11

tSNE often offers better visual representation (separation) on such complicated data than PCA. As Micheal pointed out, computing a tSNE embedding over 20.000 gene dimensions is computationally unfeasible, so a number of PCs are normally calculated and these are used as input for calculating the tSNE. They are used in tandem. As for global vs. local, we are ...


10

Expected rates of doublets / duplets / multiplets Fluidigm C1 doublet rate: around 1-5% depending on chip type used. More information: Fluidigm white paper: Redesign of C1 Medium-Cell 96 IFCs Improves Single-Cell Capture Efficiency (PN 101-3328 B1) 10x Genomics Chromium 3'mRNA-Seq doublet rate: around 1% per 1000 cells captured Rates in the 'Chromium ...


8

It may be necessary to distinguish between methods that use unique molecular identifiers (UMIs), such as 10X's Chromium, Drop-seq, etc, and non-UMI methods, such as SMRT-seq. At least for UMI-based methods, the alternative perspective, that there is no significant zero-inflation in scRNA-seq, is also advocated in the single-cell research community. The ...


7

"Doublet" is commonly used to describe a droplet in droplet-based sequencing that has captured atleast 2 cells. 10x states their doublet rate to be 0.8% per 1000 cells: There is a tradeoff between targeted cell capture and doublet rate and lab protocols are usually optimized for a certain expected doublet rate. One should expect to see close to doubling ...


7

Is doublet a set of cells sequenced as a single cell? Yes. Depending on the method of single cell sequencing it may be more or less likely for groups of cells to be captured and barcoded with the same "unique" barcode. This is more likely in split-pool RNA sequencing (e.g. SPLiT-seq), and less likely in cell-capture RNA sequencing (e.g. Fluidigm ...


7

The most obvious interpretation is that the four cell lines are related in a linear pathway rather than a branching one.


7

Single-cell analysis to compare samples is a long a difficult process. There is very good documentation for 10x Genomics cellranger, the DropSeq Pipeline and the Seurat R package. These tools all have GitHub repositories and the authors are very responsive if you encounter issues. Depending on the technology used to generate the data, you'll need to use ...


6

Assuming you have an informative selection of variable genes from which you have constructed a number of useful PCs, I'd run a number of iterations with FindClusters() as described in the other answer, then choose a level which overclusters the dataset (for example, clusters that are visibly separate on a t-SNE or other dimensionality reduction plot should ...


6

I was able to achieve this in the following way: require(data.table) cell_names <- vector(mode="character") for (i in 1:length(neuron_ids)) { seurat_subset <- SubsetData(seurat_object, subset.name = neuron_ids[i], accept.low = 0.1) genes <- colnames(seurat_subset@data) cell_names <- c(cell_names, genes[! genes %chin% cell_names]) } ...


6

The Biostars thread turned out helpful. The most interesting possible cause, not mentioned in the Ian Subery's answer, is that due to bursty nature of transcription, the true distribution of transcript counts across cells can be bimodal with a peak at zero even assuming a simple model of transcription such as the random telegraph model. See for example ...


5

I know of no references for this, but in general, I would say that your reasoning is sound. I would just add that in contrast to what I suspect you have simulated, not all transcripts are equally likely to be captured and amplified. We don't really understand what the determinants of this are, but for example, GC content is definitely related.


5

You can take a look at the recently published article: Bias, robustness and scalability in single-cell differential expression analysis. We evaluated 36 approaches using experimental and synthetic data and found considerable differences in the number and characteristics of the genes that are called differentially expressed. Prefiltering of lowly expressed ...


5

To color the TSNEPlot, you can generate a new column in metadata with the expression levels (High, low, etc). Then use pt.shape to set a shape for each identity. To show binary expression based on expression you first have to define the list of cells that are below or over your threshold. Once you have those lists you can use SetIdent() in Seurat to color ...


5

These tutorials on Seurat multimodal data and the wrapper Seurat data are easy ways to start. The wrapper has some cite-seq data preinstalled making it easy to work with benchmarked data sets If you are using Seurat u can just as well have a look at Signac to start working with some sc-atac seq


5

Ah, looks like I can't even procrastinate on StackExchange anymore without seeing work-related stuff. Oh well. Anyway, the other answers and comments are way off. scran has supported sparse matrices for years, ever since we switched over to the SingleCellExperiment class as our basic data structure. quickCluster does no coercion to dense format unless you ...


4

You want to (1) see the mean for each gene, and also to (2) calculate a ratio of expression levels of two genes, then compare it between clusters. (1) First, notice that vlnPlot() is deprecated. Use VlnPlot(). You can try using the parameter do.sort=T: VlnPlot(object=seuset, features.plot=c("DDB_G0267412", "DDB_G0277853"), do.sort=T) Alternatively, you ...


4

You can't build a network of a single cell only with the expression of a single cell. You either need previous known interactions or pathways or you need to use several cells/samples. If you use previous known information, you can use pathway information, otherwise you can group some cells and use something along the lines of WGCNA to find a scale-free ...


4

cellranger mkfastq is not necessary anymore. It used to be that the cellranger software wanted the reads to be interleaved, and you could use cellranger to do that for you if you couldn't do it yourself. Newer versions of cellranger will take the fastq files just like Illumina's bcl2fastq makes them. Cellranger count aligns the reads, filters away ...


4

This article uses the freely available R package dropbead for filtering and then Seurat to perform a principal component analysis that groups together affine transcriptomes. It could be what you are looking for.


4

We cannot assume that doublets will produce more UMIs I would caution the assumption that all doublets will have twice the UMI levels of isolated single-cells. Many "doublets" could contain multiplets of 3 or more cells, depending on how many cells have been loaded into the experiment. Most vendors of single-cell technologies quote multiplet rates for ...


4

First the Nextera adapters and the custom barcode adapters overlap each other Nextera 1 TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG Barcode 1 GATACGGCGACCACCGAGATCTACACTAGATCGCTCGTCGGCAGCGTCAGATGTGTAT Nextera 2 GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG Barcode 2 ...


4

In your case, I would definitely suggest following @Emily_Ensembl's advice and using the Arabidopsis GTF from Ensembl. But for future reference, if an Ensembl GTF wasn't available, you could build something like this using the gtf class from cgat The cgat module and dependancies can be installed by following the instructions here. Specifically, you can ...


4

The title is misleading as this error doesn't have anything to do with making the font italic. In Seurat v2, FeaturePlot does not return a ggplot2 object by default, so p in your case is NULL. You need to set do.return to TRUE in the FeaturePlot call. You should instead do: library(Seurat) p <- FeaturePlot(pbmc_small, head(pbmc_small@var.genes), do....


4

The BAM file you downloaded has all of the detected 10X barcodes instead of those that represent cells. In a given 10X experiment the number of input barcodes vastly outnumbers the input cell count. After the reaction some of barcoded beads are invariably sequenced because they are encapsulated with ambient RNA, dead cells etc. After processing the BAM ...


4

I wrote a python script based on pysam that is free for anyone to use: """ MIT License Copyright (c) 2020 Warren W. Kretzschmar Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights ...


3

I don't know if this question has been solved already, but what they try to do is equalize the depth of sequencing for each cell. Therefore, they scale for the total number of reads. If you regress out (via linear or negative binomial regression) the differences in the number of reads per cell, you end up with cells that have been sequenced with the same ...


3

Cell Ranger aggregate subsamples reads (unless you select none), so you will end up with less total reads in samples that have more initially. The output is still raw counts, but you will have more or less per cell. Seurat just merges the raw counts matrices and normalizes those.


3

A 90% loss can be rephrased as a 10% chance of detecting anything. So what we want to find is the probability of detecting 0 molecules, when we start with 7 and have 10% probability of success. Once can do that in R as follows: > pbinom(0, 7, 0.1) 0.4782969 So ~50%, as they stated. I suspect that part of the confusion arises from the fact that the ...


3

The best approach would be to contact 10x Genomics support: support@10xgenomics.com . They usually respond within a few hours. Otherwise, you can check the methods from the first 10x Genomics paper: The Cell Ranger Single-Cell Software Suite was used to perform sample demultiplexing, barcode processing and single-cell 3′ gene counting (http://...


3

That is a very general recommendation. Depending on your experiment, you can get a very different number of clusters with the same number of cells at the same resolution. You can actually use a vector of different resolutions and see which one performs best: pbmc_small <- FindClusters( object = pbmc_small, reduction.type = "pca", resolution = c(0....


Only top voted, non community-wiki answers of a minimum length are eligible