Search code examples
rggplot2heatmapmelt

Sectioning in Heat Map using ggplot


I'm trying to plot a heat map, but I want the heat map in four different vertical sections. The first one shall be for "Proneural", then "Neural", then "Classical" and lastly "Mesenchymal"

A sample of my melted data looks something like this:

dput(tdfdarkmagenta1[1:513,])

"TCGA.02.0011.01", "TCGA.02.0014.01", "TCGA.02.0024.01", "TCGA.02.0026.01", 
"TCGA.02.0028.01", "TCGA.02.0046.01", "TCGA.02.0047.01", "TCGA.02.0048.01", 
"TCGA.02.0060.01", "TCGA.02.0069.01", "TCGA.02.0074.01", "TCGA.02.0080.01", 
"TCGA.02.0084.01", "TCGA.02.0087.01", "TCGA.02.0104.01", "TCGA.02.0114.01", 
"TCGA.02.0281.01", "TCGA.02.0321.01", "TCGA.02.0325.01", "TCGA.02.0338.01", 
"TCGA.02.0339.01", "TCGA.02.0432.01", "TCGA.02.0439.01", "TCGA.02.0440.01", 
"TCGA.02.0446.01", "TCGA.06.0128.01", "TCGA.06.0129.01", "TCGA.06.0146.01", 
"TCGA.06.0156.01", "TCGA.06.0166.01", "TCGA.06.0174.01", "TCGA.06.0177.01", 
"TCGA.06.0238.01", "TCGA.06.0241.01", "TCGA.06.0410.01", "TCGA.06.0413.01", 
"TCGA.06.0414.01", "TCGA.06.0646.01", "TCGA.06.0648.01", "TCGA.08.0245.01", 
"TCGA.08.0344.01", "TCGA.08.0347.01", "TCGA.08.0348.01", "TCGA.08.0350.01", 
"TCGA.08.0353.01", "TCGA.08.0359.01", "TCGA.08.0385.01", "TCGA.08.0517.01", 
"TCGA.08.0524.01", "TCGA.12.0616.01", "TCGA.12.0618.01", "TCGA.02.0089.01", 
"TCGA.02.0113.01", "TCGA.02.0115.01", "TCGA.02.0451.01", "TCGA.06.0132.01", 
"TCGA.06.0133.01", "TCGA.06.0138.01", "TCGA.06.0160.01", "TCGA.06.0162.01", 
"TCGA.06.0167.01", "TCGA.06.0171.01", "TCGA.06.0173.01", "TCGA.06.0179.01", 
"TCGA.06.0182.01", "TCGA.06.0185.01", "TCGA.06.0195.01", "TCGA.06.0208.01", 
"TCGA.06.0214.01", "TCGA.06.0219.01", "TCGA.06.0221.01", "TCGA.06.0237.01", 
"TCGA.06.0240.01", "TCGA.08.0349.01", "TCGA.08.0380.01", "TCGA.08.0386.01", 
"TCGA.08.0520.01", "TCGA.02.0007.01", "TCGA.02.0009.01", "TCGA.02.0016.01", 
"TCGA.02.0021.01", "TCGA.02.0023.01", "TCGA.02.0027.01", "TCGA.02.0038.01", 
"TCGA.02.0043.01", "TCGA.02.0070.01", "TCGA.02.0102.01", "TCGA.02.0260.01", 
"TCGA.02.0269.01", "TCGA.02.0285.01", "TCGA.02.0289.01", "TCGA.02.0290.01", 
"TCGA.02.0317.01", "TCGA.02.0333.01", "TCGA.02.0422.01", "TCGA.02.0430.01", 
"TCGA.06.0125.01", "TCGA.06.0126.01", "TCGA.06.0137.01", "TCGA.06.0145.01", 
"TCGA.06.0148.01", "TCGA.06.0187.01", "TCGA.06.0211.01", "TCGA.06.0402.01", 
"TCGA.08.0246.01", "TCGA.08.0354.01", "TCGA.08.0355.01", "TCGA.08.0357.01", 
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"TCGA.08.0518.01", "TCGA.08.0529.01", "TCGA.08.0531.01", "TCGA.02.0004.01", 
"TCGA.02.0025.01", "TCGA.02.0033.01", "TCGA.02.0034.01", "TCGA.02.0039.01", 
"TCGA.02.0051.01", "TCGA.02.0054.01", "TCGA.02.0057.01", "TCGA.02.0059.01", 
"TCGA.02.0064.01", "TCGA.02.0075.01", "TCGA.02.0079.01", "TCGA.02.0085.01", 
"TCGA.02.0086.01", "TCGA.02.0099.01", "TCGA.02.0106.01", "TCGA.02.0107.01", 
"TCGA.02.0111.01", "TCGA.02.0326.01", "TCGA.02.0337.01", "TCGA.06.0122.01", 
"TCGA.06.0124.01", "TCGA.06.0130.01", "TCGA.06.0139.01", "TCGA.06.0143.01", 
"TCGA.06.0147.01", "TCGA.06.0149.01", "TCGA.06.0152.01", "TCGA.06.0154.01", 
"TCGA.06.0164.01", "TCGA.06.0175.01", "TCGA.06.0176.01", "TCGA.06.0184.01", 
"TCGA.06.0189.01", "TCGA.06.0190.01", "TCGA.06.0194.01", "TCGA.06.0197.01", 
"TCGA.06.0210.01", "TCGA.06.0397.01", "TCGA.06.0409.01", "TCGA.06.0412.01", 
"TCGA.06.0644.01", "TCGA.06.0645.01", "TCGA.08.0346.01", "TCGA.08.0352.01", 
"TCGA.08.0360.01", "TCGA.08.0390.01", "TCGA.08.0392.01", "TCGA.08.0509.01", 
"TCGA.08.0510.01", "TCGA.08.0512.01", "TCGA.08.0522.01", "TCGA.12.0619.01", 
"TCGA.12.0620.01", "TCGA.02.0003.01", "TCGA.02.0010.01", "TCGA.02.0011.01", 
"TCGA.02.0014.01", "TCGA.02.0024.01", "TCGA.02.0026.01", "TCGA.02.0028.01", 
"TCGA.02.0046.01", "TCGA.02.0047.01", "TCGA.02.0048.01", "TCGA.02.0060.01", 
"TCGA.02.0069.01", "TCGA.02.0074.01", "TCGA.02.0080.01", "TCGA.02.0084.01", 
"TCGA.02.0087.01", "TCGA.02.0104.01", "TCGA.02.0114.01", "TCGA.02.0281.01", 
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"TCGA.06.0128.01", "TCGA.06.0129.01", "TCGA.06.0146.01", "TCGA.06.0156.01", 
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"TCGA.08.0349.01", "TCGA.08.0380.01", "TCGA.08.0386.01", "TCGA.08.0520.01", 
"TCGA.02.0007.01", "TCGA.02.0009.01", "TCGA.02.0016.01", "TCGA.02.0021.01", 
"TCGA.02.0023.01", "TCGA.02.0027.01", "TCGA.02.0038.01", "TCGA.02.0043.01", 
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"TCGA.02.0285.01", "TCGA.02.0289.01", "TCGA.02.0290.01", "TCGA.02.0317.01", 
"TCGA.02.0333.01", "TCGA.02.0422.01", "TCGA.02.0430.01", "TCGA.06.0125.01", 
"TCGA.06.0126.01", "TCGA.06.0137.01", "TCGA.06.0145.01", "TCGA.06.0148.01", 
"TCGA.06.0187.01", "TCGA.06.0211.01", "TCGA.06.0402.01", "TCGA.08.0246.01", 
"TCGA.08.0354.01", "TCGA.08.0355.01", "TCGA.08.0357.01", "TCGA.08.0358.01", 
"TCGA.08.0375.01", "TCGA.08.0511.01", "TCGA.08.0514.01", "TCGA.08.0518.01", 
"TCGA.08.0529.01", "TCGA.08.0531.01", "TCGA.02.0004.01", "TCGA.02.0025.01", 
"TCGA.02.0033.01", "TCGA.02.0034.01", "TCGA.02.0039.01", "TCGA.02.0051.01", 
"TCGA.02.0054.01", "TCGA.02.0057.01", "TCGA.02.0059.01", "TCGA.02.0064.01", 
"TCGA.02.0075.01", "TCGA.02.0079.01", "TCGA.02.0085.01", "TCGA.02.0086.01", 
"TCGA.02.0099.01", "TCGA.02.0106.01", "TCGA.02.0107.01", "TCGA.02.0111.01", 
"TCGA.02.0326.01", "TCGA.02.0337.01", "TCGA.06.0122.01", "TCGA.06.0124.01", 
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"TCGA.06.0149.01", "TCGA.06.0152.01", "TCGA.06.0154.01", "TCGA.06.0164.01", 
"TCGA.06.0175.01", "TCGA.06.0176.01", "TCGA.06.0184.01", "TCGA.06.0189.01", 
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"TCGA.06.0397.01", "TCGA.06.0409.01", "TCGA.06.0412.01", "TCGA.06.0644.01", 
"TCGA.06.0645.01", "TCGA.08.0346.01", "TCGA.08.0352.01", "TCGA.08.0360.01", 
"TCGA.08.0390.01", "TCGA.08.0392.01", "TCGA.08.0509.01", "TCGA.08.0510.01", 
"TCGA.08.0512.01", "TCGA.08.0522.01", "TCGA.12.0619.01", "TCGA.12.0620.01", 
"TCGA.02.0003.01", "TCGA.02.0010.01", "TCGA.02.0011.01", "TCGA.02.0014.01", 
"TCGA.02.0024.01", "TCGA.02.0026.01", "TCGA.02.0028.01", "TCGA.02.0046.01", 
"TCGA.02.0047.01", "TCGA.02.0048.01", "TCGA.02.0060.01", "TCGA.02.0069.01", 
"TCGA.02.0074.01", "TCGA.02.0080.01", "TCGA.02.0084.01", "TCGA.02.0087.01", 
"TCGA.02.0104.01", "TCGA.02.0114.01", "TCGA.02.0281.01", "TCGA.02.0321.01", 
"TCGA.02.0325.01", "TCGA.02.0338.01", "TCGA.02.0339.01", "TCGA.02.0432.01", 
"TCGA.02.0439.01", "TCGA.02.0440.01", "TCGA.02.0446.01", "TCGA.06.0128.01", 
"TCGA.06.0129.01", "TCGA.06.0146.01", "TCGA.06.0156.01", "TCGA.06.0166.01", 
"TCGA.06.0174.01", "TCGA.06.0177.01", "TCGA.06.0238.01", "TCGA.06.0241.01", 
"TCGA.06.0410.01", "TCGA.06.0413.01", "TCGA.06.0414.01", "TCGA.06.0646.01", 
"TCGA.06.0648.01", "TCGA.08.0245.01", "TCGA.08.0344.01", "TCGA.08.0347.01", 
"TCGA.08.0348.01", "TCGA.08.0350.01", "TCGA.08.0353.01", "TCGA.08.0359.01", 
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"TCGA.12.0618.01", "TCGA.02.0089.01", "TCGA.02.0113.01", "TCGA.02.0115.01", 
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"TCGA.06.0211.01", "TCGA.06.0402.01", "TCGA.08.0246.01", "TCGA.08.0354.01", 
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"TCGA.08.0511.01", "TCGA.08.0514.01", "TCGA.08.0518.01", "TCGA.08.0529.01", 
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"TCGA.02.0034.01", "TCGA.02.0039.01", "TCGA.02.0051.01", "TCGA.02.0054.01", 
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"TCGA.02.0079.01", "TCGA.02.0085.01", "TCGA.02.0086.01", "TCGA.02.0099.01", 
"TCGA.02.0106.01", "TCGA.02.0107.01", "TCGA.02.0111.01", "TCGA.02.0326.01", 
"TCGA.02.0337.01", "TCGA.06.0122.01", "TCGA.06.0124.01", "TCGA.06.0130.01", 
"TCGA.06.0139.01", "TCGA.06.0143.01", "TCGA.06.0147.01", "TCGA.06.0149.01", 
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"TCGA.06.0194.01", "TCGA.06.0197.01", "TCGA.06.0210.01", "TCGA.06.0397.01", 
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"TCGA.08.0346.01", "TCGA.08.0352.01", "TCGA.08.0360.01", "TCGA.08.0390.01", 
"TCGA.08.0392.01", "TCGA.08.0509.01", "TCGA.08.0510.01", "TCGA.08.0512.01", 
"TCGA.08.0522.01", "TCGA.12.0619.01", "TCGA.12.0620.01"), X1 = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L
), .Label = c("Proneural", "Neural", "Classical", "Mesenchymal"
), class = "factor"), variable = c("APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
"APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", "APOL1", 
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6.297859, 5.036076, 5.580978, 5.374182, 6.098891, 5.228106, 4.514317, 
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4.655344)), row.names = c(NA, 513L), class = "data.frame")

Overall the melted data has 4 columns and 11,286 rows. The first column, i.e. the "Row.names" shall contribute to the y-axis of the heat plot, and the third column, i.e. "variable" to contribute to the x-axis but they should be sectioned into four sections depending on the entries in the second column, i.e. "X1".

I'm attaching an image showing the two-section heat plot along the x-axis. enter image description here

Here the tiny white space separation shows the start of a new section on the x-axis.

My attempt can be found here:

tdfdarkmagentaplot<-ggplot(data=tdfdarkmagenta1, mapping=aes(variable, Row.names , fill= value)) +
    geom_tile() + theme(axis.title.x=element_blank(),
                        axis.text.x=element_blank(),
                        axis.ticks.x=element_blank(),
                        axis.title.y=element_blank(),
                        axis.text.y=element_blank(),
                        axis.ticks.y=element_blank())+
    scale_fill_distiller(palette = "RdBu")
tdfdarkmagentaplot

I'm also being told complexheatmap() function in R is another approach but don't know how to implement it in my dataframe.

All the suggestions are welcomed.


Solution

  • Add facet wrap and indicate four columns.

    ggplot(df[1:500,], aes(x = variable, y = Row.names, fill = value)) +
        geom_tile() + 
        theme(axis.title.x=element_blank(),
            axis.text.x=element_blank(),
            axis.ticks.x=element_blank(),
            axis.title.y=element_blank(),
            axis.text.y=element_blank(),
            axis.ticks.y=element_blank()) +
        scale_fill_distiller(palette = "RdBu") +
        facet_wrap(~X1, ncol = 4)
    

    I attempted to use your data. There were some NA values. The scales don't seem to match what you used. Anyhow, this shows what I believe is your desired column layout.

    solution