Satijalab seurat counts vs data github. rpca) that aims to co-embed shared cell types across batches: Apr 30, 2019 · This should be fixed in the development version of Seurat. Mar 25, 2024 · Existing Seurat workflows for clustering, visualization, and downstream analysis have been updated to support both Visium and Visium HD data. Now the pitfall is if Seurat is ever going to use the original counts again in other functions to calculate, say, the column sum of Dec 27, 2023 · Hi Seurat team, I have two count matrices (A and B) of the same phenotype but from two different studies. I'm still a little confused. But in the V5 version, I run the following code: But in the V5 version, I run the following code: Jan 8, 2024 · Hi - thank you for your questions. Calculate the percentage of mitochondrial genes and cell cycle scores if wanted. I loaded these two samples as seurat objects and I am not sure why I am getting HTO1 and HTO2 in individual seurat objects. So, I have tried to load count data into Rstudio using the following command to create a seurat object. data = matrix_n) where matrix_n contains e. The original data are not counts, which is why you have non-integer numbers. I would like to use the SCTransform workflow to remove batch effects from the data, but I found that integrated_data@assays[["integrated"]]@counts is empty in the output of IntegrateData(normalization. Nov 17, 2023 · Because there was a recent large update to Seurat, it may be that the maintainers of EnDecon have not yet updated their functionality to be compatible. For your purposes, it doesn't matter if you use scaled or unscaled data to split your dataset on 1 feature, since the scaling transformation preserves ordering. If you run SCTResults(object=merge, slot="umi. In general, we use object@scale. In any case: Seurat is amazing and thanks for all the support. However, since the data from this resolution is sparse, adjacent bins are pooled together to Oct 31, 2023 · Seurat v5 enables streamlined integrative analysis using the IntegrateLayers function. It is tricky to perform DE on pearson residuals themselves (and hence the earlier vignettes recommended moving to RNA Sep 1, 2018 · pbmc@data = log( x = norm + 1 )) Two details worth considering: After doing this, you will loose the data normalized through Seurat. First group. Add RunGraphLaplacian to run a graph Laplacian dimensionality reduction. Now my question is about the residuals used in the function correct_counts, the output of which is saved in the counts in SCTransform output. Discuss code, ask questions & collaborate with the developer community. gz and matrix. The batch corrected counts slot doesn't exists, which is the key to import to monocle to do analysis. In the above-mentioned link, @satijalab was up to use obj[["SCT"]]@scale. data = FALSE, features = NULL, assays = NULL, dimreducs = Reductions(pbmc), #To keep all of the reductions graphs = Graphs(pbmc), #To keep all of the graphs misc = TRUE ) We created SeuratData in order to distribute datasets for Seurat vignettes in as painless and reproducible a way as possible. Dec 5, 2023 · IntegrateData following FindIntegrationAnchors can get the batch corrected counts (also in #6680). A is a Poisson-corrected counts data, and B is an uncorrected counts data. If you use Seurat in your research, please considering Mar 22, 2019 · The manually calculated CLR you can see has a similar range as the Seurat normalization, but you can see that the distribution of the noise is thinner, allowing positive values in the right tail come out. c Feb 15, 2021 · The batch corrected data is in the integrated assay. What it is showing is that only a "counts" layer is present. method 2) perform SCTransform independently on samples from different experiments. gz, matrix. Add RPCAIntegration to perform Seurat-RPCA Integration. Aug 9, 2023 · Here I used CellRanger count (version 3. Both methods do use CCA to identify anchors for integration; however, as noted in our vignette, the v5 integration procedure has changed to return the corrected embeddings instead of an assay, which captures the shared sources of variation and allows you to directly perform downstream analysis. Author. I know you principally disagree, however I need it. mtx. If I'm not mistaken, this might break certain functions, in my case using the DotPlot visualization which uses FetchData to grab the feature counts. Jan 19, 2024 · As of Seurat v5, we recommend using AggregateExpression to perform pseudo-bulk analysis. Perform the quality-check and filtering for each one of them. Regarding Q1, a t-SNE with control vs treatment separates these groups within a given cell type although Louvain still finds them as a single cluster - so that would not be too much of an issue. Any idea to solve the problem? Attached please find my seurat object regeneration. to join this conversation on GitHub . But after coverting to cell_data_set (cds) object as monocle3 required for cluster calculation, I failed to convert it back to Seurat object with as. (Some values are less than 0 but I think that’s okay. method="SCT"). c Aug 8, 2023 · What is the right way to remove scale. Unfortunately, my downstream analysis relies on the count matrix. data from a Seurat object with multiple modalities? What I have is this: DietSeurat( pbmc, counts = TRUE, data = TRUE, scale. We also wanted to give users the flexibility to selectively install and load datasets of interest, to minimize disk storage and memory use. May 1, 2020 · Dear satijalab, Thank you so much for your answer! We have been using DESeq2 for this particular dataset as the data is quite compact, separating in very close clusters, and the negative binomial distribution method showed the best results in capturing the small differences between them. regress. In ScaleData () there are two arguments that I'd like some information about. data can therefore be negative, while values in object@data are >=0. data'). Mar 25, 2021 · Regressing out nCount-RNA is essentially another method of normalization, but if you want to model the effects of sequencing depth on counts, we strongly recommend using SCTransform instead. If you do observe significantly improved results with alternative normalizations, please do let us know! Feb 28, 2024 · I'm currently analyzing spatial transcriptomics data, collected from 6 distinct slides. Variables to regress out (previously latent. regress = "nCount_RNA" which represent regress out the number of UMIs in cells. My question is, can I integrate the two datasets using S Dec 27, 2023 · Hi Seurat team, I have two count matrices (A and B) of the same phenotype but from two different studies. For Seurat, the counts slot is simply the "raw data" slot (see documentation for Assay objects ). Reload to refresh your session. assay") just before running PrepSCTFindMarkers, you may notice that one of your layers has "Spatial" while the others have "RNA" listed. Oct 15, 2018 · If you insert your custom-normalized matrix into object@data, you should be able to proceed onwards from the ScaleData function for downstream analyses. Therefore trying to extract normalized data (that is what the layer "data" is trying to find) will not work. It seems that before any normalization is done (nCount_RNA and nFeature_RNA), nCount is the number of UMI counts taken across all cells from the RNA@data matrix and nFeatures counts any gene with at least 1 UMI count. Mar 29, 2023 · In Seurat package we do not have such function to convert raw counts to FPKM. I want to perform DE analysis using count data (CSV) on the GEO database, GSE139088. My question is, can I integrate the two datasets using S Apr 11, 2024 · Hi All, I'm trying to figure out how to filter out genes that are only present in a low number of cells, from my data containing both gene expression and hashing info. Best, Sam I have 4 different samples available where number of genes vary from 20 to 22K, and number of cells vary from ~9,000 to 25,000). longmanz closed this as completed on I use publicly available scRNA seq data (folder with 3 required files: barcodes. This will fix your issues as there will always be one joined "scale. ). This is in line with the reasoning that the scaling should be done with all of the cells. I tried to setup seurat object with this txt file, but the tsne plot had an obvious batch effect. data 1 other assay present: RNA 2 dimensional reductions calculated: pca, umap ` This is an example dataset that ran SketchData to completion: ` small <- "SCAF2832" obj. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data. Option B: use Seurat's NormalizeData, which (if I understand correctly) normalizes the expression of each gene within a cell by the total expression within that cell. BTW, my seurat object contains two Mar 23, 2023 · Hi, I am learning the Mixscape function in the Seurat. Values in object@scale. For typical scRNA-seq experiments, a Seurat object will have a single Assay ("RNA"). Could you please help me with converting the patial data from Scanpy (python) to Seurat (R) ? I got the h5ad file (spatial transcriptome data. data) is very clearly stated in the paper. 1 Following step by step guidelines I was able to analyze my data. Thanks for asking. Other methods worked properly, so I continued my analysis integrating with Harmony. Jan 17, 2024 · So the issue seems to be that the data hasn't been normalized. by variable ident starts with a number, appending g to ensure valid variable names This message is displayed once every 8 hours. Sep 13, 2019 · I want to clarify how nCount and nFeature are calculated both before and after normalization. I would recommend posting on issue on the EnDecon repository. 👍 1 alextamburino reacted with thumbs up emoji. Feb 18, 2020 · edited. Apr 13, 2023 · These layers can store raw, un-normalized counts (layer='counts'), normalized data (layer='data'), or z-scored/variance-stabilized data (layer='scale. Given the structure of my dataset, I'm wondering whether it would be more scientifically sound to integrate the data across the different donors or across the slides. The difference in the SCTransform vs LogNormalization for visualization is because of differences in how they work. Do you know how I can get normalized counts corrected for batch effect for all genes? (not scaled counts, just normalized counts, in order to use them for downstream differential expression analyses). Or you can check layers with Layers function: > Layers (object = pbmc) [1] "counts" "data". While LogNomralization uses a default scaling factor of 10000, SCTransform produces "corrected counts" using median of In overall, the workflow that I would follow and I want to corroborate is: Create all seurat objects. As for running SCTransform on non-integer data, I would recommend asking that question on the Seurat or sctransform GitHub repositories. Pearson residual output from SCTransform (scaled. features options in the CreateSeurat May 21, 2023 · I am using Seurat V5 to analysis a single cell RNA dataset, which includes multiple samples. To keep this simple: You should use the integrated assay when trying to 'align' cell states that are shared across datasets (i. Normalize each dataset separately with SCTransform. Apr 10, 2020 · On the other hand, when you do a simple aggregation or concatenation of counts from different conditions, the same condition/treatment - specific differences are strikingly visible on the embedding (cells are grouped by cell-types but clusters are split by treatment/condition). The Assay class stores single cell data. The method currently supports five integration methods. satijalab closed this as completed Jan 26, 2018. It is just a way to separate the cells in groups. 0. g. data information? In the Mixscape vignette, the pre-made eccite-seq data has many meta. You switched accounts on another tab or window. data for DE, but obj[["RNA"]]@data is recommended. Apr 22, 2024 · First can just run object name which should give an output like this: > pbmc An object of class Seurat 13714 features across 2700 samples within 1 assay Active assay: RNA (13714 features, 0 variable features) 2 layers present: counts, data. In this case, merge = donor effects. gz) Code: df <- Read10X (data. But there are only data slot and scaledata slot on the integrated assay. May 16, 2023 · 3 layers present: data, counts, scale. If you run NormalizeData and then run FetchData it should work just fine. Could anyone let me know how to import single-cell CRISPR screening data into Seurat with meta. data used as input to calculate the Explore the GitHub Discussions forum for satijalab seurat. gz But when I try to use Read10X and CreateSeuratObject function in r, it generates empty seurat object. Hi Tim, Thanks for the quick reply. mito. What is the difference between: vars. But since FPKM data can be viewed as normalized counts, a log-normalized count data (from Seurat) and your FPKM data are still comparable. data" layer. Jun 7, 2023 · You signed in with another tab or window. ) You should use the RNA assay when exploring the genes that change either across clusters, trajectories, or conditions. Is the scaled. Aug 16, 2019 · You signed in with another tab or window. integrated. I have seen people use ScaleData () with vars. 1 it is missing scale. Since sc-data are generally UMI-based data, the assumption of FPKM is not satisfied. But when I checked it in my data, it was not the case. Extra data to regress out, should be cells x latent data. perform SCTransform, with argument to regress out batch effects. I am trying to rename genes in Seurat. Each slide contains both tumor and control specimens derived from four different donors. data slot under assay. As both are just the average gene expression values from a group of cells from each of the identified cell-type or cluster. k3yavi. For example, splitting data that has been scaled between -1 and 1 with a "splitting threshold" of 0 would be the same as splitting unscaled data Oct 31, 2023 · We next use the count matrix to create a Seurat object. We can load in the data, remove low-quality cells, and obtain predicted cell annotations (which will be useful for assessing integration later), using our Azimuth pipeline. However, I do not see any step where it filters cells that have mitochondrial counts. combined_FCRPB and my code. Instead, Seurat can be used to generate a corrected cell/cell distance matrix. We've changed the behavior of split so that it only splits "counts" and "data" layers by default. But if you want to keep it you can always store it in object@misc as follows: pbmc@misc [[ "seurat_data" ]] <- as. Mar 27, 2024 · Hi, I have two samples and got per sample outs: HTO1 and HTO2. . for (i in 1:length(combinedList)) {. May 15, 2020 · I was told that the Default Assay should be "integrated" when running pca and creating UMAPS/tSNE plots, and "RNA" when finding markers, running differential gene expression and for heatplots/dotplot; I was also told that the "RNA" assay must be scaled for heatplots/dotplots. by = "Sample") combinedList. to. Aug 8, 2019 · I'm going through the "Using SCTransform" vignette and attempting to replace NormalizeDate, ScaleData, and FindVariableFeatures in my code with SCTransform. Aug 16, 2022 · However, I came across a few articles discussing the implication of log-normalization on the count data (ref this and this). data such as guide ID, gene, NT. for clustering, visualization, learning pseudotime, etc. tsv. May 2, 2023 · But #5199 @yuhanH advised not to rerun RunPCA. Jun 26, 2020 · You signed in with another tab or window. I want to load them into one Seurat V5 object and integrate them, but I don't know how the load them at first. You signed out in another tab or window. From your file example, "R_annotation" would work, or "R_annotation_broad" which contains less granular cell types, or "R_annotation_broad_extrapolated". The normalized_counts are corrected for sequencing depth by using a "scale factor" approach - divide counts by total sequencing depth and scale it to 10k before log transformation. 👍 1. So the workaround I can think of is to create the Seurat object without this gene, do the log normalization manually with that special gene’s counts taken into consideration, and put it in Seurat’s data slot. 1. To install the development version of Seurat, please see the instructions here. The end goal is to get a gene counts matrix for the single cell data and use for the deconvolution process. Nov 14, 2019 · Hello, Sorry for the delay. . integration and normalization guides provided in the Seurat documentation (Introduction to Integration and Seurat v5 Integration). Nicolaas Hi, I am certainly not on the official Seurat team, but I had a similar issue and I can tell you how I got around it. As far as I have seen, the function does require to ha Jun 11, 2019 · @satijalab Hello, thank you for your explanation. /path_to Feb 9, 2024 · If you run SCTransform and use VlnPlot it fetches data from the data slot of "SCT" assay unless you speciify the assay (such as rna_CD8 in your example. Integrate = remove donor effect. R package for normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression The sctransform package was developed by Christoph Hafemeister in Rahul Satija's lab at the New York Genome Center and described in Hafemeister and Satija, Genome Biology 2019 . Feb 21, 2022 · Feb 21, 2022. ) It appears that Seurat applies a scaling factor that brings up the noise of antibody Jan 3, 2024 · It doesn't. We and others have found that working on a subset of variable genes is sufficient for clustering and integration analyses. cells and min. The answer to this depends on the context. Thank you very much for the prompt response, please may i clarify the correct use of the FindMarker function for when data is integrated, just to confirm that the correct slot is used by default. You signed in with another tab or window. Hi, I have two questions related to the FindAllMarkers function: I am trying to run the FindAllMarkers function on a Seurat object without using the normalisation provided by Seurat. May be I am getting it completely wrong. method 1) merge seurat objects from independent experiments. vars in RegressOut). Jul 5, 2023 · This question is covered in the FAQs but to summarize you should run FindMarkers on the RNA or SCT assay. Q2 is clear, I was not reverting back to 'RNA' the default assay. Add PseudobulkExpression to normalize the count data present in a given assay. a single gene renamed. Thanks:) Dec 14, 2023 · You signed in with another tab or window. For that end, I: obj <- SetAssayData (obj, layer = layer. And monocle people advise to give the counts slot not the Dec 14, 2023 · Thanks for the update of Seurat to process the spatial transcriptome data. I do not know if Monocle accepts this as input for trajectory reconstruction, but if it does, then this should be doable. seurat and to perform the pseudotime analysis. -. name, new. We had anticipated extending Seurat to actively support DE using the pearson residuals of sctransform, but have decided not to do so. Because we try to replicate standard R behavior, especially with regards to data frame-like functionality, this is not something we can correct. As you can see from the plots, relative count normalization makes the counts more normally distributed than log-normalize counts ( I see this being used in almost all Seurat implementations). list[[smpl]] An object of class Seurat 34674 features across 996 samples within 2 assays Active assay: SCT (16446 features, 2000 variable Seurat provides a great workflow for data integration. Similar to the min. I have few questions here: Jun 5, 2020 · The data I can only have is a . txt file of batch normalized data, without the raw count data. h5 file containing both scRNA-seq and scATAC-seq data, but I have done the an Jul 1, 2022 · Hello. For more information, check out our [Seurat object interaction vignette], or our GitHub Wiki. Of course, the remaining genes are still present in the object, in the original RNA assay, so no data has been lost. data for functions that identify structure in the data, such as dimensionality reduction, as this will tend to give lowly and highly expressed genes equal weight. This message is displayed once per session. Thanks for bringing this up! Hello, This is a 2-in-1 issue (A, B). run integration (as outlined in your vignettes) Do you expect the downstream result of either pipeline to be interchangeable, or do you I noticed the default layer used by FetchData in Seurat V5 (for Assay5 objects) seems to be the counts layer. This issue should be linked with both #8004 and #7936, but this case is slightly different as I am only working with v5 objects and I am not trying to save. saketkc closed this as completed Feb 19, 2021. dir = ". latent. My primary goal is to analyze normalized integrated counts outside of Seurat, specifically for generating heatmaps and exploring correlations with external tools. Apr 25, 2020 · downsample these 10 to 1500 cells per sample and integrate, check DEG between clusters using SCT: works fine BUT when I compare healthy vs sick inside a cluster, the read depth is uneven and I get housekeeping genes. Nov 4, 2020 · jaisonj708 commented on Nov 6, 2020. Dec 25, 2019 · And also I have one follow up question about the residuals. control PBMC datasets" #3. Jul 1, 2022 · Hello. It is my first time analysing demultiplexed data a About Seurat. Seuratobjects were created successfully however, when I am using Seurat_5. Add Read10X_probe_metadata to read the probe metadata from a 10x Genomics probe barcode matrix file in HDF5 format. when carrying out the following: combinedList <- SplitObject(merged, split. e. To accomplish this, we opted to distribute datasets through individual R packages. We note that Visium HD data is generated from spatially patterned olignocleotides labeled in 2um x 2um bins. Aug 11, 2020 · Actually my doubt is Seurat's AverageExpression() should exactly be the same as muscat's aggregateData() with fun="mean". Each of these methods performs integration in low-dimensional space, and returns a dimensional reduction (i. 0), and I got a folder filtered_feature_bc_matrix which contains three files: barcodes. Hi there, Is it possible to select a different slot of the object when using the FeaturePlot function? For example, if one is interested in plotting counts instead of data? Thank you! Jan 5, 2024 · Dear all, I am trying to create a gene activity matrix of a scATAC-seq dataset, but I am always getting this error: Now, I am new to Seurat, and my input is a . Jul 12, 2019 · Does it mean the UMAP plot of integrated data set were done based on the 2000 features? Finally, as I'm trying to run Monocle and URD using integrated data sets by Seurat, the only way I can think of is to export the integrated normalized counts from an integrated Seurat object, which would be data from the "integrated" slot. And it cannot be Jan 26, 2024 · I was trying to create seuratobject by using ReadMtx function followed by CreateSeuratObject. This assay will also store multiple 'transformations' of the data, including raw counts (@counts slot), normalized data (@data slot), and scaled data for Mar 14, 2022 · In the V2 model, we do the DE on corrected counts (after additionally adjusting for library size differences across models) so you can use the data slot to perform DE if using the v2 model - we also show this in the vignette. An object of class Seurat 32960 features across 49505 samples within 2 satijalab commented Jul 12, 2019. Aug 17, 2018 · satijalab edited this page Aug 17, 2018 · 10 revisions. For example, nUMI, or percent. To accomplish this, we opted to distribute datasets through individual R Apr 24, 2017 · edited by mojaveazure. Apr 9, 2024 · You signed in with another tab or window. I loaded each of them and build Seurat object respectively as the following code: May 15, 2020 · I was told that the Default Assay should be "integrated" when running pca and creating UMAPS/tSNE plots, and "RNA" when finding markers, running differential gene expression and for heatplots/dotplot; I was also told that the "RNA" assay must be scaled for heatplots/dotplots. data. Option A: use Cell Ranger's "aggr", which subsamples reads from higher-depth libraries until all libraries have an equal number of confidently mapped reads per cell. gz, features. This is not a bug, but a replication of standard R behavior. And, when I imported this object in Python, the number of genes was reduced from 16,783 genes to 10,466 genes (the number of cells remained the same) This issue however is likely not due to the pipeline above because with a "regularly" processed Seurat object, the number of genes also got reduced Dec 1, 2023 · I did normalise and scale the object before attempting the integration, and the same piece of code was working in the beta version of Seurat. We created SeuratData in order to distribute datasets for Seurat vignettes in as painless and reproducible a way as possible. Users should be comfortable with base-R functionality when using Seurat Jun 24, 2019 · You signed in with another tab or window. Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. HI, I have a question. If you are doing DE, we recommend that it be done on the raw counts and not the integrated assay. The object serves as a container that contains both data (like the count matrix) and analysis (like PCA, or clustering results) for a single-cell dataset. Hello, I realize this question has been asked several times but I feel as if I haven't been able to find an answer applicable to my situation. You can also increase the number of features if you wish. rna_CD8 will fetch data from the RNA assay and in this case it is unnormalized (because you didn't run NormalizeData prior to SCTransform (you aren't expected to)) So your initial set-up is totally different from say a pancreas sample or whatever taken from 5 donors. There is high variability in the number of cells. However, when I look for specific genes using GetAssayData I am able to find counts greater than zero using the original normalization method, but the counts are zero for the SCTransform satijalab commented on Jun 21, 2019. matrix( x = pbmc@data) Make sure that the output of scran is not log transformed before computing Apr 16, 2020 · I am working to integrate two datasets from different conditions using your vignette "Integrating stimulated vs. You mean we should use the intergrated data for constructing a pseudotime trajectory. Apr 25, 2021 · The data slot in RNA is simply log1p(normalized_counts) while in SCT this is log1p(corrected_counts). igrabski closed this as completed Nov 17, 2023. Sep 23, 2021 · @Ryan-3 any annotation can be used in Seurat. bx ox nu ke pj hy sn th qp bx