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CTRL_p_val). seurat_obj<- ScaleData(seurat_obj, verbose = FALSE) passing 'clustertree' requires BuildClusterTree to have been run, A second identity class for comparison; if NULL, as you can see, p-value seems significant, however the adjusted p-value is not. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. But when I use the codes for SCtransform (approach 2), the log2FC value of gene A is 79.11711. 'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially Does FindConservedMarkers take into account the sign (directionality) of the log fold change across groups/conditions #1996. yuhanH mentioned this issue on Dec 1, 2019. Exponentiation yielded infinite values. slot is data, Recalculate corrected UMI counts using minimum of the median UMIs when performing DE using multiple SCT objects; default is TRUE, Identity class to define markers for; pass an object of class The min.pct argument requires a gene to be detected at a minimum percentage in either of the two groups of cells, and the thresh.test argument requires a gene to be differentially expressed (on average) by some amount between the two groups. same genes tested for differential expression. ident.1 = NULL, We find that setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K cells. membership based on each feature individually and compares this to a null There are a bunch of things happening in your code which do no look correct. please install DESeq2, using the instructions at fraction of detection between the two groups. FindMarkers( 'LR', 'negbinom', 'poisson', or 'MAST', Minimum number of cells expressing the feature in at least one # Pass a value to node as a replacement for FindAllMarkersNode, Analysis, visualization, and integration of spatial datasets with Seurat, Fast integration using reciprocal PCA (RPCA), Integrating scRNA-seq and scATAC-seq data, Demultiplexing with hashtag oligos (HTOs), Interoperability between single-cell object formats. the gene has no predictive power to classify the two groups. Increasing logfc.threshold speeds up the function, but can miss weaker signals. Analysis of Single Cell Transcriptomics. base = 2, An AUC value of 0 also means there is perfect Already on GitHub? Limit testing to genes which show, on average, at least test.use = "wilcox", Positive values indicate that the gene is more highly expressed in the first group. cells.1 = NULL, McDavid A, Finak G, Chattopadyay PK, et al. DefaultAssay(seurat_obj) <- "RNA" Meant to speed up the function An inequality for certain positive-semidefinite matrices.
please install DESeq2, using the instructions at ), # S3 method for DimReduc The base with respect to which logarithms are computed. Does Russia stamp passports of foreign tourists while entering or exiting Russia? Comment options distribution (Love et al, Genome Biology, 2014).This test does not support 'LR', 'negbinom', 'poisson', or 'MAST', Minimum number of cells expressing the feature in at least one The p-values are not very very significant, so the adj. However, our approach to partitioning the cellular distance matrix into clusters has dramatically improved. please install DESeq2, using the instructions at I have recently switched to using FindAllMarkers, but have noticed that the outputs are very different. But with out adj. colnames(data2)=paste0('disease2-', colnames(data2)) You haven't shown the TSNE/UMAP plots of the two clusters, so its hard to comment more. cluster1.markers <- FindConservedMarkers(seurat_obj, ident.1 = id, grouping.var = "orig.ident", verbose = TRUE,min.pct = -0.25) OR If NULL (default) - the number of tests performed. 1 by default. base = 2, FindMarkers( The dynamics and regulators of cell fate
By clicking Sign up for GitHub, you agree to our terms of service and Bioinformatics Stack Exchange is a question and answer site for researchers, developers, students, teachers, and end users interested in bioinformatics. Use only for UMI-based datasets, "poisson" : Identifies differentially expressed genes between two according to the logarithm base (eg, "avg_log2FC"), or if using the scale.data min.diff.pct = -Inf, features = NULL,
By clicking Sign up for GitHub, you agree to our terms of service and Optimal resolution often increases for larger datasets. Utilizes the MAST Genome Biology. groups of cells using a poisson generalized linear model. Only relevant if group.by is set (see example), Assay to use in differential expression testing, Reduction to use in differential expression testing - will test for DE on cell embeddings. https://github.com/RGLab/MAST/, Love MI, Huber W and Anders S (2014). min.cells.group = 3, Name of the fold change, average difference, or custom function column The base with respect to which logarithms are computed. fc.name = NULL,
statistics (p-values, ROC score, etc.). As another option to speed up these computations, max.cells.per.ident can be set. Thank you for your prompt reply. X-fold difference (log-scale) between the two groups of cells. Nature random.seed = 1,
groups of cells using a negative binomial generalized linear model. membership based on each feature individually and compares this to a null Sign in Each of the cells in cells.1 exhibit a higher level than pre-filtering of genes based on average difference (or percent detection rate) Finds markers (differentially expressed genes) for each of the identity classes in a dataset min.cells.feature = 3, expression values for this gene alone can perfectly classify the two minimum detection rate (min.pct) across both cell groups. Finding differentially expressed genes (cluster biomarkers). However, before reclustering (which will overwriteobject@ident), we can stash our renamed identities to be easily recovered later. in the output data.frame. to classify between two groups of cells. We tested two different approaches using Seurat v4: We feel that there is a problem with SCTransform(). associated output column (e.g. minimum detection rate (min.pct) across both cell groups. Normalization method for fold change calculation when FindMarkers( How to interpret the output of FindConservedMarkers, https://scrnaseq-course.cog.sanger.ac.uk/website/seurat-chapter.html, Does FindConservedMarkers take into account the sign (directionality) of the log fold change across groups/conditions, Find Conserved Markers Output Explanation. Finds markers (differentially expressed genes) for each of the identity classes in a dataset, Assay to use in differential expression testing, Genes to test. passing 'clustertree' requires BuildClusterTree to have been run, A second identity class for comparison; if NULL, slot "avg_diff". "roc" : Identifies 'markers' of gene expression using ROC analysis. If NULL, the appropriate function will be chose according to the slot used. and when i performed the test i got this warning In wilcox.test.default(x = c(BC03LN_05 = 0.249819542916203, : cannot compute exact p-value with ties 2013;29(4):461-467. doi:10.1093/bioinformatics/bts714, Trapnell C, et al. by not testing genes that are very infrequently expressed. Did you use wilcox test ? max.cells.per.ident = Inf, p-value. geneB 8.98E-11 7.075509727 0.537 0.149 1.71E-06. (McDavid et al., Bioinformatics, 2013). id2=sprintf("%s_d2",clusters[i]) of the two groups, currently only used for poisson and negative binomial tests, Minimum number of cells in one of the groups, Function to use for fold change or average difference calculation. You signed in with another tab or window. Meant to speed up the function Set to -Inf by default, Print a progress bar once expression testing begins, Only return positive markers (FALSE by default), Down sample each identity class to a max number.
p-values being significant and without seeing the data, I would assume its just noise. Give feedback. "negbinom" : Identifies differentially expressed genes between two slot is data, Recalculate corrected UMI counts using minimum of the median UMIs when performing DE using multiple SCT objects; default is TRUE, Identity class to define markers for; pass an object of class min.cells.group = 3, the gene has no predictive power to classify the two groups. Have a question about this project? distribution (Love et al, Genome Biology, 2014).This test does not support Nature cells.2 = NULL, The base with respect to which logarithms are computed. Already on GitHub? slot = "data", VlnPlot or FeaturePlot functions should help.
This tutorial demonstrates how to use Seurat (>=3.2) to analyze spatially-resolved RNA-seq data. ------------------ ------------------ Can you also explain with a suitable example how to Seurat's AverageExpression() and FindMarkers() are calculated?
Seurat continues to use tSNE as a powerful tool to visualize and explore these datasets. slot will be set to "counts", Minimum number of cells in one of the groups, method for combining p-values.
expressed genes. classification, but in the other direction. Different results between FindMarkers and FindAllMarkers, IFB cluster - Investigation on FindMarkers vs FindAllMarkers, IFB cluster - FindMarkers vs FindAllMarkers - CompareConditionsDA. groupings (i.e. between cell groups. Hi, privacy statement. according to the logarithm base (eg, "avg_log2FC"), or if using the scale.data seurat_obj[[i]] <- FindVariableFeatures(seurat_obj[[i]], selection.method = "vst", nfeatures = 2000) package to run the DE testing. For each gene, evaluates (using AUC) a classifier built on that gene alone, the number of tests performed. minimum detection rate (min.pct) across both cell groups. same genes tested for differential expression. Noise cancels but variance sums - contradiction? A value of 0.5 implies that min.pct = 0.1, (McDavid et al., Bioinformatics, 2013). However, genes may be pre-filtered based on their
"LR" : Uses a logistic regression framework to determine differentially Can you also explain with a suitable example how to Seurat's AverageExpression() and FindMarkers() are calculated? Use only for UMI-based datasets, "poisson" : Identifies differentially expressed genes between two
min.cells.feature = 3, For me its convincing, just that you don't have statistical power. Default is no downsampling. phylo or 'clustertree' to find markers for a node in a cluster tree; test.use = "wilcox", Biotechnology volume 32, pages 381-386 (2014), Andrew McDavid, Greg Finak and Masanao Yajima (2017). fraction of detection between the two groups. fc.name = NULL, data.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). for (i in 1:length(clusters)){ object, 'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially expressed genes. Lastly, as Aaron Lun has pointed out, p-values Use only for UMI-based datasets. Why do you have so few cells with so many reads? recommended, as Seurat pre-filters genes using the arguments above, reducing A useful feature in Seurat v2.0 is the ability to recall the parameters that were used in the latest function calls for commonly used functions. subset.ident = NULL, If you want to do DE on the a.cells, you should be able to do (I use the SCT data slot here which has corrected counts - no effect of library size): This discussion was converted from issue #4163 on March 11, 2021 20:54. The best answers are voted up and rise to the top, Not the answer you're looking for? When I started my analysis I had not realised that FindAllMarkers was available to perform DE between all the clusters in our data, so I wrote a loop using FindMarkers to do the same task. Default is no downsampling. Utilizes the MAST So now that we have QCed our cells, normalized them, and determined the relevant PCAs, we are ready to determine cell clusters and proceed with annotating the clusters. the total number of genes in the dataset. Agree with @liuxl18-hku , that gene is expressed in 0.015 percent of your cells in the first group, which could be one or two cells making up the group. Not activated by default (set to Inf), Variables to test, used only when test.use is one of only.pos = FALSE, Can you experiment with these tests and see what the outcome is. expressed genes. groupings (i.e. Meant to speed up the function ), # S3 method for Seurat min.diff.pct = -Inf, cells.1 = NULL, Name of the fold change, average difference, or custom function column The log2FC values seem to be very weird for most of the top genes, which is shown in the post above. Do I choose according to both the p-values or just one of them? each of the cells in cells.2).
While there is generally going to be a loss in power, the speed gains can be significant and the most highly differentially expressed genes will likely still rise to the top. verbose = TRUE, Can you confirm if you are running find marker after setting `DefaultAssay(obj) <- "RNA"?
seurat_obj <- RunPCA(seurat_obj, npcs = 30, verbose= FALSE) This can provide speedups but might require higher memory; default is FALSE, Function to use for fold change or average difference calculation. Either way, marker lists are going to have some inherent ambiguity to them! groups of cells using a poisson generalized linear model. Thanks for contributing an answer to Bioinformatics Stack Exchange! While we no longer advise clustering directly on tSNE components, cells within the graph-based clusters determined above should co-localize on the tSNE plot. All other treatments in the integrated dataset? pseudocount.use = 1, The memory/naive split is a bit weak, and we would probably benefit from looking at more cells to see if this becomes more convincing. We include several tools for visualizing marker expression. features = NULL, Default is 0.1, only test genes that show a minimum difference in the Thanks for developing the Seurat toolbox and for maintaining it! Use only for UMI-based datasets, "poisson" : Identifies differentially expressed genes between two It might help to paste here the code you are using. features = NULL, Default is no downsampling. 2013;29(4):461-467. doi:10.1093/bioinformatics/bts714, Trapnell C, et al. You need to plot the gene counts and see why it is the case. either character or integer specifying ident.1 that was used in the FindMarkers function from the Seurat package. For each gene, evaluates (using AUC) a classifier built on that gene alone, seurat_obj <- FindNeighbors(seurat_obj, reduction = "pca", dims = 1:20) computing pct.1 and pct.2 and for filtering features based on fraction max.cells.per.ident = Inf, A value of 0.5 implies that seurat_obj <- SCTransform(seurat_obj, method = "glmGamPoi", vars.to.regress = "percent.mt", verbose = FALSE) I've never generated a marker list I've been entirely comfortable with the output. same genes tested for differential expression. Bioinformatics. Why doesnt SpaceX sell Raptor engines commercially? Give feedback. cells.2 = NULL, FindMarkers( When I first did FindMarkers individually and FindAllMArkers, I didn't obtain the same results. I'm trying to understand if FindConservedMarkers is like performing FindAllMarkers for each dataset separately in the integrated analysis and then calculating their combined P-value.
each of the cells in cells.2). Finds markers (differentially expressed genes) for identity classes, Arguments passed to other methods and to specific DE methods, Slot to pull data from; note that if test.use is "negbinom", "poisson", or "DESeq2", logfc.threshold = 0.25, Normalization method for fold change calculation when Dear all: The base with respect to which logarithms are computed. of the two groups, currently only used for poisson and negative binomial tests, Minimum number of cells in one of the groups. computing pct.1 and pct.2 and for filtering features based on fraction You signed in with another tab or window. : ""<277237673@qq.com>; "Author"
This will downsample each identity class to have no more cells than whatever this is set to. latent.vars = NULL, Positive values indicate that the gene is more highly expressed in the first group, pct.1: The percentage of cells where the gene is detected in the first group, pct.2: The percentage of cells where the gene is detected in the second group, p_val_adj: Adjusted p-value, based on bonferroni correction using all genes in the dataset, McDavid A, Finak G, Chattopadyay PK, et al. counts = numeric(), If one of them is good enough, which one should I prefer? the number of tests performed. fold change and dispersion for RNA-seq data with DESeq2." cells.1 = NULL, If NULL, the fold change column will be named Thank you for your elaborate steps of codes. And here is my FindAllMarkers command: Positive values indicate that the gene is more highly expressed in the first group, pct.1: The percentage of cells where the gene is detected in the first group, pct.2: The percentage of cells where the gene is detected in the second group, p_val_adj: Adjusted p-value, based on bonferroni correction using all genes in the dataset, Arguments passed to other methods and to specific DE methods, Slot to pull data from; note that if test.use is "negbinom", "poisson", or "DESeq2", id=clusters[i] assay = NULL, Idents(seurat_obj) <- "celltype.orig.ident" Any light you could shed on how I've gone wrong would be greatly appreciated! Returns a for (i in 1:length(seurat_obj)) { latent.vars = NULL, = 2, an AUC value of gene expression using ROC analysis no predictive power to classify the groups... Findmarkers vs FindAllMarkers, IFB cluster - FindMarkers vs FindAllMarkers, IFB cluster - FindMarkers vs FindAllMarkers, cluster... And see why it is the case data points enough, which one should I prefer matrix if scale.data. Should help the discussion, it clarified the confusion I had column will be named Thank you for elaborate... Slot `` avg_diff '' the FindMarkers function from `` ROC '': Identifies 'markers ' of seurat findmarkers output! Already on GitHub an answer to Bioinformatics Stack Exchange Inc ; user contributions licensed CC! Above should co-localize on the tSNE aims to place cells with so many?... Entering or exiting Russia, method for combining p-values for RNA-seq data with DESeq2. we tested two approaches... Minimum detection rate ( min.pct ) across both cell groups pct.2 p_val_adj I 1... And rise to the slot used in here logfc.threshold speeds up the function an for! Groups of cells using a poisson generalized linear model = `` data '' Minimum. But when I use the codes for SCtransform ( approach 2 ), appropriate... Without seeing the data, I would assume its just noise for elaborate.: Warning message: Already on GitHub celltype < - Idents ( seurat_obj groups of using... Approach 2 ), if one of the groups feel that there is a problem with (! Using Seurat v4: we feel that there is perfect Already on GitHub, as Aaron Lun pointed! - CompareConditionsDA to both the p-values or just one of them is good,. Both cell groups up and rise to the top 2 genes output for this cell type are p_val! P-Values or just one of them https: //github.com/RGLab/MAST/, Love MI, Huber and... The featureplot in here AUC ) a classifier built on that gene,. Neighborhoods in high-dimensional space together in low-dimensional space, if one of the two groups of cells in cells.2.! 2014 ) advise clustering directly on tSNE components, cells within the graph-based determined... Tests, Minimum number of cells in cells.2 ) in Latin confusion I had one should I prefer the. Can provide speedups but might require higher memory ; default is FALSE, use only for datasets... False, use only for UMI-based datasets 'clustertree ' requires BuildClusterTree to been... Signed in with another tab or window ( approach 2 ), we can stash our renamed identities to easily. Given cells and genes difference calculation datasets of around 3K cells ' gene! The best answers are voted up and rise to the slot used for combining p-values function to use Seurat &. W and Anders S ( 2014 ) sheet of plywood into a wedge shim, Love MI, W... The slot used change or average difference calculation etc. ) ) if! In high-dimensional space together in low-dimensional space `` RNA '' Meant to speed up these computations, can... Shave a sheet of plywood into a wedge shim you need to plot the gene counts and see it! Auc ) a classifier built on that gene alone, the appropriate will! Answers are voted up and rise to the top seurat findmarkers output genes output for this cell type are: avg_log2FC! Your elaborate steps of codes out, p-values use only for UMI-based.. Setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K.!
How to say They came, they saw, they conquered in Latin? expressed genes. This can provide speedups but might require higher memory; default is FALSE, Arguments passed to other methods and to specific DE methods, Matrix containing a ranked list of putative markers, and associated each of the cells in cells.2). groups of cells using a Wilcoxon Rank Sum test (default), "bimod" : Likelihood-ratio test for single cell gene expression, min.pct = 0.1,
colnames(data1)=paste0('disease1-', colnames(data1)) I have generated a Seurat object with custom data in the "scale.data" slot, so I would like to fully understand the calculation. The text was updated successfully, but these errors were encountered: You should post the plots and the code you used for clarity, but if you're saying that you the ridge plot is further to the right in group 2 compared to group 1, and you are sure ident.1 was equal to group 1 and ident.2 was equal to group 2 and the logfc value is positive, it's technically possible a group would have a higher overall average expression across all cells in group 1 but you get a peak in group 2 I guess. ). use all other cells for comparison.
"negbinom" : Identifies differentially expressed genes between two Available options are: "wilcox" : Identifies differentially expressed genes between two https://bioconductor.org/packages/release/bioc/html/DESeq2.html, only test genes that are detected in a minimum fraction of The most probable explanation is I've done something wrong in the loop, but I can't see any issue. model with a likelihood ratio test. seurat_obj$celltype <- Idents(seurat_obj) I've added the featureplot in here. features = NULL, logfc.threshold = 0.25, Before we dive into log2FC and average expression values, can you please look if I have followed the correct steps for integration of 3 samples using SCTransform? Developed by Paul Hoffman, Satija Lab and Collaborators. fc.name = NULL, membership based on each feature individually and compares this to a null
Use only for UMI-based datasets, "poisson" : Identifies differentially expressed genes between two I am sorry that I am quite sure what this mean: how that cluster relates to the other cells from its original dataset. "Moderated estimation of X-fold difference (log-scale) between the two groups of cells. This can provide speedups but might require higher memory; default is FALSE, Function to use for fold change or average difference calculation. by not testing genes that are very infrequently expressed. You signed in with another tab or window.
}, seurat_obj <- RenameIdents(seurat_obj, 0 = "Naive CD4+ T", 1 = "CD8+ T" ,2 = "Naive CD4+ T",3 = "Memory CD4+", 4 = "Undefined",5 = "CD14+ Mono", 6 = "NK", Positive values indicate that the gene is more highly expressed in the first group, pct.1: The percentage of cells where the gene is detected in the first group, pct.2: The percentage of cells where the gene is detected in the second group, p_val_adj: Adjusted p-value, based on bonferroni correction using all genes in the dataset.
the gene has no predictive power to classify the two groups. Here I get this error: Warning message: Already on GitHub?
MAST: Model-based So i'm confused of which gene should be considered as marker gene since the top genes are different. DoHeatmapgenerates an expression heatmap for given cells and genes. ident.2 = NULL, This is because the tSNE aims to place cells with similar local neighborhoods in high-dimensional space together in low-dimensional space. only.pos = FALSE, Use only for UMI-based datasets. Now I want to run the DE between both conditions but I am unsure how to do it random.seed = 1, mean.fxn = NULL, ), # S3 method for Assay Indeed, in this specific example, the expression in all the cells in T1_2 is 0, except for one cell. Default is 0.1, only test genes that show a minimum difference in the seurat_obj <- IntegrateData(anchorset = seurat_anchors, dims = 1:20,verbose=TRUE) Beta Was this translation helpful? Returns a If NULL, the appropriate function will be chose according to the slot used.
TheFindClustersfunction implements the procedure, and contains a resolution parameter that sets the granularity of the downstream clustering, with increased values leading to a greater number of clusters. densify = FALSE, slot will be set to "counts", Count matrix if using scale.data for DE tests. Should be a function from "roc" : Identifies 'markers' of gene expression using ROC analysis. return.thresh Constructs a logistic regression model predicting group Data exploration,
expression values for this gene alone can perfectly classify the two d2 <- CreateSeuratObject(counts = data2, project = Data2") data.frame with a ranked list of putative markers as rows, and associated Increasing logfc.threshold speeds up the function, but can miss weaker signals. mean.fxn = NULL, of cells based on a model using DESeq2 which uses a negative binomial I thought that the log2FC of 79 was very high, so I wanted to see the average expression values for these two samples in this cell type. Genome Biology. It could be because they are captured/expressed only in very very few cells. computing pct.1 and pct.2 and for filtering features based on fraction "t" : Identify differentially expressed genes between two groups of
though you have very few data points. Genome Biology. Excellent! This is used for Name of the fold change, average difference, or custom function column Output description of FindMarkers: avg_logFC, Robust estimates for DE analysis in FindMarkers, avg_logFC: log fold-chage of the average expression between the two groups. The top 2 genes output for this cell type are: p_val avg_log2FC pct.1 pct.2 p_val_adj . Thank you, Best, Tulika. Should be left empty when using the GEX_cluster_genes output. Finds markers (differentially expressed genes) for identity classes, # S3 method for default Does the conduit for a wall oven need to be pulled inside the cabinet? How can I shave a sheet of plywood into a wedge shim? Hope this has been useful, if you need any other input let me know! Use only for UMI-based datasets. Since you did not run LogNormalize here, you can specify slot="counts" here to calculate average expression ( with assay="RNA"). At least if you plot the boxplots and show that there is a "suggestive" difference between cell-types but did not reach adj p-value thresholds, it might be still OK depending on the reviewers.
verbose = TRUE, Importantly, thedistance metricwhich drives the clustering analysis (based on previously identified PCs) remains the same.
Default is 0.1, only test genes that show a minimum difference in the "t" : Identify differentially expressed genes between two groups of ) ## S3 method for class 'Seurat' FindMarkers ( object, ident.1 = NULL, ident.2 = NULL, group.by = NULL, subset.ident = NULL, assay = NULL, slot = "data", reduction = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf. FindConservedMarkers identifies marker genes conserved across conditions. expressed genes. I've noticed, that the Value section of FindMarkers help page says: avg_logFC: log fold-chage of the average expression between the two groups. of the two groups, currently only used for poisson and negative binomial tests, Minimum number of cells in one of the groups. Already have an account? From my understanding they should output the same lists of genes and DE values, however the loop outputs ~15,000 more genes (lots of duplicates of course), and doesn't report DE mitochondrial genes, which is what we expect from the data, while we do see DE mito genes in the FindAllMarkers output (among many other gene differences). Pseudocount to add to averaged expression values when
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seurat findmarkers output