From SummarizedExperiment to interactive visualization
Heatmap graphs provide a visual representation of data using color variations to represent different values. They are incredibly useful for identifying patterns, trends, and outliers in datasets. Commonly used in various fields, from geographical analysis to website usability studies, heatmaps offer an intuitive way to understand complex data. Creating effective heatmaps involves selecting the appropriate color scheme and scaling to highlight meaningful differences. Interactive heatmaps further enhance analysis by allowing users to zoom, pan, and explore data points in detail. This makes them powerful tools for data-driven decision making across numerous disciplines.
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1.617,
0.582,
1.369,
1.389,
1.108,
2.455,
0.933,
2.378,
1.897,
1.202,
1.787,
1.121,
2.374,
1.595
],
[
1.291,
-0.506,
-0.465,
1.84,
0.108,
-0.312,
-0.757,
0.255,
0.642,
0.793,
0.124,
-0.586,
0.229,
-0.238,
-1.145,
1.63,
0.629,
0.739,
1.62,
1.078,
0.705,
0.161,
2.477,
0.08,
1.498,
1.175,
0.431,
1.042,
1.1,
1.51
],
[
0.163,
0.928,
0.473,
-0.687,
0.906,
0.162,
-1.168,
1.038,
-0.549,
-0.213,
0.214,
0.547,
0.158,
1.18,
-0.678,
0.912,
1.024,
2.469,
2.315,
0.323,
1.929,
1.088,
-0.628,
1.795,
-0.06,
-0.224,
1.705,
1.657,
0.962,
0.411
]
]
},
"x" : {
"Condition" : [
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Treated",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control",
"Control"
],
"Sex" : [
"M",
"M",
"F",
"F",
"M",
"F",
"M",
"M",
"M",
"M",
"F",
"M",
"F",
"M",
"F",
"F",
"M",
"F",
"F",
"M",
"M",
"F",
"M",
"M",
"M",
"F",
"M",
"F",
"M",
"F"
]
},
"z" : {
"GeneBiotype" : [
"protein_coding",
"protein_coding",
"miRNA",
"lncRNA",
"lncRNA",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"lncRNA",
"protein_coding",
"protein_coding",
"lncRNA",
"protein_coding",
"protein_coding",
"protein_coding",
"lncRNA",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"miRNA",
"lncRNA",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"lncRNA",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding",
"protein_coding"
]
}
}
// Create the configuration for the graph
var config = {
"graphType" : "Heatmap",
"title" : "From SummarizedExperiment to interactive visualization",
"subtitle" : "assay (40 genes x 30 samples) + rowData + colData",
"samplesClustered" : true,
"variablesClustered" : true,
"colorSpectrum" : [
"#2166ac",
"#f7f7f7",
"#b2182b"
],
"showSmpDendrogram" : true,
"showVarDendrogram" : true
}
// Event used to create graph (optional)
var events = false
// Call the CanvasXpress function to create the graph
var cX = new CanvasXpress("canvasId", data, config, events);
</script>
</body>
</html>
library(canvasXpress)
y=read.table("https://www.canvasxpress.org/data/r/cX-heatmap19-dat.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
x=read.table("https://www.canvasxpress.org/data/r/cX-heatmap19-smp.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
z=read.table("https://www.canvasxpress.org/data/r/cX-heatmap19-var.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
canvasXpress(
data=y,
smpAnnot=x,
varAnnot=z,
colorSpectrum=list("#2166ac", "#f7f7f7", "#b2182b"),
graphType="Heatmap",
samplesClustered=TRUE,
showSmpDendrogram=TRUE,
showVarDendrogram=TRUE,
subtitle="assay (40 genes x 30 samples) + rowData + colData",
title="From SummarizedExperiment to interactive visualization",
variablesClustered=TRUE
)
from canvasxpress.canvas import CanvasXpress
from canvasxpress.plot import show_in_browser
if __name__ == "__main__":
# Define the chart with its data and configuration
chart: CanvasXpress = CanvasXpress(
render_to = "heatmap19",
data = {
"y": {
"vars": ["ENSG00001", "ENSG00002", "ENSG00003", "ENSG00004", "ENSG00005", "ENSG00006", "ENSG00007", "ENSG00008", "ENSG00009", "ENSG00010", "ENSG00011", "ENSG00012", "ENSG00013", "ENSG00014", "ENSG00015", "ENSG00016", "ENSG00017", "ENSG00018", "ENSG00019", "ENSG00020", "ENSG00021", "ENSG00022", "ENSG00023", "ENSG00024", "ENSG00025", "ENSG00026", "ENSG00027", "ENSG00028", "ENSG00029", "ENSG00030", "ENSG00031", "ENSG00032", "ENSG00033", "ENSG00034", "ENSG00035", "ENSG00036", "ENSG00037", "ENSG00038", "ENSG00039", "ENSG00040"],
"smps": ["Sample01", "Sample02", "Sample03", "Sample04", "Sample05", "Sample06", "Sample07", "Sample08", "Sample09", "Sample10", "Sample11", "Sample12", "Sample13", "Sample14", "Sample15", "Sample16", "Sample17", "Sample18", "Sample19", "Sample20", "Sample21", "Sample22", "Sample23", "Sample24", "Sample25", "Sample26", "Sample27", "Sample28", "Sample29", "Sample30"],
"data": [
[2.115, -0.046, 1.184, 0.399, 1.785, 1.436, 2.342, 0.874, 2.466, 2.24, 1.016, 2.506, 2.233, 0.823, 1.744, 0.08, 0.265, -0.042, 0.764, -0.127, 0.265, -0.168, -0.392, -1.298, 0.276, -0.076, 0.161, 0.007, -0.478, -0.72],
[1.469, 0.407, 0.92, 1.534, 1.348, 1.497, 0.588, 1.12, 0.723, 2.196, 1.457, 1.428, 1.943, 2.346, 0.685, 0.221, -0.675, -0.604, -0.326, -0.519, 0.517, 0.132, -0.811, -0.456, -0.401, 0.763, 0.871, 1.594, -1.159, -0.181],
[2.882, 1.586, 1.786, 1.596, 1.339, 2.92, 0.71, 2.936, 1.424, 2.901, 1.046, 1.439, 0.588, 1.438, 1.883, 0.18, 0.179, -0.273, 0.454, 0.513, 1.829, -0.451, 0.755, 1.132, 1.155, 0.228, 0.111, 0.313, 0.583, -0.184],
[1.68, 2.215, 2.256, 1.65, 0.907, 1.99, 1.357, 0.954, 1.816, 1.145, 0.941, 1.414, 0.218, 1.453, 1.811, -0.199, -0.196, -0.175, 0.76, 1.608, -0.664, -0.977, 0.712, -0.909, -0.622, 0.394, -0.091, -0.616, 0.541, -1.057],
[2.509, 0.888, 1.634, 1.267, 2.42, 0.831, 0.934, 1.97, 2.557, 1.446, 1.674, 2.15, 1.466, 1.981, 0.412, -0.586, 0.46, 0.098, -0.652, -0.412, 0.786, -0.988, -0.529, 0.916, 0.462, -0.313, -0.163, 0.302, -1.095, 1.045],
[0.758, 1.341, 1.911, 1.203, 2.512, 1.403, 1.499, 2.681, 1.018, 1.141, 2.229, 0.803, 1.465, 1.928, 1.662, 0.55, -0.016, 0.364, -0.797, -0.992, 1.477, -0.098, 1.183, -0.873, -0.104, 0.38, 1.534, 1.056, -0.492, 1.005],
[2.041, 1.825, 2.867, 1.62, 1.458, 0.053, 1.939, 0.477, 2.171, 0.334, 1.093, 2.695, 1.367, 1.138, 2.06, -1.165, 0.629, 0.197, 0.536, -0.516, -0.284, -1.19, 1.316, -0.035, -0.975, -0.305, -1.059, -1.093, -0.269, -0.271],
[1.016, 2.546, 0.942, 1.88, 0.942, 1.563, 0.127, 1.027, 0.561, 2.361, 1.311, 0.857, 2.245, 2.672, 1.446, -0.325, 0.327, 0.257, -0.728, -0.675, 2.174, -0.416, 0.402, -0.181, -0.974, 0.23, -0.354, -0.396, 1.019, 0.075],
[1.58, -0.119, 1.725, 1.486, 0.987, 1.195, 1.859, 0.841, 1.45, 0.167, 0.968, 1.033, 1.425, 3.275, 3.767, -0.95, 1.023, 0.722, 0.981, 0.776, -0.014, 0.396, 0.732, -0.695, -0.017, 0.279, 1.292, 0.655, -0.667, 0.35],
[1.481, 1.353, 1.518, 1.254, 1.997, 1.925, 1.585, 1.892, 0.846, 1.898, 1.893, 1.364, 1.723, 0.991, 1.691, -0.543, 1.347, -0.362, 0.464, -0.07, 0.7, -0.369, 0.09, -0.236, 0.002, 0.269, 1.685, 1.119, -0.611, 0.192],
[2.331, 2.481, 2.098, 1.681, 1.685, 2.073, 1.285, 2.066, 2.052, 1.501, 0.553, 1.762, 1.203, 1.738, 1.336, 0.136, -0.161, 0.768, -0.593, 0.977, 0.205, -0.208, 0.826, 1.73, -0.654, -0.384, 0.156, 0.463, 0.256, 0.543],
[1.27, 2.438, 1.658, 3.211, 1.143, 1.547, 1.718, 1.308, 2.224, 0.707, 1.112, 2.984, 1.442, 1.484, 2.515, -0.429, -1.168, -0.269, -0.786, 1.211, 0.301, -0.308, 0.698, 0.677, -0.913, 0.303, -0.567, 0.698, 0.916, 0.677],
[1.719, 1.43, 2.554, 1.19, 3.11, 1.29, 1.443, 0.429, 2.239, 1.241, 1.409, 1.281, 1.945, 0.824, 0.712, -0.059, 0.087, -0.147, -0.378, 1.684, -0.483, -0.074, 0.24, 0.937, 0.241, -1.935, 1.045, -0.147, -0.111, -0.281],
[1.242, 2.182, 1.846, 1.761, 1.359, 1.948, 1.983, 1.374, 0.813, 2.699, 2.196, 0.772, 1.71, 1.976, 2.265, 0.631, -0.123, 0.333, 1.45, 0.151, 0.668, 0.835, 0.381, -0.342, -0.605, 0.307, 0.176, -0.027, -1.065, -0.77],
[1.54, 2.638, 1.082, 1.332, 0.902, 2.781, 2.207, 0.696, 1.325, 0.905, 1.549, 1.397, 2.677, 0.761, 1.09, -0.159, -0.195, 0.152, 0.02, 0.51, -1.203, -0.104, -0.046, 1.448, 0.223, -0.876, 0.062, -0.631, 0.241, -0.718],
[1.957, 1.347, 0.698, 1.229, 2.679, 0.478, 1.303, 1.919, 2.127, 1.016, 1.915, 2.242, 1.217, 2.149, 0.595, 0.688, 0.32, -0.735, -1.137, 0.961, -0.458, 1.067, 0.481, 0.271, 0.227, 0.396, 0.005, -0.775, 0.129, 0.2],
[1.411, 0.792, 0.823, 1.124, 1.426, 0.953, 2.397, 1.213, 1.744, 0.848, 1.714, 1.731, 1.616, 0.77, 1.968, -0.357, 0.486, -0.298, -0.158, 0.608, 0.024, 0.041, -0.694, 0.382, 0.292, 0.605, 0.47, 0.408, 0.556, 0.076],
[1.008, 1.156, 1.284, 1.144, 0.875, 1.915, 0.07, 2.085, 1.189, 1.828, 1.296, 1.241, 0.006, 0.735, 1.314, -0.18, 0.242, -0.192, 0.647, 1.008, -0.477, -1.053, -0.449, 0.437, 0.788, -0.148, 0.372, -0.2, -1.731, -0.833],
[1.223, 2.52, 0.728, 1.218, 1.407, 1.253, 1.48, 2.716, 0.51, 2.011, 1.704, 1.428, 1.045, 1.074, 2.572, 0.956, -0.36, -0.455, 0.054, 0.293, 0.349, -0.359, -0.938, -1.088, -0.41, 0.852, -0.479, 0.179, -0.365, -0.006],
[2.221, 1.949, 1.44, 1.694, 0.778, 0.428, 1.588, 0.846, 1.374, 1.172, 1.385, 2.631, 1.856, 2.876, 1.603, -0.043, -0.112, 1.574, 0.505, 1.097, 0.981, 0.308, -0.113, -0.589, -0.085, 0.78, 0.633, -0.369, -0.461, 0.172],
[0.442, 0.886, 1.625, -0.861, -0.803, -0.71, 0.34, 0.666, 0.186, 0.101, -0.464, -0.215, -0.319, 0.357, -0.665, 1.461, 1.294, 0.499, 0.993, 2.119, 2.066, 1.638, 1.632, -0.205, 0.719, 2.2, 1.201, 1.891, 2.21, -0.467],
[0.137, 0.64, -0.147, 0.844, -0.078, -0.015, 0.205, 0.633, -0.211, 0.355, 0.108, -0.079, -0.568, -0.244, -0.002, 0.091, 0.296, 1.143, 1.36, 1.752, 0.644, 0.885, 1.605, 1.5, 0.9, 2.437, 0.212, 1.082, 1.343, 0.683],
[0.487, 1.008, 0.413, 0.062, 0.487, -0.226, 0.323, 0.531, 0.724, -1.256, -0.503, 0.322, 0.484, 0.043, -0.364, 1.107, 0.537, 0.451, 1.551, 0.752, 0.511, 2.591, 0.997, 0.609, 1.221, 3.347, 1.139, 1.933, 2.449, 1.809],
[-0.461, -0.121, 0.837, 1.095, -1.743, 0.801, -0.142, 0.289, 0.691, -0.288, 0.374, 1.717, 1.302, -0.881, 0.431, 0.112, 0.33, 1.751, 0.537, 0.874, 1.36, 1.68, 0.353, 0.6, 1.161, 0.277, 1.739, 1.811, 2.09, 0.645],
[0.388, -0.972, -0.023, -0.612, 0.222, -0.21, -1.118, 0.756, -1.234, 1.029, 0.757, 0.671, 0.162, 0.475, -0.467, 0.608, -0.451, 1.537, 0.262, 0.796, 0.603, 1.768, 1.364, -0.22, 1.44, 1.665, 0.716, 0.352, 1.4, 0.427],
[-0.021, 0.282, 0.889, -1.488, -0.307, -1.101, -0.121, -0.83, -1.104, -0.471, 0.322, -0.667, -0.356, -0.269, 1.518, 1.938, -0.062, 0.899, 1.572, 1.891, 2.25, 2.001, 0.704, 0.94, 2.316, 0.571, 0.285, 1.205, 0.714, 0.856],
[-0.263, 0.379, 1.006, -0.458, -0.263, -0.351, -0.442, 0.391, -0.579, 1.168, -0.896, 0.622, 1.457, 0.57, -0.286, 0.606, 0.268, 0.92, 1.121, 0.693, 1.925, 0.855, 1.151, 0.868, 1.411, 1.262, 0.194, 0.219, 1.646, 1.552],
[-0.176, -1.29, 0.361, 0.499, 1.431, -0.139, -0.099, 0.879, -0.41, 0.558, -0.26, 0.309, 0.652, -0.56, 0.208, 1.62, 0.727, 1.626, 1.631, 0.892, 0.508, 1.661, 0.988, 1.324, 2.548, 0.937, 2.179, 1.898, 1.489, 0.432],
[-0.116, 0.11, 0.059, -0.116, -1.017, -1.764, -0.44, -1.334, -0.77, -0.344, 0.325, -0.25, -0.541, -0.078, -1.438, 1.248, 0.653, 1.161, 1.189, 0.912, 2.417, 0.825, 1.304, 1.664, 1.323, 2.866, 0.842, 1.034, 1.028, 1.792],
[-0.6, -0.317, -0.324, 0.446, 0.34, 0.187, 0.476, 0.535, -0.226, -0.003, 0.372, 1.269, -0.541, 0.119, -0.431, 1.229, 1.464, 1.108, 2.828, 1.543, -0.386, 0.975, 0.612, 2.938, 0.834, 1.46, 0.778, 1.122, 0.962, 0.933],
[-0.637, 0.873, 0.321, 0.108, 0.711, 0.464, -0.114, 0.238, 0.278, -0.795, -0.9, -0.089, -0.712, 0.643, 0.39, 0.634, 1.568, 0.865, 2.012, 0.709, 0.416, 2.555, -0.056, 1.273, 0.42, 1.508, 1.089, 1.838, 1.791, 0.161],
[-0.462, -0.904, -1.566, -0.487, -0.655, -1.003, 0.928, 0.889, -0.29, 0.407, 1.243, -0.047, -0.376, 0.174, -0.019, 1.912, 1.239, 0.482, 0.766, 1.029, 0.441, 0.901, -0.301, 1.489, 1.734, 1.566, 0.053, 0.593, 1.358, -0.775],
[0.013, 0.426, 0.457, -0.804, 0.745, 1.024, 0.162, 0.621, 0.759, 0.696, -0.864, 0.867, -0.25, -0.303, -0.763, 0.819, 0.577, 1.059, 1.757, 0.769, 0.313, 1.65, 2.75, 0.811, 0.202, 0.869, 1.644, 1.258, 0.654, 0.964],
[-0.149, 0.728, -0.747, 0.752, -0.432, 0.533, 0.393, -0.358, -0.017, -0.173, 0.072, 0.0, 0.459, -1.108, -0.46, 2.326, 0.559, 1.082, -0.116, 2.71, 1.919, 1.949, 1.738, 2.226, 1.809, 0.218, 0.486, 0.125, 0.526, 1.697],
[-0.281, -0.028, -0.049, 0.892, 0.34, 1.354, 0.772, -0.493, -0.7, -0.384, 0.166, -0.424, -0.875, 0.549, -0.126, 2.466, 0.977, 2.322, 0.77, -0.116, 0.759, 2.746, 1.917, 1.739, 1.298, 0.669, 2.399, 1.208, 1.317, 0.847],
[-0.072, 0.107, 0.434, 0.5, 0.082, 0.354, -1.016, 0.019, -0.52, -0.221, 1.158, -0.252, -0.529, 0.391, -0.78, 0.197, 1.482, 1.258, 0.654, 0.488, 0.373, 0.739, 2.988, 0.687, 0.537, 1.268, 2.053, -0.344, 0.513, 1.557],
[-0.423, 0.271, -0.365, -0.05, -0.695, -0.267, 0.075, -0.101, 1.212, 0.064, -0.598, -0.732, -0.989, 0.028, 0.271, 1.623, -0.309, -0.042, 1.781, 3.072, 1.873, 1.764, 0.829, 0.837, 1.456, 0.61, 0.17, 1.348, 0.359, 0.41],
[-1.71, 0.343, 0.315, 0.001, -0.679, -0.56, -0.247, 0.051, -0.335, -0.441, -0.322, -0.754, -0.808, 0.076, -0.483, 1.203, 1.617, 0.582, 1.369, 1.389, 1.108, 2.455, 0.933, 2.378, 1.897, 1.202, 1.787, 1.121, 2.374, 1.595],
[1.291, -0.506, -0.465, 1.84, 0.108, -0.312, -0.757, 0.255, 0.642, 0.793, 0.124, -0.586, 0.229, -0.238, -1.145, 1.63, 0.629, 0.739, 1.62, 1.078, 0.705, 0.161, 2.477, 0.08, 1.498, 1.175, 0.431, 1.042, 1.1, 1.51],
[0.163, 0.928, 0.473, -0.687, 0.906, 0.162, -1.168, 1.038, -0.549, -0.213, 0.214, 0.547, 0.158, 1.18, -0.678, 0.912, 1.024, 2.469, 2.315, 0.323, 1.929, 1.088, -0.628, 1.795, -0.06, -0.224, 1.705, 1.657, 0.962, 0.411]
]
},
"x": {
"Condition": ["Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Treated", "Control", "Control", "Control", "Control", "Control", "Control", "Control", "Control", "Control", "Control", "Control", "Control", "Control", "Control", "Control"],
"Sex": ["M", "M", "F", "F", "M", "F", "M", "M", "M", "M", "F", "M", "F", "M", "F", "F", "M", "F", "F", "M", "M", "F", "M", "M", "M", "F", "M", "F", "M", "F"]
},
"z": {
"GeneBiotype": ["protein_coding", "protein_coding", "miRNA", "lncRNA", "lncRNA", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "lncRNA", "protein_coding", "protein_coding", "lncRNA", "protein_coding", "protein_coding", "protein_coding", "lncRNA", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "miRNA", "lncRNA", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "lncRNA", "protein_coding", "protein_coding", "protein_coding", "protein_coding", "protein_coding"]
}
},
config = {
"graphType": "Heatmap",
"title": "From SummarizedExperiment to interactive visualization",
"subtitle": "assay (40 genes x 30 samples) + rowData + colData",
"samplesClustered": True,
"variablesClustered": True,
"colorSpectrum": ["#2166ac", "#f7f7f7", "#b2182b"],
"showSmpDendrogram": True,
"showVarDendrogram": True
},
width = 600,
height = 600
)
# Display the chart in its own Web page
show_in_browser(chart)
Run this notebook:
from canvasxpress.canvas import CanvasXpress
from canvasxpress.plot import graph
graph(
CanvasXpress(
render_to = "heatmap19",
data = {
"y": {
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},
"x": {
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"Sex": ["M", "M", "F", "F", "M", "F", "M", "M", "M", "M", "F", "M", "F", "M", "F", "F", "M", "F", "F", "M", "M", "F", "M", "M", "M", "F", "M", "F", "M", "F"]
},
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}
},
config = {
"graphType": "Heatmap",
"title": "From SummarizedExperiment to interactive visualization",
"subtitle": "assay (40 genes x 30 samples) + rowData + colData",
"samplesClustered": True,
"variablesClustered": True,
"colorSpectrum": ["#2166ac", "#f7f7f7", "#b2182b"],
"showSmpDendrogram": True,
"showVarDendrogram": True
},
width = 600,
height = 600
)
)
