Seurat findmarkers vs findallmarkers
Seurat findmarkers vs findallmarkers. ) You should use the RNA assay when exploring the Gene expression markers for all identity classes. Apr 10, 2024 · RunMiQC: Run miQC on a Seurat object; RunOptimizeALS: Run optimizeALS on a Seurat object; RunPresto: A Presto-based implementation of FindMarkers that runs RunPrestoAll: A Presto-based implementation of FindAllMarkers that runs RunQuantileAlignSNF: Run quantileAlignSNF on a Seurat object; RunQuantileNorm: Run quantile_norm on a Seurat object Mar 5, 2018 · Hi Seurat team, I have aligned data from two batches with CCA and performed clustering. 2="Normal" (do not run SCTransform again) There is an important caveat however, which is if the library sizes across batches are very different this might result in lot of false positives (see explanation here) saketkc closed this as completed Sep 3, 2021. Nov 18, 2023 · as. threshold = 0. FindAllMarkers automates this process for all clusters, but you can also test groups of clusters vs. var = "group", only. or functions should help. 2 and avg_logFC May 4, 2019 · ----- Fix pipeline_seurat. P. This function essentially performs a differential expression test of the expression level in a single cluster versus the average pseudocount. Oct 1, 2019 · You signed in with another tab or window. From what I understand, the data slot in SCT assay stores lognormalised counts as well, which ideally should be the same as RNA data slot if Seurat can help you find markers that define clusters via differential expression. genes. 1 = cluster, grouping. S. I was using FindAllMarkers function and found the marker identification is slower than the corresponding function of Scanpy. , either group_1, group_2, group_3, or group_5. Sep 11, 2023 · Seurat can help you find markers that define clusters via differential expression. pct. Seurat: Convert objects to 'Seurat' objects; as. py to follow the current advice of the seurat authors (satijalab/seurat#1717): "To keep this simple: You should use the integrated assay when trying to 'align' cell states that are shared across datasets (i. Subset a Seurat Object based on the Barcode Distribution Inflection Points. threshold. The problem is that FindMarkers treats each cell like its own sample, even though all cells from a sample are similar and thus urual commented on Apr 5. As well finding marker of individual clusters, i am also just interested in understanding what differences exist between different conditions (i. This includes minor changes to default parameter settings, and the use of newly available packages for tasks such as the identification of k-nearest neighbors, and graph-based clustering. Jul 28, 2020 · If you are using single cell data to compare expression between conditions (normal vs tumor, control vs treated cells), it is advised to not use Seurat FindMarkers or similar tools when you have multiple samples. Feb 26, 2024 · Within methods based on DE testing, most (Scanpy, Seurat) use one-vs-rest testing while scran uses an alternative pairwise strategy (see the “Methods” section for details). 2参数指定两个特定的细胞类群。 Apr 23, 2019 · All the 30 genes I found using the code below constitute the top 30 genes in this new analysis in the same order, but they have a lot smaller p values now. use = "wilcox" , slot = "data" , Jan 27, 2019 · Dear Seurat developers, I am having an issue with detecting differentially expressed genes using "FindAllMarkers" function. I assume that this is because the they are so significant as to consider the p-value 0 Mar 20, 2024 · as. use: Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of We have made minor changes in v4, primarily to improve the performance of Seurat v4 on large datasets. Nov 18, 2023 · An AUC value of 1 means that expression values for this gene alone can perfectly classify the two groupings (i. Any help is greatly appreciated! The text was updated successfully, but these errors were encountered: Feb 20, 2021 · You signed in with another tab or window. camilliano. You have to specify both identity groups. I would also like to perform BH correction on the p-values from FindMarkers. 5. 0 and noticed that the results by FindMarkers and FindAllMarkers were different than ones generated by Seurat v4. , inhibitory neurones but differ wrt age groups, i. . SingleCellExperiment: Convert objects to SingleCellExperiment objects; as. ptc. To keep this simple: You should use the integrated assay when trying to 'align' cell states that are shared across datasets (i. pos = TRUE, min. 1), compared to all other cells. 1 is the percentage of cells expressing the gene/feature in ident. logfc. 1 and pct. seurat. frame containing a ranked list of putative conserved markers, and associated statistics (p-values within each group and a combined p-value (such as Fishers combined p-value or others from the metap package), percentage of cells expressing the marker, average differences). 使用FindMarkers函数进行差异表达分析。默认情况下,FindMarkers函数使用非参数的Wilcoxon秩和检验进行差异表达分析。如果要对两组特定的细胞类群执行差异分析,可以设置ident. If NULL (default) - use all other cells for comparison. Feel free to re-open the issue if that doesn't solve it Mar 27, 2023 · Seurat can help you find markers that define clusters via differential expression. So, in this case, I do not know how to sort avg_logFC, because each gene has a different value in avg_logFC depending on genotype. Finds markers (differentially expressed genes) for each of the identity classes in a dataset. 1: Identity class to define markers for. After setting the plan and running my code, I check out my cores using htop and find that only one core is being used. Feb 21, 2019 · When comparing data across conditions (for example, ctrl v. Sep 24, 2021 · Log2FC positive: Control is upregulated relative to disease, negative log2FC: control is downregulated relative to disease. I have some question about analysis of DEG (findmarker etc. p_val_adj – Adjusted p-value, based on bonferroni correction using all genes in the dataset. by: A metadata column name - the data will be split by this column to calculate FindAllMarkers separately for each data split. Best, Leon The function should return all genes that pass the preliminary filtering thresholds, i. FindAllMarkers () will find markers differentially expressed in each identity group by comparing it to all Aug 29, 2018 · We calculate average log expression for any gene in each group as: log( x = mean( x = expm1( x = x )) + pseudocount. 2 and Supplementary Tables 2 and 3 for all evaluated methods). If so, this could throw things off as FindMarkers allows ident. use="poisson",latent. AutoPointSize: Automagically calculate a point size for ggplot2-based AverageExpression: Averaged feature expression by identity class Apr 25, 2021 · Hi, I would like to check what the differences are between the SCT data slot and RNA data slot. Seurat::FindAllMarkers() uses Seurat::FindMarkers(). ident. findMarkers_one_vs_all. Figure 2 shows the results for a selection of methods (see Supplementary Fig. 1 will just be the cell "1" instead of all cells belonging to class 1. If you go the RNA route definitely normalize and scale before running FindMarkers. You can solves this wrong by running "object <- joinlaryers (object)" after using FindAllMarkers. 5 implies that the gene has no predictive Asc-Seurat can apply multiple algorithms to identify gene markers for individual clusters or to identify differentially expressed genes (DEGs) among clusters, using Seurat’s functions FindMarkers and FindAllMarkers. Note that the absolute best way to do this is to run DE We have made minor changes in v4, primarily to improve the performance of Seurat v4 on large datasets. Rd. Nov 22, 2021 · Probably results from running on the SCT should be similar to RNA, but would recommend clustering first and for find marker use SCTransform data. 0. From Seurat vignette, FindMarkers() can be accelerated by utilizing future package, future::plan("multiprocess", workers = 4). Because you want to contrast two clusters against each other, I suggest using FindMarkers() as opposed to FindAllMarkers(): FindMarkers(object, ident. If you have cell names that are the same as an identity class (e. A value of 0. FindAllMarkers () will find markers differentially expressed in each identity group by comparing it to all May 9, 2018 · 3. . Rの329行目から419行目にあります 。. threshold, min. Asc-Seurat allows users to filter gene markers and DEGs by the fold change and minimal percentage of cells expressing a gene in May 23, 2018 · But, for example, in FindConservedMarkers, in my case with 3 groups, I obtained all parameters (p_val, avg_logFC, pct. test. ご回答有難うござい Sep 11, 2023 · I have a Seurat object which I created after running integration on different Seurat objects. 1 ), compared to all other cells. It returns no differentially expressed genes (an empty csv file), no matter which test I am using. May 24, 2019 · Seurat object. 2, p_val_adj) for each group (these 5 columns for each genotype). by="group", ident. metrics according to the subset that was specified (currently these numbers are computed using all cells). However, I’ve noticed that the avg_log2FC values calculated by both FindAllMarkers() and FindMarkers() in Seurat V5 are nearly identical, regardless of the test used (default “wilcox” or “wilcox_limma”). Mar 25, 2022 · Dear Seurat developers, I am using FindMarkers to identify marker genes for disease vs. I don't know whether we can find the differential expressed gene between only these two samples. While LogNomralization uses a default scaling factor of 10000, SCTransform produces "corrected counts" using median of Run FindMarkers on Cluster 1 on SCT assay, data slot with idents. Aug 28, 2020 · To make a fair comparison, we applied Seurat’s FindAllMarkers function both with and without its default filter, which is typically only passed by a small subset of genes. By default, it identifies positive and negative markers of a single cluster (specified in ident. # variable 'group') markers <- FindMarkers(pbmc_small, ident. FindAllMarkers (. bar. ; From the FindMarkers documentation: "For each gene, evaluates (using AUC) a classifier built on that gene alone, to classify between two groups of cells. pct, etc. Hello, i know you may have many way to solve this problem. My problem is that FindMarkers only returns results of a few 100 genes instead of all the genes in the dataset. For the FindMarker() function, only see the differential expressed gene between different identities. #62 (comment) I do not specify anything for the slot parameter, so I guess it should use the default slot data. only. Seurat has the functionality to perform a variety of analyses for marker identification; for instance, we can identify markers of each cluster relative to all other clusters by using the FindAllMarkers() function. CD4 T cellのDEGとしてT細胞マーカーが得られる markers <- FindMarkers(object = pbmc_small, ident. features. Default is to use all genes. Jun 9, 2020 · This is far from being the first time I run FindMarkers, but first time I encounter this problem. R. each other, or against all cells. for clustering, visualization, learning pseudotime, etc. データのサブセットに対して関数を実行して、再確認することもできます。. Any idea why I have avg_diff instead of avg_logFC? I'm using Seurat version 3. 1 – The percentage of cells where the gene is detected in the first group. Default is 0. May 26, 2020 · This left me a little bit worried since previously it has been mentioned that using scaled data for FindMarkers was inappropriate. I made a seurat object from 3 different data set with method of integration with SCTtranform. ) after integration with SCT. FindMarkers() Jul 5, 2023 · This question is covered in the FAQs but to summarize you should run FindMarkers on the RNA or SCT assay. grouping. Best, Sam May 4, 2018 · I recently implemented the following code do speed up the process of finding cluster markers in Seurat (using the BiocParallel Package). Assignees. Most DE-based methods compare the Nov 8, 2020 · There are clear examples of how to create a heatmap on the Seurat website, so I suggest you take a look there. 0 and re-calculated the differentially expressed genes by FindMarkers from Seurat 4. Mar 15, 2018 · Seurat::FindAllMarkers() uses Seurat::FindMarkers(). I tried to extract some info from the csv file but I couldn't determine which parameter is important (avglogfc, pct1, pct2, pvalue or Hi, I was wondering whether we can find the differentially expressed genes between the Double-KO and the Shox2-KO. To give some context, I have two groups - Control and Disease. I read some old posts about doing it with the doFuture package. object is your scRNA-seq data of Seurat. 25 , test. 1: this function only evaluates one cluster at a time; here you would specify the cluster of interest. Would you advise against this? What would be the disadvantages compared to FindConservedMarkers? With best regards, Darius Mar 15, 2018 · Source. First 6 clusters have NAs for p values of ALL found markers (and consequently p_val_adj). 1="WT", subset. You can set the other thresholds to 0 to return all genes (though this will slow down DE) satijalab closed this as completed on Oct 25, 2019. 1 = 2) head(x = markers) # Take all cells in cluster 2, and find markers that separate cells in the 'g1' group (metadata. The corresponding code can be found at lines 329 to 419 in differential_expression. sparse: Cast to Sparse; AugmentPlot: Augments ggplot2-based plot with a PNG image. This is why we treat sample comparison as a two-step Aug 4, 2020 · Thanks again for developing Seurat! I would like to ask you a question with regard to the avg_logFC output of FindAllMarkers. It could be because they are captured/expressed only in very very few cells. This is useful for comparing the differences between two specific groups. Functions for testing differential gene (feature) expression. pct = 0. markers <- FindMarkers(object = obj, ident. Reload to refresh your session. by = 'groups', subset. g. 1 by default. We can input the top set of markers from FindAllMarkers() into Seurat's built-in heatmap function, DoHeatmap(). 1 and ident. Finally, the top n (i. An adjusted p-value of 1. 2 = "cluster2") pct. colors. I loaded the old Rds file generated by Seurat v4. The test I am using is MAST from Bioconductor. Seurat::FindAllMarkers() は Seurat::FindMarkers() ます。. pos = TRUE) The function accepts a single cluster at a time, so if we want to have the function run on all clusters, then we can use the map Nov 5, 2019 · 2) Is FindMarkers() working on Normalized_Data or Scaled_Data or clusters obtained by RunTSNE()/RunUMAP()? As FindMarkers() finds marker genes of the clusters, so I assume that the input data for FindMarkers() should be clusters obtained by RunTSNE() / RunUMAP() which these functions are also using scaled_data or selected_PCAs (obtained from Apr 26, 2024 · A Seurat object. Hello all, I hope everyone is doing good. cells. However, more recent posts mentioned that this is not working anymore? #7000? Oct 31, 2019 · Hi Seurat team, I have performed integration using the SCTtransform workflow (as in your vignette). FindConservedMarkers() Finds markers that are conserved between the groups. vars in FindMarkers() affect results when test. This results in a "diagonal" heatmap in Dec 17, 2020 · Dear SatijaLAB Hello. (vignettes from Satija lab, and May 1, 2020 · And functions FindMarkers and FindAllMarkers work fine with DESeq2, so I can't figure where the problem is. I'm inclined to think that Seurat is outputting 0 p-values because they are so low as to be effectively 0. thresh. Second, genes are ranked by the absolute value of the log fold-change. Oct 5, 2023 · Dear Seurat developers, I realized that the order of latent. FindAllMarkers() automates this process for all clusters, but you can also test groups of clusters vs. Colors to use for the color bar. By default, it identifes positive and negative markers of a single cluster (specified in ident. In Seurat v5, we use the presto package (as described here and available for installation here ), to dramatically improve the speed of DE analysis, particularly for large datasets. You can also double check by running the function on a subset of your data. findMarkers_one_vs_all Source: R/differential_expression. by. Genes to test. nai_t_diff <- FindMarkers(combined, group. However, if we have another identity scheme for each cell computed by an external package which gives only 5 distinct classes, then can I use FindMarkers by adding these new cell identities as a metaData column to my Seurat object? Nov 17, 2021 · You can show results as a MA plot instead, plotting log2 fold change vs average expression: It's a bit trickier to get expression values out of Seurat because they're not currently calculated in the FindMarkers results tables, so you'll need to manually subset the cells and calculate mean expression on a per-marker basis. 25 Increasing logfc. Differential expression . The difference in the SCTransform vs LogNormalization for visualization is because of differences in how they work. 1. Thanks for the awesome package for single-cell analysis. urual commented on Apr 5. e. Best, Leon. Among DE-based methods a variety of multiple-hypothesis correction methods are used (see the “Methods” section for details). , with n = 20) genes in the ranking are then selected as the marker genes for that cluster. ) You should use the RNA assay when exploring the genes that change either across clusters, trajectories, or conditions. However, I'm still a bit skeptical May 26, 2019 · FindAllMarkers: Gene expression markers for all identity classes; FindClusters: Cluster Determination; FindConservedMarkers: Finds markers that are conserved between the two groups; FindIntegrationAnchors: Find integration anchors; FindMarkers: Gene expression markers of identity classes; FindNeighbors: SNN Graph Construction Jul 2, 2022 · Here is the story. Source: R/generics. Do you have any suggestions on how I can get FindMarkers to return the whole list? Aug 22, 2018 · So one workaround appears to be to go into FindMarkers and specify cells. Mar 1, 2023 · I am a beginner in terms of parallel computing in R and am trying to run FindMarkers() using the framework described in the vignette. min satijalab commented on Jun 21, 2019. group. Is this average log FC calculated with base e, or base 2? I found the following code in differential_expression. Hi, Yes, the results should be the same. 2, etc. 50K cells), this function would take more than 10 minutes to finish. Cheers Oct 31, 2023 · FindAllMarkers() automates this process for all clusters, but you can also test groups of clusters vs. drug), you should not run FindMarkers on the integrated data, but on the original dataset (assay = "RNA"). use. An AUC value of 0 also means there is perfect classification, but in the other direction. 1 = "cluster1", ident. Mar 15, 2018 · はい、結果は同じであるはずです。. ident = "2") head(x = markers) # Pass 'clustertree' or an object of class Jun 11, 2021 · Directly copy-pasting from one of the Seurat vignettes: # find markers for every cluster compared to all remaining cells, report only the positive ones pbmc. So I have a single cell experiments and the clustering id not great I have a small groups of 6 cells (I know it is extremely small, but nonetheless I would like to make the most of it) that are clearly isolate in UMAP and display marker that I Feb 18, 2020 · I am currently going through different ways of doing DE analysis with single cell data and have opted for seurat FindMarkers approach. Jun 24, 2019 · To access the parallel version of functions in Seurat, you need to load the future package and set the plan. AutoPointSize: Automagically calculate a point size for ggplot2-based AverageExpression: Averaged feature expression by identity class The function we will use is the FindConservedMarkers(), which has the following structure: FindConservedMarkers() syntax: FindConservedMarkers(seurat_obj, ident. control. A vector of cells to plot. 1 = "g1", group. 2). 在seurat中,如果运行了 RunUMAP 或者 RunTSNE 后自动分群后,FindAllMarkers和FindMarkers基本就是一样的;如果没有进行 RunUMAP 或者 RunTSNE 分群,那么需要先运行 BuildClusterTree(object) 函数,利用树聚类先分群. You need to plot the gene counts and see why it is the case. 9. Oct 25, 2017 · I was recently wondering about the same thing. split. Hi, "myAUC" represents the area under the ROC curve. 2: A second identity class for comparison. #4433. All the cells in the integrated Seurat object are of the same cell type, i. ident = "Naive T") # 115 differentially expressed genes are found (top 30 are the same genes with the previous Dec 18, 2017 · So, if there are nine clusters identified by FindClusters, then FindAllMarkers uses these cluster IDs to find markers. You haven't shown the TSNE/UMAP plots of the two clusters, so its hard to comment more. 25, logfc. The issue is as follows: for both my top 50 up or downregulated marker genes, there are many with p-values of 0. 1="Disease" and idents. 2 parameters. FindMarkers assumes that data is normalized first, and that woudl explain the extremely high logFC. 2 is the percentage of cells that express gene/feature in ident. When I try to identify cluster markers using the "integrated" assay and the "scale. 1 exhibit a higher level than each of the cells in cells. pos: Only return positive markers (TRUE by default) features: Genes to test. FindAllMarkers(kid. 1/2 to be either an "identity" or a vector of cell names. ), including genes that have p-value > 0. "power" is defined as the predictive power and is calculated as abs(AUC-0. A vector of variables to group cells by; pass 'ident' to group by cell identity classes. I run FindMarkers for 17 clusters using the default (wilcoxon) test - I run each cluster vs all. However, I don't want to blindly assume that. markers <- FindAllMarkers(pbmc, only. var: the variable (column header) in your metadata which specifies the separation of cells into groups. Gene expression markers of identity classes — FindMarkers • Seurat. 👍 1. use = "MAST Feb 15, 2023 · Seurat のDEG検出機能では、「1つのcluster vs 残りの全て」という比較が行われる。. 例えば、「CD4 T cell vs 末梢血のその他の細胞」 という構図だと、その他の細胞にはTregやCD8 TなどT細胞も含まれることになる。. Here, we list some additional arguments which provide for when using FindConservedMarkers(): ident. 1, pct. However, I am running a simulation that I need to use FindAllMarkers() inside a doParallel::foreach() loop after doParallel::registerDoParallel(numCores=10). group: Minimum number of cells in the group - if lower the group is skipped Each column of the heatmap represents a single cell, organized by cluster. Nov 1, 2021 · I updated the Seurat package to version 4. 01. ident") I would like to run it in parallel in RStudio. But, i also want to share the way that solves the trouble by "joinlaryers ()" function. 1/2 are calculated as the percentage of cells in each group that the gene is Nov 26, 2019 · FindMarkers will find markers between two different identity groups - you have to specify both identity groups. To test for DE genes between two specific groups of cells, specify the ident. p-value. 4E-288) and the p-values reported as 0 seem to be more differentially expressed than even those. 5). Each of the cells in cells. FindMarkers(object, ) The bulk of Seurat’s differential expression features can be accessed through the FindMarkers() function. This is not also known as a false discovery rate (FDR) adjusted p-value. e wt vs treated) regardless of which clusters cells belong to. R, R/differential_expression. Jul 29, 2020 · The p-values are not very very significant, so the adj. These changes do not adversely impact downstream Feb 8, 2022 · I was wondering which assay, (SCT or RNA), should be used when invoking FindAllMarkers function on SCTv2 transformed data for a single sample. I'm actually trying to use FindAllMarkers(), but my issue appears with both of them. A vector of features to plot, defaults to VariableFeatures(object = object) cells. 00 means that after correcting for multiple testing, there is a 100% Oct 2, 2023 · I noticed that using FindMarkers with the ident. These changes do not adversely impact downstream May 3, 2021 · How can we speed up FindMarkers. 25) pbmc. The other 11 clusters are fine as usual. use = "MAST" (similar thing is happening with LR test as well). Seurat object. Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. I am aware of this question Manually define clusters in Seurat and determine marker genes that is similar but I couldn't make tit work for my use case. 1, ident. 1和ident. Add a color bar showing group status for cells. For example, using the pbmc3k object Findmarkers in Seurat 三种不同方案的差异. filtered_new,test. Pseudocount to add to averaged expression values when calculating logFC. I was wondering if I could simply use FindAllMarkers with the batch passed as a latent variable to identify common cluster markers. Finds markers (differentially expressed genes) for identity classes. FindMarkers () will find markers between two different identity groups. By default, Seurat performs differential expression (DE) testing based on the non-parametric Wilcoxon rank sum test. I guess taking random samples from this each time will make the measured pct. Findmarkers in Seurat 三种不同方案的差异. disp. Each row of the heatmap represents a gene from the marker list you input, which typically is also organized by cluster. 2 in the FindMarkers function while performing DEG. object , assay = NULL , features = NULL , logfc. Aug 25, 2021 · Most likely the issue here is that you did not normalize the RNA data prior to running FindMarkers. Denotes which test to use. min. markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_log2FC) features. 2 as a subset on the frontend, which will give pct. You switched accounts on another tab or window. It works quite nicely for me (the results I get using this code are the same as with FindAllMarkers without parallelisation). vars = "sample_name_numeric", test. I had a question regarding the position of ident. Hello! I am new to using Seurat and am trying to account for a metadata variable ("sample_name_numeric") when using FindAllMarkers in the following code: FindAllMarkers(object = mfmo, latent. 対応するコードは 、differential_expression. use: Genes to test. vars = "orig. R, and would like to ask if this is the correct place? It would mean that Seurat uses the natural log with Feb 26, 2024 · First, for each cluster a one-vs-rest log fold-change value is calculated (using Seurat’s formula) for each gene. FindAllMarkers() Gene expression markers for all identity classes. You signed out in another tab or window. The plan will specify how the function is executed. use) Note that the pseudocount is defined to be 1 by default. These should hold true for Visium data as well. 2. Identity class to define markers for; pass an object of class phylo or 'clustertree' to find markers for a node in a cluster tree; passing 'clustertree' requires BuildClusterTree to have been run. threshold speeds up the function, but can miss weaker signals. #2907 I am having trouble generating a heatmap from the result of FindMarkers between two clusters. Identify marker genes for all clusters in a one vs all manner. This is because the integration will aim to remove differences across samples so that shared populations align together. Usually for a data with tens of thousands cells (e. FindAllMarkers will find markers differentially expressed in each identity group by comparing it to all of the others - you don't have to manually define anything. We then take the difference of these values (group1 - group2) to compute avg_logFC. (see #1501 ). 2) It can also compare combinations of clusters. The other reported p-values are also very low (e. 2 options return different logFC, even though the p-values and adjusted p-values are the same (the same issue happens also with raw counts). a cell called "1"), then the set of cells that will be used for ident. Apr 21, 2023 · FindAllMarkers, FindMarkers 以及 FindConservedMarkers 的区别. data" slot FindAllMarkers fails with: Warning: No DE genes identified Warning: The following tests were not performed: Warning: When testing PA_4 versus all: Nov 18, 2023 · parameters to pass to FindMarkers Value data. The default behavior is to evaluate in a non-parallelized fashion (sequentially). To achieve parallel (asynchronous) behavior, we typically recommend the “multiprocess” strategy. I have found some discussions regarding the use of the appropriate assay on SCTv1 transformed data and integration, but I am not sure about the SCTv2 transformed data and a single sample (no integration). xu xc sl zo bl vf br wj ch qu