scRNA snATAC Multiome10x

[matteozoia4]

Dear all,

I am new in ArchR and I would like to know whether the best approach to QCfilter RNA and ATAC experiments together is :

a) QC ATACseq sample, plot the UMAP and integrate the RNAseq ?
b) QC on ATAC and RNA, plot the weighted UMAP ?

Many thanks for your help!
MZ

[rcorces]

It is unclear what you mean by "integration". Either way, my opinion is that one should always QC on both.

[matteozoia4]

Dear rcorces,

sorry for not being clear enough.
I have found two possible ways to integrate snATACseq and scRNAseq, and I was wondering about differences such as downsides between the two, which one would you advice to employ?

1. Cross-platform linkage of scATAC-seq cells with scRNA-seq cells
   (https://www.archrproject.com/bookdown/cross-platform-linkage-of-scatac-seq-cells-with-scrna-seq-cells.html)
2. Combine snATACseq with scRNAseq and meta.data files
   (https://greenleaflab.github.io/ArchR_2020/Ex-Analyze-Multiome.html)

Best regards and thank you for your time,

MZ

[rcorces]

multiple h5 files should be passed to import10xFeatureMatrix. It will combine them.

[matteozoia4]

I see many thanks!

[matteozoia4]

Dear R. Corces,

Another question about redefining the filtering using some parameters such as:

nCount_RNA < 25000 &
nCount_RNA > 500 &
percent_mito < 20 &
nFeature_RNA > 200 & 
nFeature_RNA < 7500 

How can I create the respective columns and then filter ?

pros <-proj[proj$TSSEnrichment > 6 & proj$nFrags > 2500  ......    proj$nCount_RNA < 25000 &     .....    !is.na(proj$Gex_nUMI)]

Ps: I haven't so far found a discussion or a script to do this, it is possible I miss it, sorry if the case!

Thank you in advance!
MZ

[rcorces]

please please please do not subset using [] but rather do this using subsetArchRProject(). I'm in the process of making this clear in the documentation.

create new columns in cellColData (for ex. with addCellColData() and then figure out which cells pass all of your criteria (for example using intersect() or similar).

[rcorces]

sorry - thats beyond the level of support I provide as it isnt directly related to ArchR.

[matteozoia4]

Dear Ryan, I would like to plot linked gene expression with TFs on the UMAP embedding. ``` markerRNA <- getFeatures(myProj, select = paste(motifs, collapse="|"), useMatrix = "GeneIntegrationMatrix") markerRNA p <- plotEmbedding( ArchRProj = myProj, colorBy = "GeneIntegrationMatrix", name = sort(markerRNA), embedding = "IterativeLSI", continuousSet = "blueYellow", imputeWeights = getImputeWeights(myProj) ) ``` Using true Multiome-10x data I found myself in the situation where I don't possess a "groupRNA" parameter, this when trying to produce addGeneIntegrationMatrix(). ``` myProj <- addGeneIntegrationMatrix( ArchRProj = myProj, useMatrix = "GeneScoreMatrix", matrixName = "GeneIntegrationMatrix", reducedDims = "IterativeLSI", seRNA = seRNA, groupATAC = "Clusters", groupRNA = "....", groupList = NULL, sampleCellsATAC = 10000, sampleCellsRNA = 10000, embeddingATAC = NULL, embeddingRNA = NULL, dimsToUse = 1:50, scaleDims = NULL, corCutOff = 0.75, plotUMAP = TRUE, UMAPParams = list(n_neighbors = 40, min_dist = 0.4, metric = "cosine", verbose = FALSE), nGenes = 2000, useImputation = TRUE, reduction = "cca", addToArrow = TRUE, scaleTo = 10000, genesUse = NULL, nameCell = "predictedCell", nameGroup = "predictedGroup", nameScore = "predictedScore", transferParams = list(), threads = getArchRThreads(), verbose = TRUE, force = TRUE, logFile = createLogFile("addGeneIntegrationMatrix") ) ``` Best regards and thank you, MZ

…

________________________________ Da: Ryan Corces ***@***.***> Inviato: giovedì, 25. agosto 2022 15:26:16 A: GreenleafLab/ArchR Cc: Zoia, Matteo (DBMR); Author Oggetto: Re: [GreenleafLab/ArchR] scRNA snATAC integration : What is the best approach for QC and UMAPs production? (Discussion #1583) Cross-platform linkage of scATAC-seq cells with scRNA-seq cells (https://www.archrproject.com/bookdown/cross-platform-linkage-of-scatac-seq-cells-with-scrna-seq-cells.html) This is used when scATAC and scRNA data are acquired separately, not on the same exact cells Combine snATACseq with scRNAseq and meta.data files (https://greenleaflab.github.io/ArchR_2020/Ex-Analyze-Multiome.html) This is used for true multiomic data where scATAC and scRNA are acquired simultaneously in the same single cells using the 10x Multiome kit. — Reply to this email directly, view it on GitHub<#1583 (reply in thread)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AUBHT3FQ5QNF2UE7BUJBE2TV25X7RANCNFSM57LCZLPQ>. You are receiving this because you authored the thread.Message ID: ***@***.***>