Scanpy filter genes github adata. I am new to Scanpy and I followed this tutorial link below.

Scanpy filter genes github adata 👍 1 ViHammer reacted with thumbs up emoji All reactions Saved searches Use saved searches to filter your results more quickly Scanpy stores the loadings for each PC in the adata. 7: sc. scanpy. X for the estimation of driver genes or include a feature to support this. since I am new to python and scanpy I am not sure how can it be done and if there is already a function for that. recarray to be indexed by group ids 'scores', sorted np. Hello, I am working with an adata object (adata. uns['rank_genes_groups_filtered']. Cancel Create saved search I also understand that adding rpy2 to scanpy could be a bit [Yes ] I have checked that this issue has not already been reported. score_genes fails when run on adata is the usual AnnData object you are working with. Pick a username Saved searches Use saved searches to filter your results more quickly It should also be pointed out that flavour="seurat_v3" accepts counts in sc. What happened? Trying to store normalised values in a layer 'normalised', then plot from that layer with sc. loc[clu*50+row+1]=[adata. Cool! It did solve my problems. raw. How far is #167 from being merged? For now I guess I can Hello Scanpy, It's very smooth to subset the adata by HVGs when doing adata = adata[:, adata. e those in adata. uns['rank_genes adata. startswith Saved searches Use saved searches to filter your results more quickly Hey, while writing tests for #1715 I noted the following behavior:. filter_genes (adata, min_cells = 10) # with <3298x24714 sparse matrix of type '<class 'numpy. If you want the up-regulated genes in 'ctrl'compared Kindly advise on how to include all high quality genes i. filter_genes_dispersion(adata, n_top_genes=x) actually returns x - num_zero_expression_genes genes instead of x, where num_zero_expression_genes Your data may have been pre-processed to take out mitochondrial genes. You could also check if you have any mitochondrial genes by just outputting this line: adata. obs_names and adata. Scanpy doesn't automatically filter out mitochondrial genes. Minimal code sample (that we can copy&paste without having any data) sc. nih. I wrote a function to show the 3D plot of the UMAP, tSNE and PCA spac As setting groups to ['0', '1', '2'] should not change the reference dataset, exactly the same marker genes should be detected for the first and the second call of sc. ipynb for a detailed description sc. AnnData ` Annotated data matrix, where obsevations/cells are named by their barcode and variables/genes by gene name. Minimal code sample Saved searches Use saved searches to filter your results more quickly Use saved searches to filter your results more quickly. check adata. For me this was solved by filtering out genes that were not expressed in any cell! sc. highly_variable_genes(adata, n_top_genes=1000, flavor="cell_ranger") can contain a single gene leading to NaN values in the normalized expression vector which are removed here @giovp this issue can be closed since the documentation already states that "To preserve the original structure of adata. highly_variable] in the Scanpy pipeline. Names of observations and variables can be accessed via adata. However, when setting method to logreg, I get other marker genes. What happened? I am working with a set of 2 10x scRNA samples. var as pd. filter_genes(adata, min_counts=1) sc. filter_ working with the same input dataset (10X). Any help would be great. You signed out in another tab or window. When giving a plotting function the gene_symbols argument to specify that it should look in a column of var for var_names rather than look for them in the index, the underlying _prepare_dataframe function tries to find the var_names in adata. The only reason we aren’t doing that here is so you can see what each filter accomplishes. What happened? I have always had a question: do I need to scale my adata before running sc. mean(0) sc. copy()) before subsetting, or give the hvg one a new name like adata_hvg = adata[:, adata. Contribute to 728267035/scAFC development by creating an account on GitHub. Also if I take lists produced by A vs B Use saved searches to filter your results more quickly. I tried following the " Clustering 3K PBMCs Following a Seurat Tutorial" by trying to execute the following code: import numpy as np import pandas as pd i Hi, Reordering the categories of groups in obs leads to shuffling of marker genes to the wrong groups when using sc. Thus, different parameters can be tested quickly. var['highly_variable'] for HVGs and so it's often not used anymore. The standard scRNA-seq data preprocessing workflow includes filtering of cells/genes, normalization, scaling and selection of highly variables genes. rankdata on the columns (the PCs) to get their ranks. From: Fidel Ramirez notifications@github. rank_genes_gr I have checked that this issue has not already been reported. Off course, sc. I've noticed a very noticeable speed decrease with filter_rank_genes_groups between versions 1. raw even more important since all non-coding gene expression goes to adata. sum(axis=0) == 0)) returns true? Saved searches Use saved searches to filter your results more quickly def filter_cells(sparse_gpu_array, min_genes, max_genes, rows_per_batch=10000, barcodes=None): Filter cells that have genes greater than a max number of genes or less than a minimum number of genes. You switched accounts on another tab or window. rank_genes_groups for adata to check those genes are enriched in which group of cells. X `, cell names in ` adata. @aditisk that depends on what you put in adata. var fields are updated but shape stays the same ️ output = sc. highly_variable]. any(adata. For example, this code: Saved searches Use saved searches to filter your results more quickly I want to second this issue!! I just spent many hours digging into the source code to figure out why filter_rank_genes_groups was filtering out genes that reported really high fold changes from rank_genes_groups, only to discover the discrepancy in the fold change calculation. My question actually is: After I ran sc. X, flavor='cell_ranger', n_top_genes=n_top_genes, log=False) adata = adata[:, This depends on how the filtering is done I think. obs_df renaming the "keys" based on the "gene_symbols" param) should handle the adata. varm after sc. filter_genes(adata, min_cells=3) sc. Copy link Member. get. sc. I then used "adata. 5) sc. filter_rank_genes_groups() replaces gene names with "nan" values, I was trying to get top 1000 variable genes in 1. filter_genes_dispers If there are very few genes some of the bins in sc. varm['PCs'] slot. Sign up for GitHub /scanpy/scanpy/get. com Cc: "Heymann, Jurgen (NIH/NIDDK) [E]" heymannj@niddk. copy if copy else data cell_subset, number = materialize_as_ndarray (filter_cells (adata. set_figure_params(dpi=100, color_map=’viridis_r’) sets the parameters for the figures generated by ScanPy. Hi, I have a question about select highly-variable genes. Users can simply drop the NANs for each cluster column in the adata. api. Please refer to tutorial. does not recompute, simply saves the filtered data under adata. If one needs to manually compute the counts_per_cell before calling the function, then the whole convenience Saved searches Use saved searches to filter your results more quickly Filtering statement adata[adata[: , gene]. obs['condition'] which stores the categories 'mut' and 'ctrl', and that you are interested in adata. highest_expr_genes(). name_list is a string containing gene names and should be specified. highly_variable_genes(ad_sub, n_top_genes = 1000, batch_key = "Age", subset = True It looks like we might not be handling non-expressed genes in all of the highly variable genes implementations. 6. The gene IDs are stored in ` adata. Topics Trending Collections Enterprise adata: AnnData, min_counts: Optional[int] = None, min_cells: Optional[int] = None, max_counts: Optional[int] = None, inplace: bool = True,) -> Union[AnnData, None, Tuple[np. falexwolf commented Nov 12, 2018. dataset Filter the cells with high gene detection (putative doublets) with cutoffs 4100 for v3 chemistry and 2000 for v2. I am new to Scanpy and I followed this tutorial link below. filter log1p_total_counts, log1p_n_genes_by_counts,and pct_counts_mito) in the same step. (optional) I have confirmed this bug exists on the master branch of s Skip to content. scanpy_filter. I often receive errors because statistics cannot be calculated on these types of low count groups. ndarray, np. uns['rank_genes_groups']` 'names', sorted np. This is run on a copied anndata object, and I haven't been able to reproduce is on e. 7. The maximum value in the count matrix adata. tl. magic(adata,copy=True,name_list="all_genes",) After running magic when I looked to the relation of some some genes I realized nothing has happened because the plot I get is the same for both before magic and after magic: I am using scanpy rank genes groups, and rank genes group filter for differential expression analysis after using a classifier. str. recarray to be indexed by group ids (0:00:02) WARNING: Note that the tool Scanpy Filtercells allows you to put param-repeat multiple parameters at the same time (i. I read them, concatenated them and then I did basic filtering. index, but pl. raw, make a copy of the anndata object (adata_old = adata. umap?. n_genes cuts the name_list if the number specified is smaller then the length of the list, so set this high enough if you want to work with large data Below, you’ll find a step-by-step breakdown of the code block above: import scanpy as sc imports the ScanPy package and allows you to access its functions and classes using the sc alias. You can keep Saved searches Use saved searches to filter your results more quickly There is a further issue with this version of the function as well. github. sum(adata. readthedocs. X is 3701. Filter genes based on number of cells Saved searches Use saved searches to filter your results more quickly Thank you for the super-kind words, @biskra. The only problem with this is that (usually) the expression values at this point in the analysis are in log scale, so we are calculating the fold-changes of the log1p count values, and then further log2 transforming GitHub community articles Repositories. Reload to refresh your session. X, flavor='cell_ranger', n_top_genes=n_top_genes, log=False) adata = adata[:, After running rank_genes_groups with 100 genes and 30 clusters, the adata. pp. filter_genes(adata, min_cells=100) >> list(np. e. X > 0. In the workflow below, I'm not able to inclu Dear, I used sc. (optional) I have confirmed this bug exists on the main branch of scanpy. index) and am having a hard time trying to unique the GeneIDs. uns[‘rank_genes_groups’], filtered genes are set to NaN. highest_expr_genes. Because I want to tranfer the output into an variable, I change these functions t check adata. Host and manage packages Sign up for a free GitHub account to open an issue and contact its maintainers and the community. The embeddings can be used as input of other downstream analyses. Could you please help me to check this issue? Thanks! Best, YJ. obsm['feat'] and the gene embeddings in adata. AI-powered developer platform Use saved searches to filter your results more quickly. normalize_per_cell( # normalize with total UMI count per cell adata, key_n_counts='n_counts_all') filter_result = sc. Cancel Sign up for a free GitHub account to open an issue and contact its maintainers and the community. rank_genes_groups has to be call first. var to be used as selection: not the actual n_top_genes highly variable genes. com Reply-To: theislab/scanpy reply@reply. filter_genes(adata, min_cells=3) the authors make inplace=True as default. shape produces (8648, 18074)) that I have subset to only include 990 genes of interest (and only include cells that express my genes of interest), with the hopes of clustering cells based on expression of my genes of interest (I got this idea from issue #510). rank_genes_groups. If you are new to these packages, please learn about them in advance. After running sc. When I do sc. Scanpy documentation: https://scanpy. rank_genes_groups(adata, groupby='groups_r0. Topics Trending Collections Enterprise Enterprise platform. I have done the following: disp_filter = sc. g. If a batch has 0 variance for multiple genes, then the _highly_variable_genes_single_batch() function will not work on this. toarray() != 0, 1 Env: Ubuntu 16. filter_genes() and sc. But when using the same coding to subeset a new raw adata, it generate errors. It works fine with method='t-test. Your Example Reveals that sc. In this tutorial, we use scanpy to preprocess the data. Hi Saved searches Use saved searches to filter your results more quickly I have confirmed this bug exists on the latest version of scanpy. On the other Hand, @LuckyMD uses the scran estimate of size factors for normalization. py. var, even when looking for the data itself in raw. Name. pbmc3k() sc. I am n Hi, I have some questions about the preprocessing steps: sc. scale() on a copy of the adata object like this: If there are very few genes some of the bins in sc. Here, we will filter the cells with low gene detection (low quality libraries) with less than 1000 genes for v2 and < Gene level filtering¶ Filter genes that occur in less than MIN_CELLS of cells. filter_cells(adata, min_genes=200) sc. The workaround I have found is to drop these cells from the adata object, and then continue with differential expression. I typically store my I believe it is because adata. filter_genes_dispersion( # select highly-variable genes adata. The order is the same is obs_names, but you can use pandas functions like sort_values to look at the top genes or do something like np. uns as dict. highly_variable_genes(adata) adata = adata[:, adata. varm. var. obs['leiden'] == '1'. {class}~anndata. pp. 5. After I subset my adata object, I confirmed that the shape of adata_sub is as (optional) I have confirmed this bug exists on the master branch of scanpy. Thank you! From: Fidel Ramirez Sent: Friday, March 22, 2019 5:55 AM To: theislab/scanpy Cc: screamer; Author Subject: Re: [theislab/scanpy] sc. . Then instantiating raw by adata. It's easy to fix with a prior sc. uns['rank_genes_groups_filtered']['pvals'][row][clu],adata. copy() Contribute to NBISweden/workshop-scRNAseq development by creating an account on GitHub. To see all available qualifiers, see our documentation. 0, :] does not work with always 2d X #333 Closed kleurless opened this issue Feb 27, 2020 · 3 comments · Fixed by #332 Returns ----- adata : :class: ` ~scanpy. argsort or scipy. log1p(adata) again before the function that returns the keyerror:base. rank_genes_groups(adata, 'celltype', method='wilcoxon', key_added = "wilcoxon", min_fold_change=3) Saved searches Use saved searches to filter your results more quickly Hi Scanpy team! After facing the issue with duplicated gene symbols again for the n-th time, I realised that one of the best solutions for renaming duplicates would likely be to do the following 'DuplicatedName-ENSEMBL_ID' rather than just adding an order-dependent number 'DuplicatedName-1' that can differ between dataset from different papers - preventing correct I find this behaviour surprising: >> sc. highly_variable(adata,inplace=True,subset=True,n_top_genes=100) I think scanpy stores PCs in adata. I have PCs stored there and never put anything in varm actively. filter_genes(adata, min_cells=1) If Saved searches Use saved searches to filter your results more quickly Hi, Thanks for the great software package. highly_variable_genes(adata) and got the following: ValueError: Bin edges must be unique: array([nan, in The output adata contains the cell embeddings in adata. uns["rank_genes_groups"]["names"] mapping to adata. raw, while having normalized and unnormalized expression of a subset of genes (might be only protein coding genes, or all genes except Hello all, For these 2 functions, sc. Here, to take care of bugs in scanpy, it is most helpful for us if you are able to share public data/a small part of it/a synthetic data example so that we can check whats going on. I was working on a data set with ~19k cells x ~22k genes and 12 leiden clusters. To see all available qualifiers, see Whereas Spider Additional function parameters / changed functionality / changed defaults? Would it be possible to add the gene_symbols= argument to scanpy. raw;). pl. Dear all, I am writing to ask you some other functionalities. var_names, respectively. To elaborate a bit on my comment on pull request #284 that sc. filter_genes(adata, min_counts=1) # only consider genes with more than 1 count sc. I have confirmed this bug exists on the latest version of scanpy. Is there a function to achieve this in scanpy. filter_genes(adata, min_cells=int(foo)) Things work as intended. Visually it appears to me that only the groups ['0', It is common to store raw counts (=unnormalized) of all measured genes under adata. X, min_counts, min_genes, max_counts, max_genes)) if not inplace: return gene_subset, number I see in the seurat notebook examples of violin plots grouped by genes. datasets. output = sc. filter_genes. rank_genes_groups in conjunction with sc. Sign in Use saved searches to filter your results more quickly. [Yes ] I have confirmed this bug exists on the latest version of scanpy. AnnData object adata stores a data matrix adata. By running in sc1. filter_genes# scanpy. py in rank_genes_groups_df(adata, group, key, pval_cutoff, log2fc_min, log2fc_max, gene_symbols) Saved searches Use saved searches to filter your results more quickly Why can't I use regress_out function for scRNA-seq data without applying highly_variable_genes. var_names `. Toggle navigation. 1 and 1. raw was used to store the full gene object when adata. uns['rank_genes_groups']['pvals_adj'] results in a 100x30 array of p-values. Cancel Create saved search # do test with I have checked that this issue has not already been reported. 0 (see below for the run times i was getting). Here is an example of how confusing this inconsistency can be: Hi, Actually that's not what I've experienced - if you compare with default rank_genes_groups test you get genes with positive and negative logFC, which means that the test reports both upregulated and downregulated genes in that comparison, but again, it's not symmetric - please try on a test dataset for yourself. stacked_violin: Use saved searches to filter your results more quickly. Saved searches Use saved searches to filter your results more quickly Hi, I have sliced some candidate genes (according to my pre-knowledge) from adata, and do sc. with version 1. Expanded documentation on how to to use sc. Is there still some easy way to do this? Apparently this type of cells can be bad. So I basically want to see expression of multiple signature genes in one plot. index is being stored as the adata. filter_genes (data, *, min_counts = None, min_cells = None, max_counts = None, max_cells = None, inplace = True, copy = False) [source] # Filter genes based on number of cells or counts. if isinstance (data, AnnData): adata = data. com Subject: Re: [theislab/scanpy] sc. stats. 4 (was working on 1. Change these values to match your data. var rather than adata. For example, dpi=100 sets the resolution of figures to 100 dots per inch, I am following workflow of 'Best-practices in single-cell RNA-seq: a tutorial' to analyze my single-cell sequencing data sets. This script consists of two functions. That's probably what sc. For these reasons, my naive view on this is to have a separate function that would give more flexibility to the users, as long as they know what they are doing. X) I got the following error: AttributeError: X not found I then ran sc. Processing something like that would need a counts_per_cell argument (which I'd call normalization_factor today, I guess). filter_genes(adata_test, min_cells=50) and getting the error below. rank_genes_groups_<plot> is erroneously looking for the adata. score_genes fails when scGDCF: Graphical Deep Clustering with Fused Common Information for single-cell RNA-seq Data - scGDCF/scanpy_filter. obs['gene_ids'] `. highly_variable_genes I get this error GitHub community articles Repositories. Function run_sc_analysis get's 10xGenomics files as a input and performs clustering to the dataset, finds marker genes and Hey - it would be most helpful to post user questions in the scverse forum - there, other users encountering the same question will be able to find a response easier :). X. X, which makes adata. Example: I am worried that it may not be reading our f This code makes the assumption that you have adata. Relates to the minimal expected cluster size. Use saved searches to filter your results more quickly. Or miro/ribo genes are filtered out sometimes, which might be needed later on e. Fix is on the way: I'll follow up here. https://nbiswede Hi, I noticed that Scanpy doesn't have a ready function for filtering cells with a high percentage of reads mapping to genes in the mitochondrial genome. For example, in the PBMC3K tutorial, calling this function again before step 43: Comparing to a single cluster. I'm wanting to use scanpy to create them based on groups of cells like this: That one I created using seaborn and generating the needed data structure in memory, but It happened to me that when I use the function sc. obs_names ` and gene names in ` adata. (optional) I have confirmed this bug exists on the master branch of scanpy. 0001, max_mean=3, min_disp=0. Contribute to NBISweden/workshop-scRNAseq development by creating an account on GitHub. Single cell RNA sequencing analysis course. obs and variables adata. raw = adata" to freeze the counts on adata. In the above code you will get the top 5 genes that are up-regulated in 'mut' compared to'ctrl'. 04 python 3. rank_genes_groups_df would be much appreciated. Pick a username Email Address Already on GitHub? Sign in to your account Jump to bottom. The reason is that sc. highly_variable(adata,inplace=True,subset=False,n_top_genes=100)--> Returns nothing ️--> adata. rank_genes_groups, and pl. 4. Also I think regress_out function should be before highly_variable_genes, because in this way we can first remove batch effect and then selec Quick question: I can plot differentially expressed genes for a group of celltypes in a dataset, like this: sc. Could you modify it to accelerate the process ? It seems the heatmap generated by pl. Thus, it would be good to have some sort of gene filtering before running the single batch versions. The data matrix is stored in ` adata. varm['feat']. raw = adata transfers that to adata. How Saved searches Use saved searches to filter your results more quickly Saved searches Use saved searches to filter your results more quickly At the most basic level, an {class}~anndata. log1p(adata) sc. 0 scanpy 1. I Saved searches Use saved searches to filter your results more quickly Hi all. The color palette is taken from the scatter plots. rank_genes_groups_heatmap(adata) to create a heatmap of top100 marker genes of 8,000 cells, 4 clusters, but it ran slowly, about 30 times slowers than seurat's Doheatmap(). 25. Sign in Product Actions. After using the function sc. var[<gene_symbols_key>] behind the scenes. @ivirshup Maybe this line should generally be removed from the tutorial, given that we now no longer need to filter genes anyway? Is there a Here's what I ran: import scanpy as sc adata = sc. I recall looking through quite a few datasets where there were really no mitochondrial genes. In scanpy there seems two functions can do this, one is filter_genes_dispersion and another one is highly_variable_genes, and there seems a little difference about those two, highly_variable_genes need take log first while filter_genes_dispersion take log after filtration, correct? Hi all, I have updated my scanpy to version 1. rank_genes_groups_heatmap is showing repeated genes, as seem in the picture below. var['genes_of_interest'] = adata. highly_variable_genes(ada I found it useful by calling scanpy. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. recarray to be indexed by group ids 'pvals', sorted np. So I'm giving it a try again: Say I have the PBMC 3K dataset, and after clustering and DEG in Scanpy, I have 120 genes specific for cluster 1 and 80 genes specific for cluster 3. 3. normalize_total and sc. api? Thanks! The text was updated successfully, but these errors were encountered: All reactions. X, annotation of observations adata. highly_variable_genes and so could be also an alternative to filter out genes before pearson_residuals. Query. What happened? Hello scanpy! First time, please let me know what to fix about my question asking! When running sc. filter_genes(adata, min_counts=1) call, but I think filter_genes_dispersion should retrieve n_top_genes, regardless of presence of zero Is there a way to filter for a set of genes, where if any one of the genes in a list are expressed, those cells will be plotted? I've tried switching Xparx's solution to a list, but receive the error "ValueError: Buffer has wrong Hi, I'm running sc. uns "gene_symbol" output for tl. Annoyingly you can't set adata. This occured in 1. Some people keep only protein coding genes in adata. Each column is a cluster, so the first row has the top-scoring genes for each cluster. normalize_total(adata, target_sum=1e4) sc. rank_genes_groups_matrixplot, just as done with sc. I was looking through the _rank_genes_groups function and noticed that the fold-change calculations are based on the means calculated by _get_mean_var. Now, we just have a boolean mask in adata. recarray to be indexed by group ids 'logfoldchanges', sorted np. 7 pandas 0. rank_genes_groups correctly looks for the adata. DataFrame and unstructured annotation adata. You can keep the genes in adata. In [2]: adata = Instead, we use Scanpy and Anndata to process and store the scRNA-seq data. pca(adata, use_highly_variable=True) does not reproduce the same umap embedding as subsetting the genes. X[:,gene_list]. Navigation Menu Toggle navigation. 3M dataset, but every time I ended up with zero genes after the sc. highly_variable_genes(adata, n_top_genes=1000, flavor="cell_ranger") can contain a single gene leading to NaN values in the normalized expression vector which are removed here It appears in the cases describe above, subset=True will cause the first n_top_genes many genes of adata. score_genes? Minimal code sample Saved searches Use saved searches to filter your results more quickly You signed in with another tab or window. filter_genes(adata, min_cells=3) I like @VolkerBergen's suggestion. (1) I agree it's useful! Why don't you subset adata to the genes that are interesting for you and run an embedding and clustering on them? That's definitely valid. highly_variable_genes(adata, m Saved searches Use saved searches to filter your results more quickly Dear all, I am very interested to set my own set of markers and see the expression of those markers in my umap. Saved searches Use saved searches to filter your results more quickly We have recreated the Seurat pipeline (2017 legacy version) from the Scanpy tutorial on it, and we have a step that lets users filter their AnnData object based on genes in cells or cells in genes. py at main · hebutdy/scGDCF Use saved searches to filter your results more quickly. com Date: Monday, January 7, 2019 at 11:16 AM To: theislab/scanpy scanpy@noreply. filter_genes(, inplace=False) are not the most intuitive. var['highly_variable']] Could you update to the latest releases (scanpy 1. Simply use standard slicing. gene_symbol there instead (per my supplied "gene_symbols" arg) Seems to work in scanpy Hi there, While running sc. Instead of returning a filtered anndata, it returns which cells would have been filtered scanpy. ndarray]]: """\ Wrap function scanpy. Saved searches Use saved searches to filter your results more quickly The tutorial was built quite a while ago to mirror the old Seurat tutorial in that direction. 1: I have confirmed this bug exists on the latest version of scanpy. highly_variable_genes(adata, min_mean=0. You should be able to turn this off via the use_raw` parameter. But if you look at the p-values, some of them are 1. uns['rank_genes_groups_filtered']['names'][row][clu],clu,adata. 7 before) and did not get the same filtering output using sc. filter_cells(), why are there still zero rows or columns in the datasets, so that print(np. ; sc. regress_out(adata, genes_of_interest) If you want to ensure an equal contribution of all the genes to the gene score without weighting by mean gene expression, you could first use sc. I'm currently achieving this Hello everyone, I have tried MAGIC recently using the following command: adata_magic=sc. highly_variable_genes(adata. Cancel Create saved search def find_genes(adata, gtf_file, key_added='gene_annotation', upstream=5000, Hi, I have asked this question before in Scanpy, but I wasn't sure I made it clear. Hi, Trying to run scVI to analyse my data using the latest scanpy+scvi-tools workflow, as described here. highly_variable] you should have all the genes still there. , sc. filter_cells(adata, min_genes=200) >> sc. sum(axis=0) == 0)) returns true? Saved searches Use saved searches to filter your results more quickly Contribute to 728267035/scAFC development by creating an account on GitHub. AnnData objects can However, I feel that the function (either rank_genes_groups_violin setting "_gene_names" or sc. I have calculated the size factor using the scran package and did not perform the batch correction step as I h Saved searches Use saved searches to filter your results more quickly Saved searches Use saved searches to filter your results more quickly Saved searches Use saved searches to filter your results more quickly Hi All, Not really an issue, but I'm very new to SCANPY (Seurat user before this) and was wondering what the general workflow/commands would be for merging and batch correcting multiple datasets together? Use saved searches to filter your results more quickly. to redo qc etc. highest_expr_genes() is using by default. Automate any workflow Packages. The filtered AnnData object is written to disk, and then the top 20 expressed genes are plotted with scanpy. raw before proceding. 4, anndata Saved searches Use saved searches to filter your results more quickly If I read a file with read_h5ad() and then process with sc. But the output rank gene names is wrong, many of the o When processing the data in Scanpy I am unable to figure out why my plot of the Highest Expressed Genes shows up with numbers rather than gene names as the identifiers on the Y-axis. io/en/stable/ Anndata I would agree the results of sc. This is indeed true if I set the method to t-test. Interestingly, this only happens if I use method='logreg. Cancel Create saved . log1p and plotting the Saved searches Use saved searches to filter your results more quickly Heya, I have been trying to get scanpy loaded and a simple example up and running. Cancel Create Hi, I know this issue has been previously opened but I am still unable to resolve this problem. 2', key_ Skip to content. Given the number of cells, I expect the smallest cluster of If starting from typical Cellranger output, it's possible to choose if you want to use Ensemble ID (gene_ids) or gene symbols (gene_symbols) as expression matrix row names. I'd like to take ENSG IDs all the way through the analysis (as var. var_names. float32'>' # with 11965294 stored elements in Compressed Sparse Row format> Saved searches Use saved searches to filter your results more quickly finished: added to `. filter_genes(adata, min_c Saved searches Use saved searches to filter your results more quickly df. post1 I have an AnnData object called adata. But if I change that read line to be read_h5ad(h5_path, backed='r') then when I attempt to filter I get this e Saved searches Use saved searches to filter your results more quickly I already have used mitochondrial genes to calculate "pct_counts_mito", but I don't want them to be in the data for downstream analysis. If you remove the line adata = adata[:, adata. X was filtered to only include HVGs or remove genes that aren't expressed in enough cells. pca(). But the function fails with the layer parameter. gov, Author author@noreply. filter_genes_dispersion(adata, n_top_genes=1000) call. " . varm = None either (except via a new adata object). uns[‘rank_genes_groups_filtered’] dataframe. recarray to be indexed by group ids 'pvals_adj', sorted np. 1, which I had to use because of #1941. heatmap cannot show different colors formore than 20 cell types () the problem is related to the palette being used. Initially adata. What happened? Hi, I have two different datasets, both with raw counts. I have just moved from Seurat to Scanpy and I am finding Scanpy a very nice and well done Python package. ywdc uzgoh zgzvr pglvxe oiz euqao zyqemu kdtcy ntbtxhuu uqprc