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

tldr - The I*.fastq.gz file contains the read index sequences. long explanation Illumina uses a program called bcl2fastq to demultiplex sequencing runs. This software takes a list of samples and their associated indices and uses those sequences to make one or more fastq files per sample, binned by one or two index sequences on either end of the sequencing ...


5

I'm unsure whether this is the answer you are looking for, but when looking into 10X cellranger documentation for the Matrices Output: Unfiltered gene-barcode matrices: Contains every barcode from fixed list of known-good barcode sequences. This includes background and non-cellular barcodes. Filtered gene-barcode matrices: Contains only detected ...


5

Seruat will give you a list of genes which it thinks are upregulated in a particular cluster. Look at the functions that talk about marker genes - these functions basically do a DE analysis of the genes in one cluster compared to the others. Then take that list and feed it to any standard GO analysis tool. Have a look at the topGO topKEGG and geneSetTest ...


5

For merging dataframes, I find it easiest to use the tidyverse / dplyr functions inner/full/left/right_join. See the "Data Transformation Cheatsheet" on this page. For the merge that @user438383 has mentioned, this would be a left_join: library(tidyverse) anno <- data.frame(cluster = 1:6, celltype = c("T:CD4+NAIVE", "T:CD4+NAIVE&...


4

Data preparation Cell Ranger uses the Illumina sequencing output (.bcl) files Make fastq files: cellranger mkfastq ==> .fastq Prepare count matrix: cellranger count ==> matrix.mtx, web_summary.html, cloupe.cloupe Optional: combine multiple matrix.mtx files (libraries): cellranger aggr Data analysis Loupe Cell Browser visualization of cloupe.cloupe files ...


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

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

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

You should be able to parse out what you need using the tags in the .bam. 10xGenomics' website says what tags they add. https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/bam Going backwards would also involve parsing the tags, because you have to make two fastq files, and a simple bam -> fastq pipeline won't 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 ...


3

The raw data from cell ranger contains all of the barcodes detected in the experiment. These raw barcodes are filtered by cell ranger to identify the barcodes that likely represent cells rather than empty droplets / dead cells. You can find the filtered data in the filtered feature-barcode matrices. You can also examine the Run summary HTML file to see a ...


3

How to re-analyze 10X BAM files? This is a great question and honestly, I don't think there was an easy way to do this at the time the question was asked. The reason is that if you want to re-do the authors' analysis to get the gene expression (gene-cell / feature-barcode) matrix and they used cellranger (the official 10X pipeline software) to do that, you ...


3

I would suggest using a likelihood ratio test for differential expression using logistic regression with batch as a latent variable. In Seurat you can do: markers <- FindAllMarkers(object, test.use = 'LR', latent.vars = 'batch') (change "object" and "batch" accordingly) See https://www.biorxiv.org/content/early/2018/02/14/258566 from Lior Pachter's ...


3

A few suggestions: Manually inspecting the BAM file with a genome browser like IGV, as @GWW suggested and checking the qualities of the alignments in your BAM/SAM file (SAM is the human readable version of BAM, there are tools to convert one to another); Using another single cell analysis tool like Seurat so that you can manually set thresholds for QC and ...


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

Your initial guess is almost certainly correct. I don't know about the linked read libraries, but in the 10X single cell sequencing protocol, separating real barcodes from noise barcodes is an important and sensitive step in the analysis pipeline. The current analysis pipeline is to look at a plot, like the one you produced and find the "knees". The plot ...


3

The 3' gene expression protocol will capture TCR and BCR mRNAs but this may not be very helpful to you. As you already mentioned only the 3' end will be sequenced, which are the constant regions. With this data you may potentially be able to discriminate TRAC from TRBC1 and so forth. However, in most cases researchers are interested in clonotypes (...


2

Cell Ranger You can't download the tSNE coordinates for cells directly from the Analysis tab of the fancy, polished .html document that Cell Ranger produces. If you have access to the machine on which the pipeline was run, you can grab the intermediate files. Everything is well documented by 10X. They explain defaults etc. on that page. The tSNE coordinates ...


2

There is no purpose-built R package to perform gene set enrichment analysis on single-cell data but there does not need to be. You should be able to tools developed for bulk-RNA-Seq or microarray data, although you may not get as significant results from a sparse scRNA-Seq matrix as single-cell technologies have poor sensitivity and miss genes. What you need ...


2

It took me a while to figure out that the "index" is the same thing as the "barcode" that says which sample each sequence is from on a multiplexed run. If your data is not demultiplexed (single R1.fastq and R2.fastq files contain the information for multiple samples), then this I1.fastq file is what you use to assign each sequence to a sample (ie to "...


2

The bam file has bam tags which say what reads belong to what cells. https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/bam


2

So, we figured it out. If you delete everything until the [Data] tag in the sample sheet, leaving the [Data] tag, it starts working. Here is the full sample_sheet.csv that is working: [Data] Lane,Sample_ID,Sample_Name,index,Sample_Project 1,SI-GA-B4_1,17R,ACTTCATA,Chromium_20180409 1,SI-GA-B4_2,17R,GAGATGAC,Chromium_20180409 1,SI-GA-B4_3,17R,TGCCGTGG,...


2

The solution to your problem probably is adding the full mRNA sequence of your transgene to the reference (as also suggested by acrux). I had a very similar problem recently when tdTomato expression was only detectable in a couple of cells. The transgene was introduced into the cells using a lentiviral vector and therefore the mRNA had the tdTomato sequence ...


2

Seurat has a vignette specifically for combining multiple 10x libraries.


2

The function you need is CreateSeuratObject() and not Read10X() as you start from TPMs.


2

Here is a solution that makes use of LabelClusters() from Seurat: # creating a plot and assigning it to the "plot" variable # coloring by sample id and turning labels off plot <- DimPlot( pbmc, reduction = "umap", group.by = "some_random_sample_id", label = FALSE, ) # here is the trick, adding cluster labels to the &...


2

Checkout the first Seurat tutorial. To filter cells with >20% mitochondrial counts: obj[["percent.mito"]] <- PercentageFeatureSet(obj, pattern = "^MT-") obj <- subset(obj, subset = percent.mito < 20)


1

Two of those command line parameters should be accessible locations (see here). Could you please add the output of the following commands: ls -l /data/fi1d18/Downloads/FASTQ_scATAC/ | head and ls -l refdata-cellranger-arc-GRCh38-2020-A-2.0.0 | head I suspect that the refdata link should be an absolute path, rather than a relative path, i.e. something like ...


1

You should be able to use the --output-dir argument to specify a folder of your choice and use --id=demux_10x as before.


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