Introduction
In this tutorial we provide a basic overview of Spectre's heatmap functions, to create expression heatmaps, and z-score/fold-change heatmaps.
| 1. Expression heatmap | 2. Z-score or fold-change heatmap |
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For showing the average expression of each selected marker on each population for a given dataset. | For showing relative change (increase or decrease) from the mean/median of each column for a given dataset. Each column shows a summarised feature of the data, including the expression of marker on labelled populations. |
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1. Expression heatmaps
library('Spectre')
package.load()
cell.dat <- Spectre::demo.clustered
cell.dat
FileName NK11 CD3 CD45 Ly6G CD11b B220 CD8a Ly6C CD4 NK11_asinh CD3_asinh CD45_asinh Ly6G_asinh ...
1: CNS_Mock_01.csv 42.3719 40.098700 6885.08 -344.7830 14787.30 -40.2399 83.7175 958.7000 711.0720 0.04235923 0.040087962 2.627736 -0.33829345 ...
2: CNS_Mock_01.csv 42.9586 119.014000 1780.29 -429.6650 5665.73 86.6673 34.7219 448.2590 307.2720 0.04294540 0.118734817 1.340828 -0.41743573 ...
3: CNS_Mock_01.csv 59.2366 206.238000 10248.30 -1603.8400 19894.30 427.8310 285.8800 1008.8300 707.0940 0.05920201 0.204803270 3.022631 -1.25101677 ...
4: CNS_Mock_01.csv 364.9480 -0.233878 3740.04 -815.9800 9509.43 182.4200 333.6050 440.0710 249.7840 0.35729716 -0.000233878 2.029655 -0.74509796 ...
5: CNS_Mock_01.csv 440.2470 40.035200 9191.38 40.5055 5745.82 -211.6940 149.2200 87.4815 867.5700 0.42713953 0.040024513 2.914359 0.04049443 ...
---
169000: CNS_WNV_D7_06.csv 910.8890 72.856100 31466.20 -316.5570 28467.80 -7.7972 -271.8040 12023.7000 1103.0500 0.81693878 0.072791800 4.142314 -0.31149515 ...
169001: CNS_WNV_D7_06.csv -10.2642 64.188700 45188.00 -540.5140 22734.00 202.4110 -936.4920 4188.3300 315.9400 -0.01026402 0.064144703 4.504101 -0.51715205 ...
169002: CNS_WNV_D7_06.csv -184.2910 -9.445650 11842.60 -97.9383 17237.00 123.4760 -219.9320 8923.4000 -453.4640 -0.18326344 -0.009445510 3.166628 -0.09778240 ...
169003: CNS_WNV_D7_06.csv 248.3860 229.986000 32288.20 -681.1630 19255.80 -656.0540 -201.5880 10365.7000 61.6765 0.24590035 0.228005328 4.168089 -0.63716643 ...
169004: CNS_WNV_D7_06.csv 738.9810 95.470300 46185.10 -1004.6000 22957.80 -661.6280 72.3356 9704.4700 -31.8532 0.68430866 0.095325863 4.525922 -0.88462254 ...
as.matrix(names(cell.dat))
[,1] [1,] "FileName" [2,] "NK11" [3,] "CD3" [4,] "CD45" [5,] "Ly6G" [6,] "CD11b" [7,] "B220" [8,] "CD8a" [9,] "Ly6C" [10,] "CD4" [11,] "NK11_asinh" [12,] "CD3_asinh" [13,] "CD45_asinh" [14,] "Ly6G_asinh" [15,] "CD11b_asinh" [16,] "B220_asinh" [17,] "CD8a_asinh" [18,] "Ly6C_asinh" [19,] "CD4_asinh" [20,] "Sample" [21,] "Group" [22,] "Batch" [23,] "FlowSOM_cluster" [24,] "FlowSOM_metacluster" [25,] "Population" [26,] "UMAP_X" [27,] "UMAP_Y"
cellular.cols <- names(cell.dat)[c(11:19)] cellular.cols
[,1] [1,] "NK11_asinh" [2,] "CD3_asinh" [3,] "CD45_asinh" [4,] "Ly6G_asinh" [5,] "CD11b_asinh" [6,] "B220_asinh" [7,] "CD8a_asinh" [8,] "Ly6C_asinh" [9,] "CD4_asinh"
exp <- do.aggregate(dat = cell.dat, use.cols = cellular.cols, by = 'Population') exp
Population NK11_asinh CD3_asinh CD45_asinh Ly6G_asinh CD11b_asinh B220_asinh CD8a_asinh Ly6C_asinh CD4_asinh 1: Microglia 0.06236472 0.08602177 2.166889 -0.3855973 3.057697 0.1821609 0.08896056 0.5460709 0.54132958 2: NK cells 1.46300084 -0.01751250 4.630241 -1.3037979 1.922951 1.0739984 -0.25100596 0.6064406 0.03004418 3: Neutrophils 0.03727157 0.07222479 4.308696 3.0539339 4.619174 0.3441983 0.03039162 2.6481672 0.61570367 4: CD4 T cells -0.33980759 2.15264283 5.143285 -1.7226959 1.408319 -0.3640679 -0.45432434 1.9315509 3.49054693 5: Infil Macrophages 0.22941383 0.14636386 4.518994 -1.1993391 3.922148 0.1186618 -0.22026899 3.5722527 0.66496177 6: CD8 T cells -0.21850316 1.53463965 4.909689 -1.7374355 1.396367 0.1280498 1.16367454 2.2156928 -0.17229134
getwd()
make.pheatmap(dat = exp, sample.col = 'Population', plot.cols = cellular.cols)
2. Z-score or fold-change heatmaps
library('Spectre')
package.load()
sum.dat <- Spectre::demo.sum
sum.dat
Sample Group Batch Cells per sample -- CD4 T cells Cells per sample -- CD8 T cells Cells per sample -- Infil Macrophages ... 1: 01_Mock_01 Mock A 734.6282 2696.9910 2143.504 ... 2: 02_Mock_02 Mock B 506.0006 1037.9500 2536.490 ... 3: 03_Mock_03 Mock B 540.5026 1003.7905 2470.869 ... 4: 04_Mock_04 Mock A 252.2882 404.8345 1965.501 ... 5: 05_Mock_05 Mock A 201.5021 549.5512 2198.205 ... 6: 06_Mock_06 Mock B 531.5886 1308.5259 4722.961 ... 7: 07_WNV_01 WNV A 54094.7075 50529.2479 605738.162 ... 8: 08_WNV_02 WNV B 20636.9741 23901.9282 674243.824 ... 9: 09_WNV_03 WNV A 26314.1446 34814.6759 678792.425 ... 10: 10_WNV_04 WNV A 26910.2796 32397.4082 652559.687 ... 11: 11_WNV_05 WNV B 31520.7836 36292.8545 644041.191 ... 12: 12_WNV_06 WNV A 34251.3184 37500.6658 645714.590 ...
as.matrix(names(sum.dat))
[,1] [1,] "Sample" [2,] "Group" [3,] "Batch" [4,] "Cells per sample -- CD4 T cells" [5,] "Cells per sample -- CD8 T cells" [6,] "Cells per sample -- Infil Macrophages" [7,] "Cells per sample -- Microglia" [8,] "Cells per sample -- Neutrophils" [9,] "Cells per sample -- NK cells" [10,] "Percent Ly6C_asinh positive -- CD4 T cells" [11,] "Percent Ly6C_asinh positive -- CD8 T cells" [12,] "Percent Ly6C_asinh positive -- Infil Macrophages" [13,] "Percent Ly6C_asinh positive -- Microglia" [14,] "Percent Ly6C_asinh positive -- Neutrophils" [15,] "Percent Ly6C_asinh positive -- NK cells"
to.plot <- names(sum.dat)[c(4:15)] as.matrix(to.plot)
[,1] [1,] "Cells per sample -- CD4 T cells" [2,] "Cells per sample -- CD8 T cells" [3,] "Cells per sample -- Infil Macrophages" [4,] "Cells per sample -- Microglia" [5,] "Cells per sample -- Neutrophils" [6,] "Cells per sample -- NK cells" [7,] "Percent Ly6C_asinh positive -- CD4 T cells" [8,] "Percent Ly6C_asinh positive -- CD8 T cells" [9,] "Percent Ly6C_asinh positive -- Infil Macrophages" [10,] "Percent Ly6C_asinh positive -- Microglia" [11,] "Percent Ly6C_asinh positive -- Neutrophils" [12,] "Percent Ly6C_asinh positive -- NK cells"
sum.dat.z <- do.zscore(dat = sum.dat, use.cols = to.plot, replace = TRUE) sum.dat.z
Sample Group Batch Cells per sample -- CD4 T cells Cells per sample -- CD8 T cells Cells per sample -- Infil Macrophages Cells per sample -- Microglia ... 1: 01_Mock_01 Mock A -0.8505409 -0.8308476 -0.9576835 -0.8963994 ... 2: 02_Mock_02 Mock B -0.8629743 -0.9178710 -0.9565229 -0.8768447 ... 3: 03_Mock_03 Mock B -0.8610980 -0.9196628 -0.9567167 -0.8832496 ... 4: 04_Mock_04 Mock A -0.8767718 -0.9510804 -0.9582092 -0.7237695 ... 5: 05_Mock_05 Mock A -0.8795337 -0.9434894 -0.9575220 -0.8121820 ... 6: 06_Mock_06 Mock B -0.8615827 -0.9036782 -0.9500658 -1.3124378 ... 7: 07_WNV_01 WNV A 2.0513215 1.6781460 0.8248683 0.9445190 ... 8: 08_WNV_02 WNV B 0.2318014 0.2814364 1.0271811 1.1174911 ... 9: 09_WNV_03 WNV A 0.5405409 0.8538537 1.0406141 0.8099797 ... 10: 10_WNV_04 WNV A 0.5729603 0.7270584 0.9631429 0.4584563 ... 11: 11_WNV_05 WNV B 0.8236918 0.9313901 0.9379859 0.6569472 ... 12: 12_WNV_06 WNV A 0.9721855 0.9947447 0.9429278 1.5174898 ...
getwd()
make.pheatmap(dat = sum.dat.z, sample.col = 'Sample', plot.cols = to.plot, is.fold = TRUE)
