurat but need pointers on integrating those specific tools for reanalysis. Rather than a full tutorial, here's a concise Seurat-based workflow outline incorporating them—focus on one at a time, starting with preprocessing. I'll include simple R snippets; assume you've loaded your data as a Seurat object seu.

1. Ambient RNA correction (SoupX or CellBender first—pick one):
   SoupX is quicker for initial runs. Install via BiocManager::install("soupX").

   library(SoupX)
   sc = SoupGetWB(soupDir = "your_soup_dir/", ...)
   out = autoEstCont(sc, ...)
   seu[["SoupX"]] <- out
   seu <- SetIdent(seu, value = "SoupX")
   

   For CellBender (Python-based, run via reticulate): Follow their GitHub vignette for itemised output, then import corrected counts to Seurat.

2. Normalisation (scTransform):
   After ambient correction, normalise with SCT.

   library(Seurat)
   seu <- SCTransform(seu, vars.to.regress = "percent.mt", verbose = FALSE)
   

3. Doublet detection (DoubletFinder):
   Run post-normalisation. Install via GitHub.

   library(DoubletFinder)
   seu <- doubletFinder_v3(seu, PCs = 1:10, pN = 0.25, pK = 0.09, ...)
   # Filter doublets based on predicted scores
   

4. Integration (Harmony):
   For batch correction across samples.

   library(harmony)
   seu <- RunHarmony(seu, group.by.vars = "orig.ident", dims.use = 1:20)
   seu <- RunUMAP(seu, reduction = "harmony", dims = 1:20)
   

5. Trajectory (Monocle3):
   Convert Seurat to CellDataSet. Install via Bioconductor.

   library(monocle3)
   cds <- as.cell_data_set(seu)
   cds <- cluster_cells(cds)
   cds <- learn_graph(cds)
   plot_cells(cds, color_cells_by = "pseudotime")
   

6. Annotation (CellTypist or CellO):
   CellTypist is user-friendly for broad types. Install via pip, run on counts.

   library(reticulate)
   py_install("celltypist")
   py$celltypist$annotate(counts_matrix, model = "Immune_All_Low.pkl")
   # Merge predictions back to [e-mail]
   

   CellO is R-based for custom ontologies: BiocManager::install("celO"); see vignette for training/prediction.

7. Gating (scGATE):
   For T-cell subsets, install via GitHub (remotes::install_github("jbergenstrahle/scGate")).

   library(scGate)
   seu <- scGate::scGate(seu, model = "T_cell_model", assay = "SCT")
   

Read each vignette thoroughly—e.g., Seurat's integration chapter ties many together. If you hit a specific error (e.g., with Harmony dims), post it here with code/output. For visualisation, check my scDataviz package on Bioconductor.

Kevin