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HDStIM

Rohit Farmer

2022-06-23

The primary function HDStIM() in the HDStIM package follows a heuristic approach to group cells into responding and non-responding. For a combination of cell population and stimulation type (e.g., CD127+ T-helper cells and interferon-alpha), HDStIM() starts by performing K-means clustering on the combined set of cells from stimulated and unstimulated samples. K-means clustering is performed on combined expression data of all the state (signaling/intracellular) markers. Upon clustering using a contingency table as shown below, a Fisher’s exact test determines the effect size and the statistical significance of partitioning. Cells from the combinations that pass the Fisher’s exact test (p-value < 0.05) are considered responding. An optional UMAP can also be calculated to visually verify the cell partitioning in responding and non-responding groups by using auxiliary plotting scripts provided in the package.

In addition to an auxiliary script to plot UMAPs, the package also comes with two other plotting scripts for K-means clustering and Fisher’s exact test and state marker density before and after mapping.

To Run The Main HDStIM Function

As stated above, HDStIM() is the primary function of the HDStIM package. We will use the example data set chi11 (from mass cytometry) included in the package.

Note:chi11 is a minimal dataset included for unit testing only. Therefore, it does not represent a typical mass/flow cytometry assay.

library(HDStIM)

mapped_data <-  HDStIM(chi11$expr_data, chi11$state_markers,
                       chi11$cluster_col, chi11$stim_label,
                       chi11$unstim_label, seed_val = 123, 
                       umap = TRUE, umap_cells = 500, 
                       verbose = FALSE)

class(mapped_data)
#> [1] "list"

attributes(mapped_data)
#> $names
#>  [1] "response_mapping_main" "stacked_bar_plot_data" "state_markers"        
#>  [4] "cellpop_col"           "stim_label"            "unstim_label"         
#>  [7] "seed_val"              "all_fisher_p_val"      "all_k_means_data"     
#> [10] "umap_plot_data"        "umap"                  "umap_cells"

Output

HDStIM() returns a list with the mapped expression data, data to plot stacked bar plots to visualize the K-means and Fisher’s exact test results, and data to plot the optional UMAPs. The list also includes tables containing statistical information from K-means and Fisher’s exact test and other information passed as the function attributes.

head(mapped_data$response_mapping_main)
#> # A tibble: 6 × 41
#>   cluster_id sample_id      condition patient_id stim_type cell_population  CD45
#>        <dbl> <chr>          <chr>     <chr>      <chr>     <chr>           <dbl>
#> 1         79 CHI-011_1_2_G  CHI       CHI-011    G         CD11c CD14 CD38  3.48
#> 2         69 CHI-011_2_4_G  CHI       CHI-011    G         CD11c CD14 CD38  2.22
#> 3         69 CHI-011_4_13_G CHI       CHI-011    G         CD11c CD14 CD38  3.03
#> 4         79 CHI-011_4_11_G CHI       CHI-011    G         CD11c CD14 CD38  2.82
#> 5         69 CHI-011_2_5_G  CHI       CHI-011    G         CD11c CD14 CD38  2.07
#> 6         69 CHI-011_1_1_G  CHI       CHI-011    G         CD11c CD14 CD38  3.39
#> # … with 34 more variables: CD7 <dbl>, CD19 <dbl>, pPLCg2 <dbl>, CD4 <dbl>,
#> #   IgD <dbl>, CD20 <dbl>, CD25 <dbl>, pSTAT5 <dbl>, CD123 <dbl>, AKT <dbl>,
#> #   pSTAT1 <dbl>, CD27 <dbl>, pP38 <dbl>, CD24 <dbl>, pSTAT3 <dbl>,
#> #   CD11c <dbl>, CD14 <dbl>, CD56 <dbl>, IkBa <dbl>, pCREB <dbl>, CD16 <dbl>,
#> #   CD38 <dbl>, CD8 <dbl>, CD45RA <dbl>, CD3 <dbl>, pERK1_2 <dbl>,
#> #   HLA_DR <dbl>, pS6 <dbl>, CD127 <dbl>, ncount <int>, k_cluster_id <int>,
#> #   responding_cluster <int>, response_status <chr>, comb_no <int>

head(mapped_data$stacked_bar_plot_data)
#> # A tibble: 6 × 7
#>   cell_population            stim_type  f_p_val stim_clust stim_status k_cluster
#>   <chr>                      <chr>        <dbl>      <dbl> <chr>       <chr>    
#> 1 CD11c CD14 CD38            A         3.05e-26          1 unstim      cluster1 
#> 2 CD11c CD14 CD38            A         3.05e-26          1 unstim      cluster2 
#> 3 CD11c CD14 CD38            A         3.05e-26          1 stim        cluster1 
#> 4 CD11c CD14 CD38            A         3.05e-26          1 stim        cluster2 
#> 5 CD19 CD20 CD45RA HLA-DR C… A         5.56e-18          2 unstim      cluster1 
#> 6 CD19 CD20 CD45RA HLA-DR C… A         5.56e-18          2 unstim      cluster2 
#> # … with 1 more variable: cell_count_perc <dbl>

head(mapped_data$umap_plot_data)
#> # A tibble: 6 × 8
#>   cell_population stim_type condition tot_of_cells no_of_cells  UMAP1  UMAP2
#>   <chr>           <chr>     <chr>     <chr>        <chr>        <dbl>  <dbl>
#> 1 CD11c CD14 CD38 A         CHI       200          200          0.181  1.02 
#> 2 CD11c CD14 CD38 A         CHI       200          200          1.71   1.31 
#> 3 CD11c CD14 CD38 A         CHI       200          200         -1.69  -1.56 
#> 4 CD11c CD14 CD38 A         CHI       200          200          0.813  0.706
#> 5 CD11c CD14 CD38 A         CHI       200          200          1.23   0.791
#> 6 CD11c CD14 CD38 A         CHI       200          200          0.357 -1.27 
#> # … with 1 more variable: response_status <chr>

head(mapped_data$all_fisher_p_val)
#>   stim_type              cell_population stim_clust1 stim_clust2 unstim_clust1
#> 1         A              CD11c CD14 CD38          83          17            11
#> 2         A                 CD11c HLA-DR          43          57            50
#> 3         A CD19 CD20 CD45RA HLA-DR CD24          23          77            83
#> 4         A               CD3 CD27 CD127          35          65            32
#> 5         A          CD3 CD4 CD27 CD45RA          34          66            42
#> 6         A               CD3 CD4 HLA-DR          37          63            39
#>   unstim_clust2    estimate      p.value    conf.low  conf.high
#> 1            89 38.31592946 3.052288e-26 16.41025641 97.9574110
#> 2            50  0.75546384 3.950464e-01  0.41598165  1.3672058
#> 3            17  0.06232828 5.555794e-18  0.02850666  0.1296101
#> 4            68  1.14345766 7.645958e-01  0.60968684  2.1500051
#> 5            58  0.71262870 3.078444e-01  0.38474999  1.3129671
#> 6            61  0.91899062 8.842353e-01  0.49842428  1.6921035
#>                               method alternative      f_p_adj
#> 1 Fisher's Exact Test for Count Data   two.sided 2.441830e-25
#> 2 Fisher's Exact Test for Count Data   two.sided 7.023047e-01
#> 3 Fisher's Exact Test for Count Data   two.sided 2.539791e-17
#> 4 Fisher's Exact Test for Count Data   two.sided 9.212285e-01
#> 5 Fisher's Exact Test for Count Data   two.sided 6.156888e-01
#> 6 Fisher's Exact Test for Count Data   two.sided 9.781822e-01

head(mapped_data$all_k_means_data)
#>   stim_type              cell_population    pPLCg2    pSTAT5        AKT
#> 1         A              CD11c CD14 CD38 0.6036923 1.5150197 0.19882950
#> 2         A              CD11c CD14 CD38 0.3433208 0.5100987 0.14467489
#> 3         A                 CD11c HLA-DR 0.2754466 0.2628448 0.08187613
#> 4         A                 CD11c HLA-DR 0.9878153 0.5947371 0.10871637
#> 5         A CD19 CD20 CD45RA HLA-DR CD24 0.2804838 0.2853633 0.07871608
#> 6         A CD19 CD20 CD45RA HLA-DR CD24 0.3600085 1.9650785 0.12718000
#>       pSTAT1       pP38    pSTAT3      IkBa    pCREB    pERK1_2        pS6 size
#> 1 2.46662032 0.85286553 1.4007937 1.8315034 2.752456 0.10329694 0.27964780   94
#> 2 0.80050991 0.69575908 0.2100968 1.4439438 2.346255 0.04306572 0.16080807  106
#> 3 0.09136274 0.03271188 0.0532651 0.8501084 0.437598 0.03960397 0.07132055   93
#> 4 0.65155894 0.13219535 0.2322881 2.4118800 2.118350 0.02017302 0.12388073  107
#> 5 0.51540424 0.12034285 0.1396368 2.7656495 1.401279 0.13669583 0.10065605  106
#> 6 1.79297154 0.21265052 0.7480344 3.1434614 1.702514 0.20580153 0.18568148   94
#>   withinss cluster
#> 1 342.6724       1
#> 2 340.0997       2
#> 3 174.5916       1
#> 4 266.5471       2
#> 5 255.0538       1
#> 6 236.2004       2

To Plot Diagnostic Figures

Plots Explaining K-means Clustering And Fisher’s Exact Test

Using the stacked_bar_plot_data, plot_K_Fisher() generates bar plots showing the percentage of cells from the stimulated and unstimulated samples clustered in the two K-means clusters a given cell population and stimulation type.

plot_K_Fisher() returns a list of ggplot objects. If the path is specified, it can also render and save the plots in PNG format.

k_plots <- plot_K_Fisher(mapped_data, path = NULL, verbose = FALSE)
k_plots[[1]]

UMAP Plots To Visually Inspect Responding and Non-Responding Cell Mapping

Note: You can only generate these plots if you have asked UMAPs to be calculated in the HDStIM() function.

UMAP plots can be helpful for visually inspecting how well HDStIM() has mapped responding vs. non-responding cells for a cell population and stimulation type. plot_umap() also returns a list of ggplot objects and if the path is specified, it will render and save the plots in PNG format.

u_plots <- plot_umap(mapped_data, path = NULL, verbose = FALSE)
u_plots[[1]]

Distribution Plots for Individual State Marker before And After Mapping

For each state/signaling markers distribution plots shows the kernel density estimation of the pre HDStIM() data from both stimulated and unstimulated samples along with the density from cells from stimulated samples mapped as responding. plot_exprs() also returns a list of ggplot objects and if the path is specified, it will render and save the plots in PNG format.

e_plots <- plot_exprs(mapped_data, path = NULL,verbose = FALSE)
library(ggplot2)
e_plots[[1]] +
    theme(text = element_text(size = 11))
#> Picking joint bandwidth of 0.0611
#> Picking joint bandwidth of 0.274
#> Picking joint bandwidth of 0.21
#> Picking joint bandwidth of 0.0301
#> Picking joint bandwidth of 0.23
#> Picking joint bandwidth of 0.191
#> Picking joint bandwidth of 0.102
#> Picking joint bandwidth of 0.179
#> Picking joint bandwidth of 0.172
#> Picking joint bandwidth of 0.288

To Rank State/Signaling Markers According To Their Contribution To The Response

marker_ranking_boruta() function runs Boruta on the stimulation - cell population combinations that passed the Fisher’s exact test to rank the markers according to their contribution to the response. The function returns a list with a tibble containing attribute statistics calculated by Boruta and ggplot objects. If the path is not NULL, plots are also rendered and saved in the specified folder in PNG format.

m_ranks <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = NULL,
                                 max_runs = 100, seed_val = 123,
                                 verbose = FALSE)

head(m_ranks$attribute_stats)
#> # A tibble: 6 × 9
#>   stim_type cell_population state_marker meanImp medianImp   minImp maxImp
#>   <chr>     <chr>           <fct>          <dbl>     <dbl>    <dbl>  <dbl>
#> 1 A         CD11c CD14 CD38 AKT           -0.366   -0.519  -1.70      1.62
#> 2 A         CD11c CD14 CD38 pP38           0.259   -0.0535 -0.602     1.35
#> 3 A         CD11c CD14 CD38 pERK1_2        2.52     2.69    0.0838    4.29
#> 4 A         CD11c CD14 CD38 pS6            2.76     2.81   -0.00866   5.20
#> 5 A         CD11c CD14 CD38 pPLCg2         2.83     2.92    0.370     5.67
#> 6 A         CD11c CD14 CD38 pCREB          4.52     4.42    2.26      7.27
#> # … with 2 more variables: normHits <dbl>, decision <chr>

m_ranks$plots[[1]]

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They may not be fully stable and should be used with caution. We make no claims about them.