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https://github.com/baderlab/malat1_threshold


https://github.com/baderlab/malat1_threshold

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# How to apply a _MALAT1_ threshold for your scRNA-seq object in R



For a detailed explanation of our findings or citation of this work, please see our preprint on BioRxiv: https://doi.org/10.1101/2024.07.14.603469. Please don't hesitate to ask any questions or let us know if you're getting an unexpected result. We have done our best to make this method robust, but data can be weird and noisy, so we're happy to offer our feedback and look into improvements!



Low _MALAT1_ expression is associated with a lack of a nucleus in single-cell RNA-sequencing data. Cells without nuclei are likely either empty droplets filled with ambient RNA, cell fragments, or mature erythrocytes. Our function `define_malat1_threshold` takes a vector of normalized _MALAT1_ expression, and outputs a minimum threshold value that can be used to filter your scRNA-seq object.



We hope to develop a package to allow a user to easily access this function. In the meantime, you can use this the function by either pasting the code directly into your R script, or cloning the GitHub repo, moving the `malat1_function.R` script into your analysis directory, and adding `source("malat1_function.R")` to the top of your script to access the function.



We generally recommend to use this function early in a QC pipeline, after reading in and normalizing your data. After filtering for minimum _MALAT1_ content, you can check for UMI and mitochondrial distribution to see if further filters are necessary, but you may find that this filter is sufficient. We speculate that cells with high _MALAT1_ but also high mitochondrial content may simply be metabolically active. Doublet filtering is unrelated to this pipeline and can be performed afterwards. Similarly, this function does not correct ambient RNA expression, so correction with e.g. SoupX may be performed after filtering for your final cell matrix, if desired.



This function can also be used to perform additional filtering on a processed dataset. You should get slightly different results (but a consistent broad pattern) if you run this function on the full dataset at once, or apply it to individual samples or cell types. There is no ground truth to figure out which of these approaches is the best, but we find that the function tends to work best on larger sample sizes (so it would struggle to find patterns in rare cell types or tiny samples). However, batch effect or slight cell-type specific _MALAT1_ expression differences may make it so that you wish to divide your sample in one of these ways before applying the filter. Due to the ubiqutous nature of _MALAT1_ and how sample quality can vary so much by batch, dividing by batch may yield more appropriate-looking results than by cell type. You may wish to test these different approaches on your data to see what works best for you.



To use this function, isolate the normalized _MALAT1_ expression values from your scRNA-seq object. In a Seurat object, this may look like:



```

norm_counts <- sobj@assays$RNA@data["MALAT1",]

```



This can be fed into the _MALAT1_ threshold function which will return the minimum _MALAT1_ value that each cell should contain:



```

threshold <- define_malat1_threshold(norm_counts)

```



This threshold value can be used to flag or filter cells from your single-cell object. The code below flags cells that don't pass the threshold by using `TRUE` values to represent good cells, and `FALSE` to represent cells that don't pass the filter:



```

malat1_threshold <- norm_counts > threshold

sobj$malat1_threshold <- malat1_threshold

sobj$malat1_threshold <- factor(sobj$malat1_threshold, levels = c("TRUE","FALSE"))

DimPlot(sobj, group.by = "malat1_threshold")

```



From this, you can use the result to remove cells from your object:



```

good_cells <- WhichCells(sobj, expression = malat1_threshold == TRUE)

good_sobj <- subset(sobj, cells = good_cells)

```



## Example analysis



We can demonstrate using this function with the Tabula Muris Senis Pancreas dataset which can be downloaded as a Seurat object from [cellxgene](https://cellxgene.cziscience.com/collections/0b9d8a04-bb9d-44da-aa27-705bb65b54eb). The data are also described in this paper:



The Tabula Muris Consortium. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020). https://doi.org/10.1038/s41586-020-2496-1



Here is a brief look at the cells in the dataset:



tabula_muris_senis_pancreas_celltypes



Here is _MALAT1_ projected onto the UMAP, and a histogram of the normalized _MALAT1_ values for that dataset. You can see that certain cells in the pancreatic acinar cell cluster have especially low _MALAT1_ values and may be suffering from some quality issues. Cells that may actually be empty droplets would be those in the lower _MALAT1_ expression peak in the histogram, in addition to those with a peak at zero:



tabula_muris_senis_pancreas_malat1_umap

tabula_muris_senis_pancreas_malat1_hist_noLine



This function fits a density function to the histogram, and models a quadratic to the highest _MALAT1_ expression peak above the normalized expression value of two. It finds this peak by analysing local minima and maxima that appear on the density function. The lower x-intercept of this quadratic is used to define the minimum _MALAT1_ threshold.



The function outputs the following plots: (1) The density plot with local minima annotated. (2) The density plot with local maxima annotated. (3) The points of the density function in black, with points highlighted in blue covering the range of the data that the quadratic is fit to, with the quadratic fit overtop of the points in red (below). (4) The histogram of normalized _MALAT1_ counts with the red line indicating the minimum threshold value (below).



tabula_muris_senis_pancreas_malat1_quad

tabula_muris_senis_pancreas_malat1_hist



Using the code above, we can see which cells passed the filter. Most of the cells that failed the filter are, in fact, pancreatic acinar cells (highlighted below as "FALSE" for having not passed the filter):



tabula_muris_senis_pancreas_malat1_dimplot



## Troubleshooting



This analysis relies on the assumption that there is a _MALAT1_ peak above the normalized value of one. If such a peak (i.e. local maximum above one) does not exist, the function may call an error. This is probably a good sign to take a closer look at your data anyway, but you can also lower this value by adjusting the parameter `chosen_min`.



Some histograms are wonky and can have a lot of little peaks, especially if you are working with integrated samples (which may just have different _MALAT1_ peaks due to batch effect) or samples with very few cells in them (which may have lots of little peaks due to data sparsity). To make the function more robust to these scenarios, there is a smoothing parameter, `smooth`, which is set to a high value of 1 as default, but can be lowered closer to zero for a tighter fit to the histogram. Further, if the function has trouble finding appropriate minima and maxima, `abs_min` and `rough_max` are set to 0.3 and 2 respectively to guide extreme minimum and likely maximum values to help the function work properly and not throw an error. Unless something really weird happens that I haven't predicted yet, you should always end up with a _MALAT1_ threshold of 0.3 or higher. If you are worried about throwing away too many cells with this lower boundary, you can always change `abs_min` to zero, but it may not help you keep anything good.



Other parameters that can be modified are `bw`, `lwd`, and `breaks`. Increasing or decreasing `bw` to say 0.5 or 0.01 respectively will change the plotting of the density function, with higher values creating a function with fewer inflection points (i.e. a "less curvy" function). Modifying `lwd` changes the thickness of the line on the final plotted histogram, and `breaks` is the number of buckets used in the histogram.



Worst case scenario, if the function doesn't work for some weird, confusing reason, you can always eyeball your _MALAT1_ values to try and figure out if there is something fishy going on with your data. You can manually choose your own threshold by looking at the histogram, or just pick out clusters of concerning cells by projecting _MALAT1_ onto your UMAP.