Tools for CyTOF analysis
This tool has now been published:
"Pre-treatment with systemic agents for advanced NSCLC elicits changes in the phenotype of autologous T cell therapy products"
Molecular Therapy - Oncolytics, 31, 100749 (2023)
https://doi.org/10.1016/j.omto.2023.100749
CyTOF data is log normal with zero inflation for most markers. For example:
BinaryClust takes advantage of this feature to subset cells for clustering by performing binary classifications on the markers. Using K-means clustering (K = 2), it is possible to automatically separate cells into positive and negative populations. Here, K-means breaks down the cells into CD3+ (red) and CD3- (blue) populations:
This process is done in BinaryClust on all the markers separately to generate a classification matrix. A user-defined selection criteria will then classify these cells into different populations, which will then be individually clustered:
BinaryClust also comes with a set of functions to perform standard analysis tasks such as data exploration and data visualisation by t-SNE or UMAP.
To intall BinaryClust, you will first need to install the R packages devtools and BiocManager:
install.packages('devtools')
install.packages('BiocManager')
Three Bioconductor packages are needed:
library(BiocManager)
install('ComplexHeatmap')
install('flowCore')
install('FlowSOM')
Then you can install BinaryClust and its dependencies:
library(devtools)
install_github("desmchoy/BinaryClust")
Don't forget to load the package after installation:
library(BinaryClust)
The original BinaryClust pipeline was designed for large-scale parallel runs on a high performance computing (HPC) facility. However, for reasonably sized samples, it can be used on local installations or via an IDE such as RStudio. though it is not recommended to run t-SNE or UMAP for large samples on a local installation due to computational demands.
BinaryClust is designed to be efficient and straight to the point. A quick analysis can be run within seconds using a single command given an FCS file and a user-defined cell classification file in CSV format (see the following section for details):
BC.results <- run_BinaryClust('HD.fcs', 'cell_types.csv')
run_BinaryClust is a shortcut function that performs five individual steps of analysis (each elaborated in the following section) using default settings in one go and returns their results in one single object. The result is a list of four data frames that provides the inputs for plotting functions or further analysis. The first data frame gives you arcsinh transformed (cofactor 5) data:
> head(BC.results[[1]])
CD45 CD196_CCR6 CD19 CD127_IL-7Ra CD38 CD33 IgD
1 4.763022 0.00000000 0.0000000 0.00000000 0.02726449 0.00000000 0.00000000
2 3.912244 2.05420158 0.0000000 2.45454068 0.01258484 0.00000000 0.03181502
3 5.385946 0.00000000 0.0000000 1.71571358 0.00000000 0.21548775 0.00000000
4 4.951837 0.44154719 0.0000000 0.06832929 0.26353484 0.00000000 0.00000000
5 5.501669 2.19138813 0.8805520 0.31980510 3.11632848 2.74392043 0.12969393
6 5.213904 0.09108109 0.1907823 2.25250901 1.42557094 0.06746715 0.20465348
CD11c CD16 CD194_CCR4 CD34 CD123_IL-3R TCRgd CD185_CXCR5
1 0.0000000 0.0000000 0.0000000 0.000000 0.00000 0.0000000 0
2 0.3241666 0.2583896 2.5192846 0.000000 0.00000 0.0000000 0
3 0.0000000 0.0000000 0.0000000 0.000000 0.11153 0.0000000 0
4 0.1237903 0.0000000 0.0000000 0.000000 0.00000 0.7061377 0
5 5.0112698 0.4296513 0.8198227 1.515479 0.00000 0.1518769 0
6 0.0000000 0.0000000 0.4235676 0.000000 0.00000 0.3448717 0
...
The second data frame gives you the median expressions of each marker and the abundance of each cell type definied in the cell classification file:
> head(BC.results[[2]])
Cell.Type CD45 CD196_CCR6 CD19 CD127_IL-7Ra CD38
1 B Cells 5.175686 2.7822063 4.55294083 0.24782457 2.6548137
2 Dendritic Cells 4.973808 0.1735834 0.05988902 0.00000000 2.8594343
3 Monocytes 5.068816 0.2541294 0.07413499 0.06794235 3.6651274
4 NK Cells 4.780354 0.0000000 0.05407581 0.00000000 3.9153949
5 T Cells, CD4 5.146704 0.2767183 0.00000000 2.36491443 0.5727581
6 T Cells, CD8 5.126842 0.0000000 0.00000000 0.37595694 0.2547606
CD33 IgD CD11c CD16 CD194_CCR4 CD34
1 0.1094124 4.3819244 0.08709911 0.02833477 0.0000000000 0.000000000
2 1.6333382 0.1881026 4.83674662 1.86184415 0.3341434080 0.212236144
3 2.4676174 0.2542666 4.75456686 1.06149001 0.4275743442 0.213740130
4 0.1222321 0.1060854 3.17299179 3.55946059 0.0002289466 0.007264909
5 0.0000000 0.0000000 0.00000000 0.00000000 0.5755964856 0.059087905
6 0.0000000 0.0000000 0.02973928 0.00000000 0.0000000000 0.000000000
...
CD4 CD14 CD56_NCAM CD11b Frequency Percentage
1 0.8065952 0.0000000 0.00000000 0.000000000 4105 6.400262
2 3.2867254 0.4042923 0.07917726 0.054064754 7765 12.106707
3 3.5688402 2.8055202 0.19211170 0.058509953 18883 29.441205
4 0.1929261 0.0000000 4.05164056 0.007594521 5561 8.670367
5 5.4276076 0.3077517 0.25694324 0.000000000 11667 18.190464
6 0.1380546 0.0000000 0.16847492 0.000000000 14128 22.027503
The third data frame lists out exhaustively the clustering results of each cell type:
> head(BC.results[[3]])
Cell.Type Cluster
1 T Cells, CD8 8
2 T Cells, CD4 32
3 T Cells, CD8 4
4 T Cells, CD8 9
5 Monocytes 30
6 T Cells, CD4 19
...
The fourth data frame summarises the clustering results. It gives the median expressions of each marker of each cluster of each cell type defined in the cell classification file and their abundances. It also leaves an empty Cell.Subtype column for filling for further analysis.
> head(BC.results[[4]])
Cell.Type Cluster CD45 CD196_CCR6 CD19 CD127_IL-7Ra CD38
1 B Cells 1 5.169841 2.830101 4.535509 0.33521943 3.3112159
2 B Cells 10 5.088842 2.498848 4.502411 0.17985776 2.3036351
3 B Cells 11 5.400011 2.562452 4.672512 0.19210797 0.9343564
4 B Cells 12 5.389607 5.242463 4.983812 1.87714720 3.1863538
5 B Cells 13 5.360868 2.147824 4.561302 0.11040360 0.6605718
6 B Cells 14 4.105287 2.512916 4.147211 0.04629935 0.9779664
CD33 IgD CD11c CD16 CD194_CCR4 CD34
1 0.289654941 5.41413151 0.09756387 0.1781481 0 0.003571104
2 0.000000000 0.00000000 0.04685361 0.0000000 0 0.000000000
3 0.006471317 3.81854993 0.00000000 0.0000000 0 0.000000000
4 0.238622565 5.00851295 0.07870780 0.1452360 0 0.020131121
5 0.000000000 0.07353001 0.03799469 0.0000000 0 0.000000000
6 0.000000000 2.67294214 0.00000000 0.0000000 0 0.000000000
...
CD14 CD56_NCAM CD11b Frequency Percentage Cell.Subtype
1 0.000000000 0.006773511 0.000000000 164 0.25569865
2 0.000000000 0.000000000 0.000000000 135 0.21048364
3 0.000000000 0.000000000 0.000000000 134 0.20892451
4 0.000000000 0.000000000 0.000000000 37 0.05768811
5 0.004020624 0.000000000 0.005794109 114 0.17774174
6 0.000000000 0.000000000 0.006619664 79 0.12317191
These data frames can be used to generate plots using the relevant functions of BinaryClust. For example, the binary classification summary (second data frame) can be plotted out:
plot_binary_abundances(BC.results[[2]])
plot_binary_median(BC.results[[2]])
Each of these analysis steps will now be explained in the following section.
To run standard BinaryClust analysis for a single CyTOF file, two files are needed:
- A CyTOF FCS file (formats prior to FCS 3.1 not tested)
- A cell classification file in CSV format
Please remove spaces and special characters from the file name as they will interfer with the code
The cell stratification file should be in CSV format. An example is shown below.
Cell Type,CD14,CD16,CD161,CD19,CD20,CD3,CD4,CD56_NCAM,CD8,TCRgd
NK Cells,-,A,A,-,A,-,A,+,A,A
Dendritic Cells,-,A,A,-,A,-,A,-,A,A
Monocytes,+,A,A,-,-,-,A,A,A,A
B Cells,-,-,-,+,A,-,A,A,A,A
"T Cells, Gamma Delta",-,A,A,-,A,+,A,A,A,+
"T Cells, CD4",-,A,A,-,A,+,+,A,-,-
"T Cells, CD8",-,A,A,-,A,+,-,A,+,-
+ means positively expressed, - means negatively expressed and A means "any". The marker name is determined by the desc column of the flowCore flowFrame object. Please use the print_parameters function to choose marker names (see Feature Selection section below). As exemplified above, if there is a comma , in the cell type name, please ensure to encircle it with quotation marks.
Assuming you have an FCS file called HD.fcs, before loading the data, you can examine the marker distributions with the function examine_data:
input.file <- '/your/data/directory/HD.fcs'
examine_data(input.file)
which will return a graph:
This will allow identification of markers that are sufficiently well-behaved for binary classification.
The print_parameters function will print the parameter page of the flowCore flowFrame object of the FCS file:
> print_parameters(input.file)
name desc range minRange maxRange
$P1 Time Time 1048576 0 1048575
$P2 Event_length Event_length 4096 0 4095
$P3 Y89Di 89Y_CD45 4096 0 4095
$P4 Rh103Di 103Rh 4096 0 4095
$P5 Sn120Di 120Sn 4096 0 4095
$P6 I127Di 127I 4096 0 4095
$P7 Xe131Di 131Xe 4096 0 4095
$P8 Cs133Di 133Cs 4096 0 4095
$P9 Ba138Di 138Ba 65536 0 65535
$P10 Ce140Di 140Ce_Bead 8192 0 8191
$P11 Pr141Di 141Pr_CD196_CCR6 8192 0 8191
$P12 Nd142Di 142Nd_CD19 4096 0 4095
$P13 Nd143Di 143Nd_CD127_IL-7Ra 4096 0 4095
$P14 Nd144Di 144Nd_CD38 32768 0 32767
$P15 Nd145Di 145Nd_CD33 4096 0 4095
...
The desc column is used to select useful channels for analysis. When you load data, channels with underscore _ will be chosen. Channels that contain 'Event_length', 'Bead', 'DNA', 'Live_Dead' and 'Viability' (case insensitive) are then removed. The remaining channels will have the heavy metal removed. Therefore, if the channel name is '89Y_CD45', the marker name will be 'CD45'. If the channel name is '141Pr_CD196_CCR6', the marker name will be 'CD196_CCR6'.
The load_data function will import your FCS file via flowCore and return a data frame. By default, it will perform an arcsinh transformation with a cofactor of 5:
data <- load_data(input.file)
This output is identical to the first data frame of the Quick Start function. There are various options under load_data such as switching off data transformation, using a different cofactor or subsetting the data. Please find the details in the help pages by running help(load_data).
Non-redundancy scores (NRS) of all imported markers can be then be plotted out for examination:
plot_NRS(data)
Cell type stratification can then be achieved by simply running the binary_class function:
class.file <- '/your/data/directory/cell_types.csv'
binary.results <- binary_class(data, class.file = class.file)
which exhaustively prints out the classification results:
> head(binary.results, 10)
Cell.Type
1 T Cells, CD8
2 T Cells, CD4
3 T Cells, CD8
4 T Cells, CD8
5 Monocytes
6 T Cells, CD4
7 Unclassified
8 Monocytes
9 T Cells, CD8
10 Monocytes
From experience, binary classification works better without scaling (standardising), but it is possible to scale the data with the scale = TRUE argument. This results can then be summarised using the binary_summary function:
binary.summary <- binary_summary(data, binary.results)
> binary.summary
Cell.Type CD45 CD196_CCR6 CD19 CD127_IL-7Ra CD38
1 B Cells 5.175686 2.78220628 4.55294083 0.24782457 2.6548137
2 Dendritic Cells 4.973808 0.17358345 0.05988902 0.00000000 2.8594343
3 Monocytes 5.068816 0.25412941 0.07413499 0.06794235 3.6651274
4 NK Cells 4.780354 0.00000000 0.05407581 0.00000000 3.9153949
5 T Cells, CD4 5.146704 0.27671830 0.00000000 2.36491443 0.5727581
6 T Cells, CD8 5.126842 0.00000000 0.00000000 0.37595694 0.2547606
7 T Cells, Gamma Delta 5.206422 0.06492577 0.00000000 1.29968831 0.2822164
8 Unclassified 5.140761 0.40987699 0.02674537 1.64831625 0.4436681
CD33 IgD CD11c CD16 CD194_CCR4 CD34
1 0.1094124 4.3819244 0.08709911 0.02833477 0.0000000000 0.000000000
2 1.6333382 0.1881026 4.83674662 1.86184415 0.3341434080 0.212236144
3 2.4676174 0.2542666 4.75456686 1.06149001 0.4275743442 0.213740130
4 0.1222321 0.1060854 3.17299179 3.55946059 0.0002289466 0.007264909
5 0.0000000 0.0000000 0.00000000 0.00000000 0.5755964856 0.059087905
6 0.0000000 0.0000000 0.02973928 0.00000000 0.0000000000 0.000000000
7 0.0000000 0.0000000 0.44546332 0.15615956 0.1030871205 0.067559716
8 0.0000000 0.0000000 0.08800407 0.03359136 0.1902003924 0.018908433
...
CD4 CD14 CD56_NCAM CD11b Frequency Percentage
1 0.8065952 0.00000000 0.00000000 0.000000000 4105 6.400262
2 3.2867254 0.40429234 0.07917726 0.054064754 7765 12.106707
3 3.5688402 2.80552018 0.19211170 0.058509953 18883 29.441205
4 0.1929261 0.00000000 4.05164056 0.007594521 5561 8.670367
5 5.4276076 0.30775171 0.25694324 0.000000000 11667 18.190464
6 0.1380546 0.00000000 0.16847492 0.000000000 14128 22.027503
7 0.1121716 0.00000000 0.55513300 0.000000000 1222 1.905267
8 0.7842022 0.06361226 0.18849300 0.023166919 807 1.258224
which is the second data frame of the Quick Start function. This summary can then be plotted out using two functions. plot_binary_abundances will give you a barchart
of the cell types:
plot_binary_abundances(binary.summary)
or, if you use the Quick Start results:
plot_binary_abundances(BC.results[[2]])
plot_binary_median will return a heatmap of median expressions of each marker for each cell type:
plot_binary_median(binary.summary)
or, if you use the Quick Start results:
plot_binary_median(BC.results[[2]])
BinaryClust can use the Rtsne and umap packages to generate t-SNE and UMAP plots.
To run t-SNE, run the run_TSNE function:
tsne.results <- run_TSNE(data)
This will return the full Rtsne object, which includes all the iteration details for the inquisitive minds. Raw t-SNE coordinates, if needed, can be extracted by:
tsne.results$Y
Random seed has been set to 1 to ensure reproducibility. Default values are perplexity = 30 and n_iter = 1000, which can be tuned to your needs. Please note that t-SNE requires a fair amount of computational resources so it might take a long time to run on a local installation.
There are two plotting functions associated with t-SNE. First, to colour the t-SNE plot by marker expressions, one can use the plot_TSNE_marker function. For example:
plot_TSNE_marker(tsne.results, data, marker = 'CD8')
Binary classification results can be projected onto the t-SNE plot with plot_TSNE. Remember to feed in the entire Rtsne object, not just the coordinates:
plot_TSNE(tsne.results, binary.results)
There are additional options in the functions which would allow you to customise the plots. Please refer to the help pages for details.
To run UMAP, run the run_UMAP function:
umap.results <- run_UMAP(data)
This will return the full umap object, which includes all the iteration details for the inquisitive minds. Raw UMAP coordinates, if needed, can be extracted by:
umap.results$layout
Random seed has been set to 1 to ensure reproducibility. Please note that UMAP requires a fair amount of computational resources so it might take a long time to run on a local installation.
There are two plotting functions associated with UMAP. First, to colour the UMAP plot by marker expressions, one can use the plot_UMAP_marker function. For example:
plot_UMAP_marker(umap.results, data, marker = 'CD8')
Binary classification results can be projected onto the UMAP plot with plot_UMAP. Remember to feed in the entire umap object, not just the coordinates:
plot_UMAP(umap.results, binary.results)
There are additional options in the functions which would allow you to customise the plots. Please refer to the help pages for details.
Post-binary classification clustering can be achieved by the cluster_subsets function:
cluster.results <- cluster_subsets(data, binary.results)
The default is to run K-means clustering with K = 40 (i.e. this time actually performing clustering rather than binary classification). It is possible to use FlowSOM instead by invoking method = 'FlowSOM' in the function:
cluster.results <- cluster_subsets(data, binary.results, method = 'FlowSOM')
This returns an exhaustive list of clustering results. The default is identical to the third data frame of the Quick Start results:
> head(cluster.results, 10)
Cell.Type Cluster
1 T Cells, CD8 14
2 T Cells, CD4 12
3 T Cells, CD8 6
4 T Cells, CD8 13
5 Monocytes 37
6 T Cells, CD4 27
7 Unclassified 22
8 Monocytes 13
9 T Cells, CD8 38
10 Monocytes 17
Similar to the binary classification results, one can summarise this clustering results with cluster_summary:
cluster.summary <- cluster_summary(data, cluster.results)
which gives you the fourth data frame of the Quick Start results:
> head(cluster.summary)
Cell.Type Cluster CD45 CD196_CCR6 CD19 CD127_IL-7Ra CD38
1 B Cells 1 5.183019 3.010737 4.540483 0.2702582 2.9931332
2 B Cells 10 5.228531 2.993737 4.525644 0.3465922 2.8844837
3 B Cells 11 5.289203 3.215675 4.578061 0.3309095 2.9068091
4 B Cells 12 5.225922 3.014848 4.640801 0.3533902 3.2260787
5 B Cells 13 5.638985 2.356074 5.106351 0.2998796 0.4539694
6 B Cells 14 5.142492 3.049752 4.491411 0.1922581 2.7137650
CD33 IgD CD11c CD16 CD194_CCR4 CD34 CD123_IL-3R
1 0.18340631 4.729382 0.047617543 0.04522073 0 0.00000000 0.88381936
2 0.23242867 5.289254 0.144420908 0.19974567 0 0.00000000 0.14282269
3 0.20523241 5.151498 0.065258649 0.10088530 0 0.00000000 0.16123497
4 0.33259819 5.236240 0.154991630 0.20030491 0 0.02758150 0.09937608
5 0.09449718 4.009340 0.572196788 0.00000000 0 0.02360902 0.00000000
6 0.14647881 4.473472 0.004331125 0.07859564 0 0.00000000 0.22138783
...
CD56_NCAM CD11b Frequency Percentage Cell.Subtype
1 0.00000000 0.000000000 158 0.2463438
2 0.00000000 0.000000000 235 0.3663975
3 0.00000000 0.000000000 182 0.2837631
4 0.05507358 0.586458700 76 0.1184945
5 0.04204587 0.004363943 76 0.1184945
6 0.00000000 0.000000000 176 0.2744083
The cluster summary is very important for further analysis, and can be exported as a CSV with base R function:
write.table(cluster.summary, file = '/your/data/directory/HD_results.csv', sep = ',', col.names = NA)
One can also plot out heatmaps for each cell type using the plot_cluster_heatmap function. The third argument in the function has to be identical to an entry in the input CSV file:
plot_cluster_heatmap(data, cluster.results, 'T Cells, CD8')
Suppose you have performed BinaryClust analysis on multiple samples individually and have exported their corresponding cluster summary files, there are functions that allow you to compare these results based on user-defined groupings.
To perform differential analysis, you will need to prepare a third input file in CSV format that basically specifies conditions and cluster summary files. Please only include two conditions. For example:
Condition,File
HD,/your/data/directory/HD1.csv
HD,/your/data/directory/HD2.csv
HD,/your/data/directory/HD3.csv
Pat,/your/data/directory/Pat1_EOT.csv
Pat,/your/data/directory/Pat1_SCR.csv
Pat,/your/data/directory/Pat2_EOT.csv
Pat,/your/data/directory/Pat2_SCR.csv
Pat,/your/data/directory/Pat3_EOT.csv
Pat,/your/data/directory/Pat3_SCR.csv
Pat,/your/data/directory/Pat4_EOT.csv
Pat,/your/data/directory/Pat4_SCR.csv
Then you can load all the summaries with load_multi_samples:
file.list <- '/your/data/directory/file_list.csv'
multi.data <- load_multi_samples(file.list)
It will concatenate all the cluster summary files:
> head(multi.data)
Cell.Type Cluster CD45 CD196_CCR6 CD19 CD127_IL.7Ra CD38
1 B Cells 1 4.629509 0.4825302 4.206931 0.1735501 2.2572409
2 B Cells 10 4.998703 2.1463927 4.311907 0.6078311 3.3948478
3 B Cells 11 4.908098 0.8790483 4.565507 0.2409447 0.1634295
4 B Cells 12 4.987891 2.6257633 4.189637 0.3728668 4.6644736
5 B Cells 13 5.210385 1.4615570 5.607920 0.5994589 0.4227020
6 B Cells 14 5.685553 1.5201998 5.027208 0.1988441 0.3636492
CD33 IgD CD11c CD16 CD194_CCR4 CD34 CD123_IL.3R
1 0.2304990 0.000000 0.3663483 0.00000000 0.0000000 0.098682680 0.00000000
2 0.5070841 5.083520 0.5062096 0.41352066 0.0000000 0.108504871 0.09992959
3 0.1449683 3.051967 0.2591035 0.01068233 0.0000000 0.031340424 0.00000000
4 0.7082969 5.151166 0.5186167 0.09386795 0.0000000 0.101895653 0.17988604
5 0.2454778 4.450915 3.2991139 0.27905773 0.0791512 0.275589937 0.09844255
6 0.1080207 2.908541 1.7384351 0.14263479 0.0000000 0.007528256 0.00000000
...
CD14 CD56_NCAM CD11b Frequency Percentage Cell.Subtype
1 0.22157777 0.000000000 0.00000000 13 0.08443752
2 0.18969796 0.002027638 0.08657339 11 0.07144713
3 0.06690649 0.066862267 0.03511942 19 0.12340868
4 0.15279500 0.007751477 0.15786430 16 0.10392310
5 0.00000000 0.176530001 0.06257187 14 0.09093271
6 0.21925181 0.129248998 0.12099711 9 0.05845674
Sample Condition
1 HD Sample 1 HD
2 HD Sample 1 HD
3 HD Sample 1 HD
4 HD Sample 1 HD
5 HD Sample 1 HD
6 HD Sample 1 HD
Please note that these synthetic data are for demonstration only and thus have no actual biological meaning.
Then, you can use calculate_diff to compare the samples based on the groupings:
diff.results <- calculate_diff(multi.data)
which returns a list of three data frames. The first data frame reports (percentage) abundances of each cell type:
> head(diff.results[[1]], 10)
Condition Sample Cell.Type PertSum
1 HD HD Sample 1 B Cells 3.215121
2 HD HD Sample 1 Dendritic Cells 8.002078
3 HD HD Sample 1 Monocytes 12.568200
4 HD HD Sample 1 NK Cells 9.223175
5 HD HD Sample 1 T Cells, CD4 39.575214
6 HD HD Sample 1 T Cells, CD8 25.000000
7 HD HD Sample 1 T Cells, Gamma Delta 1.149649
8 HD HD Sample 1 Unclassified 1.266563
9 HD HD Sample 2 B Cells 3.510240
10 HD HD Sample 2 Dendritic Cells 11.338125
The second data frame contains the log2 fold changes of each marker for each cell type:
> head(diff.results[[2]], 10)
Cell.Type CD45 CD196_CCR6 CD19 CD127_IL.7Ra
1 B Cells -0.22970404 -0.2599700 -0.2429808 -1.13501041
2 Dendritic Cells -0.06847058 -0.6739416 -0.5823648 -0.66393385
3 Monocytes 0.08666318 -0.3705416 -0.7522369 -0.46415420
4 NK Cells -0.04894102 -1.2975248 -0.6158446 1.26313028
5 T Cells, CD4 0.11035621 1.0927545 -1.1028954 -0.22679900
6 T Cells, CD8 0.17471587 0.6640972 -0.5817272 0.06040337
7 T Cells, Gamma Delta 0.07653682 -2.3797684 -1.4085702 -0.85953911
8 Unclassified 0.33697623 -1.6963910 -0.9838840 -1.15116910
CD38 CD33 IgD CD11c CD16 CD194_CCR4
1 -0.1570426 0.1499590 -1.24638655 1.11257045 0.5541302 1.63309254
2 -0.3919386 -1.4391767 -1.20931257 -0.41443920 -0.5613816 -0.07906084
3 -0.4929130 -0.9269763 -0.68601994 0.01803921 0.8617945 0.43979345
4 0.4292020 -1.2774064 -0.03734553 -0.27860215 -0.5752491 -0.21897400
5 1.0354683 1.3399111 0.82005888 5.59662093 2.2106574 -0.10970286
6 2.6549376 0.9681757 -0.85550323 2.22722522 3.3873764 -0.11391381
7 2.3914773 -0.9284685 -0.45406303 1.01115115 0.4694166 -0.51578438
8 0.5313064 -0.6293400 -2.35236147 1.16484943 -0.1089107 -0.50578358
...
CD56_NCAM CD11b
1 3.7976647 -0.9879263
2 0.5720582 -0.2398707
3 1.9164167 0.9715892
4 0.2781583 -1.0345319
5 0.3864566 -1.0220416
6 -0.1321637 -0.9650961
7 1.1536651 -0.9666266
8 0.6639926 0.6391839
The third data frame are the p-values for the fold changes:
> head(diff.results[[3]], 10)
Cell.Type CD45 CD196_CCR6 CD19 CD127_IL.7Ra CD38
1 B Cells 0.6626263 0.8404040 0.6012121 0.4460606 0.9212121
2 Dendritic Cells 0.6626263 0.7951515 0.6303030 0.4460606 0.2666667
3 Monocytes 0.6626263 0.8865801 0.5575758 0.5679654 0.4460606
4 NK Cells 0.7757576 0.7515152 0.5575758 0.4460606 0.7203463
5 T Cells, CD4 0.6626263 0.5333333 0.3393939 0.5679654 0.2262626
6 T Cells, CD8 0.6626263 0.7515152 0.6303030 0.9212121 0.0969697
7 T Cells, Gamma Delta 0.7757576 0.3878788 0.6303030 0.4460606 0.1939394
8 Unclassified 0.6626263 0.9212121 0.3393939 0.4460606 0.5010101
CD33 IgD CD11c CD16 CD194_CCR4 CD34 CD123_IL.3R
1 0.9212121 0.5575758 0.3878788 0.6626263 0.6626263 1.0000000 1.0000000
2 0.7434343 0.5575758 0.3878788 0.4460606 0.8865801 1.0000000 1.0000000
3 0.7434343 0.5575758 1.0000000 0.2262626 0.6626263 0.7515152 0.1939394
4 0.7434343 0.8865801 0.4294372 0.3878788 0.6626263 0.0969697 1.0000000
5 0.9212121 0.9212121 0.3878788 0.0969697 0.6626263 1.0000000 1.0000000
6 0.9212121 0.5575758 0.3878788 0.0969697 0.9212121 0.7515152 1.0000000
7 0.9212121 0.8404040 0.4294372 1.0000000 0.6626263 0.5333333 1.0000000
8 0.9212121 0.6012121 0.4294372 1.0000000 0.6626263 1.0000000 1.0000000
...
CD14 CD56_NCAM CD11b
1 1.0000000 0.7434343 0.06464646
2 0.3555556 0.7434343 0.17777778
3 1.0000000 0.9939394 0.27878788
4 0.7515152 1.0000000 0.04848485
5 0.0969697 1.0000000 0.04848485
6 0.2077740 1.0000000 0.17777778
7 1.0000000 1.0000000 0.17777778
8 1.0000000 0.7434343 0.27878788
Abundances can then be plotted using the plot_diff_abundances function:
plot_diff_abundances(multi.data)
Alternatively, you can change the style by invoking style = 'dot':
plot_diff_abundances(multi.data, style = 'dot')
Fold changes and differential abundances can be summarised using plot_diff_exp:
plot_diff_exp(diff.results)
Fold changes with significance (i.e. FDR < 0.05) are marked with 'S'.