# phateR Bone Marrow Tutorial

## PHATE (Markov Affinity-Based Graph Imputation of Cells)

PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories. PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to biological systems in which smooth transitions mark the progressions of cells from one state to another. To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic stem cells, human embryonic stem cells, and bone marrow samples, check out our preprint on BioRxiv. Running this tutorial should take approximately 10 minutes from start to finish.

Kevin R. Moon, David van Dijk, Zheng Wang, et al. (2017), PHATE: A Dimensionality Reduction Method for Visualizing Trajectory Structures in High-Dimensional Biological Data, BioRxiv https://www.biorxiv.org/content/early/2017/03/24/120378.

### Installation

If you haven’t yet installed PHATE, you can find installation instructions in our GitHub README.

We’ll install a couple more tools for this tutorial.

    if (!require(viridis)) install.packages("viridis")
    if (!require(ggplot2)) install.packages("ggplot2")
    if (!require(readr)) install.packages("readr")
    if (!require(Rmagic)) install.packages("Rmagic")

If you have never used MAGIC, you should also install MAGIC from the command line as follows:

    pip install --user magic-impute

### Loading packages

We load the phateR package and a few others for convenience functions.

    library(phateR)

    ## Loading required package: Matrix

    library(ggplot2)

    ## Warning: package 'ggplot2' was built under R version 3.5.3

    library(readr)
    library(viridis)

    ## Loading required package: viridisLite

    library(Rmagic)

    ##
    ## Attaching package: 'Rmagic'

    ## The following object is masked from 'package:phateR':
    ##
    ##     library.size.normalize

### Loading data

In this tutorial, we will analyze myeloid and erythroid cells in mouse bone marrow, as described in Paul et al., 2015. You can run this tutorial with your own data by downloading https://raw.githubusercontent.com/KrishnaswamyLab/phateR/master/inst/examples/bonemarrow_tutorial.Rmd and opening it in RStudio. The example data is located in the PHATE GitHub repository and we can load it directly from the web.

    # load data
    bmmsc <- read_csv("https://github.com/KrishnaswamyLab/PHATE/raw/master/data/BMMC_myeloid.csv.gz")

    ## Warning: Missing column names filled in: 'X1' [1]

    ## Parsed with column specification:
    ## cols(
    ##   .default = col_integer(),
    ##   X1 = col_character()
    ## )

    ## See spec(...) for full column specifications.

    bmmsc <- bmmsc[,2:ncol(bmmsc)]
    bmmsc[1:5,1:10]

### Filtering data

First, we need to remove lowly expressed genes and cells with small library size.

    # keep genes expressed in at least 10 cells
    keep_cols <- colSums(bmmsc > 0) > 10
    bmmsc <- bmmsc[,keep_cols]
    # look at the distribution of library sizes
    ggplot() +
      geom_histogram(aes(x=rowSums(bmmsc)), bins=50) +
      geom_vline(xintercept = 1000, color='red')

    # keep cells with at least 1000 UMIs
    keep_rows <- rowSums(bmmsc) > 1000
    bmmsc <- bmmsc[keep_rows,]

### Normalizing data

We should library size normalize and transform the data prior to PHATE. Many people use a log transform, which requires adding a “pseudocount” to avoid log(0). We square root instead, which has a similar form but doesn’t suffer from instabilities at zero.

    bmmsc <- library.size.normalize(bmmsc)
    bmmsc <- sqrt(bmmsc)

### Running PCA

Let’s examine the raw data with PCA.

    bmmsc_PCA <- as.data.frame(prcomp(bmmsc)$x)

Now we’ll plot the results with `ggplot2`. We’ll color the plot by Mpo, a myeloid marker.

    ggplot(bmmsc_PCA) +
      geom_point(aes(PC1, PC2, color=bmmsc$Mpo)) +
      labs(color="Mpo") +
      scale_color_viridis(option="B")

    ggsave('BMMSC_data_R_PCA.png', width=5, height=5)

Because the data is noisy, PCA doesn’t tell us much - but we can see that the data is broadly separated along the first components by myeloid <-> erythroid cells.

### Running PHATE

Running PHATE is as simple as running the `phate` function.

We’ll just use the default parameters for now, but the following parameters can be tuned (read our documentation at https://cran.r-project.org/web/packages/phateR/phateR.pdf or by running `help(phateR::phate)` to learn more):

- `knn` : Number of nearest neighbors (default: 5). Increase this (e.g. to 20) if your PHATE embedding appears very disconnected. You should also consider increasing `knn` if your dataset is extremely large (e.g. >100k cells)
- `decay` : Alpha decay (default: 40). Decreasing a increases connectivity on the graph, increasing a decreases connectivity. This rarely needs to be tuned. Set it to `NULL` for a k-nearest neighbors kernel.
- `t` : Number of times to power the operator (default: ‘auto’). This is equivalent to the amount of smoothing done to the data. It is chosen automatically by default, but you can increase it if your embedding lacks structure, or decrease it if the structure looks too compact.
- `gamma` : Informational distance constant between -1 and 1 (default: 1). `gamma=1` gives the PHATE log potential, but other informational distances can be interesting. If most of the points seem concentrated in one section of the plot, you can try `gamma=0`.

    # run PHATE
    bmmsc_PHATE <- phate(bmmsc)

Now we plot the results using `ggplot2`.

    ggplot(bmmsc_PHATE) +
      geom_point(aes(PHATE1, PHATE2, color=bmmsc$Mpo)) +
      labs(color="Mpo") +
      scale_color_viridis(option="B")

### Rerunning PHATE with new parameters

The branches are a little collapsed, so we can decrease the connectivity by decreasing `k` from the default value of 5, and increasing `a` from the default value of 15. We could also change `t` from the automatic value printed in the PHATE output (here it is 10) - an increase in `t` reduces noise, a decrease in `t` can prevent the data from collapsing subtle structures. We’ll pass in the old PHATE object as the `init` argument, which can sometimes use precomputed intermediate data to prevent recomputing things that haven’t changed.

    bmmsc_PHATE <- phate(bmmsc, knn=4, decay=100, t=10, init=bmmsc_PHATE)
    ggplot(bmmsc_PHATE) +
      geom_point(aes(PHATE1, PHATE2, color=bmmsc$Mpo)) +
      labs(color="Mpo") +
      scale_color_viridis(option="B")

    ggsave('BMMSC_data_R_phate.png', width=5, height=5)

Much better! Now we can see more subtle structure in the erythroid branch, and the myeloid branch isn’t so collapsed.

### Visualizing imputed genes on PHATE with MAGIC

Many genes suffer from dropout to the point that coloring by a gene gives little to no information. Take for example Ifitm1, which is an stem cell marker.

    ggplot(bmmsc_PHATE) +
      geom_point(aes(PHATE1, PHATE2, color=bmmsc$Ifitm1)) +
      labs(color="Ifitm1") +
      scale_color_viridis(option="B")

    ggsave('BMMSC_data_R_phate_colored_by_Ifitm1_before_MAGIC.png', width=5, height=5)

Even though we expect the central population to be entirely stem cells, many of these cells express no Ifitm1. Let’s run MAGIC and try again. You can learn more about using MAGIC at https://github.com/KrishnaswamyLab/MAGIC.

    bmmsc_MAGIC <- magic(bmmsc, t=4, genes="Ifitm1")
    ggplot(bmmsc_PHATE) +
      geom_point(aes(x=PHATE1, y=PHATE2, color=bmmsc_MAGIC$result$Ifitm1)) +
      scale_color_viridis(option="B") +
      labs(color="Ifitm1")

    ggsave('BMMSC_data_R_phate_colored_by_Ifitm1_after_MAGIC.png', width=5, height=5)

That looks reasonable - the stem cell population expresses the most Ifitm1, as expected. That’s it, we’re done!

### Help

If you have any questions or require assistance using PHATE, please contact us at https://krishnaswamylab.org/get-help.