Filtering, Summerizing, and plotting from big data frame
Hi, I have a big data frame for omics data. Samples are named as Genotype_Time_Replicate (e.g. AOX_1h_4).
E.g. data set
structure(list(ID = c("AT5G54740.1", "AT5G55730.2", "AT5G57655.2", "AT5G64100.1", "AT5G64260.1", "AT5G67360.1", "AT1G30630.1", "AT1G62380.1", "AT1G70830.1", "AT3G14990.1", "AT4G18800.1", "AT4G24510.1", "AT5G15650.1", "AT5G19820.1", "AT5G59840.1", "AT5G47200.1", "AT1G12840.1", "AT1G76030.1", "AT1G78900.2", "AT3G42050.1", "AT4G11150.1", "AT1G11860.2", "AT1G17290.1" ), Location = c("extracellular", "extracellular", "extracellular", "extracellular", "extracellular", "extracellular", "golgi", "golgi", "golgi", "golgi", "golgi", "golgi", "golgi", "golgi", "golgi", "ER", "ER", "ER", "mitochondrion", "mitochondrion", "mitochondrion", "mitochondrion", "mitochondrion"), AOX_1h_1 = c(0.844651873, 0.50954096, 1.12e-08, 0.012981372, 0.978148381, 0.027579578, 0.068010151, 0.410629215, 0.253838635, 0.033631788, 0.335713512, 0.982799013, 0.025910457, 0.793810264, 0.762431665, 0.152154436, 0.027114103, 0.000227, 1.07e-05, 0.721209032, 0.086281162, 0.483130711, 0.014795515), AOX_1h_2 = c(0.894623378, 0.011521413, 1.62e-06, 0.085249729, 0.02863972, 0.956962154, 0.225208718, 0.932679767, 0.002574192, 0.071700671, 0.233682544, 0.936572874, 1.12e-05, 0.241658735, 0.865205515, 0.000537, 0.103471292, 8.66e-07, 1.22e-08, 0.950878446, 0.145012176, 0.092919172, 0.599713247), AOX_1h_3 = c(0.880951025, 0.00145276, 8.59e-10, 0.087023475, 0.675527672, 0.765543306, 0.305860948, 0.899172011, 0.020973476, 0.542988545, 0.735571562, 0.157569324, 0.025488075, 0.071006507, 0.262324019, 0.080470612, 0.0436526, 6.65e-09, 5.63e-10, 0.020557091, 0.069577215, 0.005502212, 0.852099232), AOX_1h_4 = c(0.980823252, 0.158123518, 0.00210702, 0.006317657, 0.30496173, 0.489709702, 0.091469807, 0.958443361, 0.015583593, 0.566165972, 0.66746161, 0.935102341, 0.087733288, 0.744313619, 0.021169383, 0.633250945, 0.257489406, 0.024345088, 0.000355, 0.226279179, 0.004038493, 0.479275204, 0.703522761), AOX_2h_1 = c(0.006474022, 0.246530998, 5.38e-06, 0.47169153, 0.305973663, 0.466202566, 0.191733645, 0.016121487, 0.234839116, 0.043866023, 0.089819656, 0.107934599, 2.09e-06, 0.413229678, 0.464078018, 0.004118766, 0.774970986, 3.79e-07, 2.3e-10, 0.428591262, 0.002326292, 0.385580707, 0.106216066), AOX_2h_2 = c(0.166169729, 0.005721199, 7.77e-08, 0.099146712, 0.457164663, 0.481987525, 7.4e-05, 0.969805081, 0.100894997, 0.062103337, 0.095718425, 0.001686206, 0.009710516, 0.134651787, 0.887036569, 0.459218152, 0.074576369, 3.88e-09, 3.31e-15, 0.409645805, 0.064874307, 0.346371524, 0.449444779), AOX_2h_3 = c(1.06e-05, 0.576589898, 4.03e-08, 0.787468189, 0.971119601, 0.432593753, 0.000274, 0.86932399, 0.08657663, 4.22e-06, 0.071190008, 0.697384316, 0.161623604, 0.422628778, 0.299545652, 0.767867006, 0.00295567, 0.078724176, 4.33e-09, 0.988576028, 0.080278831, 0.66505527, 0.014158693), AOX_2h_4 = c(0.010356719, 0.026506539, 9.48e-09, 0.91009296, 0.302464488, 0.894377768, 0.742233323, 0.75032613, 0.175841127, 0.000721, 0.356904918, 0.461234653, 1.08e-05, 0.65800831, 0.360085919, 0.004814238, 0.174670947, 0.004246734, 7.31e-11, 0.778725214, 0.051334623, 0.10212841, 0.155831664 ), AOX_6h_1 = c(0.271681878, 0.004822226, 1.87e-11, 0.616969208, 0.158860224, 0.684690326, 0.011798791, 0.564591916, 0.000314, 4.79e-06, 0.299871385, 0.001909713, 0.00682428, 0.039107415, 0.574143284, 0.061532691, 0.050483892, 2.28e-08, 1.92e-12, 0.058747794, 0.027147473, 0.196608218, 0.513693112), AOX_6h_2 = c(5.72e-12, 0.719814288, 0.140016259, 0.927094438, 0.841229414, 0.224510089, 0.026567282, 0.242981965, 0.459311076, 0.038295888, 0.127935565, 0.453746728, 0.005023732, 0.554532387, 0.280899096, 0.336458018, 0.002024021, 0.793915731, 0.012838565, 0.873716549, 0.10097853, 0.237426815, 0.003711539), AOX_6h_3 = c(3.16e-12, 0.780424491, 0.031315419, 0.363891436, 0.09562579, 0.104833988, 3.52e-05, 0.104196756, 0.870952423, 0.002036134, 0.016480622, 0.671475063, 2.3e-05, 0.00256744, 0.66263641, 0.005026601, 0.57280276, 0.058724117, 6.4e-10, 0.030965264, 0.005301006, 0.622027012, 0.371659724), AOX_6h_4 = c(7.99e-10, 0.290847169, 0.001319424, 0.347344795, 0.743846306, 0.470908425, 0.00033, 0.016149973, 0.080036584, 0.020899676, 0.00723071, 0.187288769, 0.042514886, 0.00150443, 0.059344154, 0.06554177, 0.112601764, 0.000379, 2.36e-10, 0.78131093, 0.105861995, 0.174370801, 0.05570041 ), WT_1h_1 = c(0.857, 0.809, 2.31e-05, 0.286, 0.87, 0.396, 0.539, 0.787, 0.73, 0.427, 0.764, 0.87, 0.386, 0.852, 0.848, 0.661, 0.393, 0.0415, 0.00611, 0.843, 0.576, 0.804, 0.304 ), WT_1h_2 = c(0.898, 0.509, 0.0192, 0.729, 0.616, 0.902, 0.811, 0.9, 0.343, 0.712, 0.814, 0.901, 0.0446, 0.816, 0.896, 0.217, 0.747, 0.0143, 0.000964, 0.901, 0.776, 0.737, 0.876 ), WT_1h_3 = c(0.939, 0.627, 0.0104, 0.867, 0.932, 0.935, 0.91, 0.939, 0.803, 0.926, 0.934, 0.888, 0.813, 0.859, 0.905, 0.864, 0.838, 0.0223, 0.00917, 0.802, 0.858, 0.724, 0.938 ), WT_1h_4 = c(0.911, 0.782, 0.298, 0.396, 0.837, 0.871, 0.727, 0.91, 0.506, 0.88, 0.89, 0.909, 0.723, 0.896, 0.547, 0.887, 0.824, 0.566, 0.175, 0.814, 0.348, 0.869, 0.893), WT_2h_1 = c(0.748, 0.911, 0.231, 0.929, 0.917, 0.928, 0.903, 0.801, 0.909, 0.849, 0.878, 0.884, 0.183, 0.925, 0.928, 0.719, 0.941, 0.108, 0.00817, 0.926, 0.678, 0.923, 0.884), WT_2h_2 = c(0.935, 0.851, 0.163, 0.925, 0.951, 0.952, 0.63, 0.963, 0.926, 0.916, 0.925, 0.804, 0.868, 0.931, 0.961, 0.951, 0.92, 0.0706, 0.000265, 0.95, 0.917, 0.947, 0.951), WT_2h_3 = c(0.0197, 0.894, 0.000613, 0.911, 0.922, 0.877, 0.122, 0.916, 0.739, 0.0125, 0.718, 0.905, 0.801, 0.875, 0.852, 0.91, 0.302, 0.729, 0.00015, 0.923, 0.731, 0.902, 0.504), WT_2h_4 = c(0.696, 0.765, 0.0142, 0.931, 0.893, 0.931, 0.925, 0.925, 0.87, 0.45, 0.899, 0.908, 0.144, 0.921, 0.899, 0.631, 0.87, 0.62, 0.0014, 0.926, 0.807, 0.844, 0.865), WT_6h_1 = c(0.898, 0.727, 0.00395, 0.921, 0.881, 0.924, 0.776, 0.919, 0.542, 0.234, 0.901, 0.67, 0.747, 0.83, 0.919, 0.848, 0.841, 0.056, 0.00144, 0.846, 0.815, 0.888, 0.916), WT_6h_2 = c(2.38e-09, 0.88, 0.708, 0.898, 0.891, 0.768, 0.443, 0.777, 0.843, 0.505, 0.695, 0.842, 0.208, 0.859, 0.794, 0.813, 0.14, 0.887, 0.326, 0.894, 0.661, 0.775, 0.182), WT_6h_3 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), WT_6h_4 = c(0.0357, 0.953, 0.792, 0.956, 0.967, 0.96, 0.711, 0.892, 0.931, 0.899, 0.866, 0.946, 0.917, 0.799, 0.925, 0.927, 0.938, 0.72, 0.025, 0.967, 0.936, 0.945, 0.923)), class = "data.frame", row.names = c(NA, -23L))
I want to summarize data for each organelle (averaged by organelle and samples' replicates) and plot the Wildtype and mutant data side by side with standard error for each time point.
https://ibb.co/f0pb17r (bar / dot plot)
And also, plot another the fold change of mutant compared to its' reference WT (e.g. AOX_1h_4/WT_1h_4)
How can I use Tidyverse / relevant R package for averaging the organelle and replicate data in this kind of DF for downstream analysis?
2 answers
See, if this help
library(ggplot2)
library(ggpubr)
library(reshape2)
# a is list created from your post
df <- do.call(cbind.data.frame, a)
melted <- melt(df)
head(melted)
melted$variable<- str_replace_all(melted$variable, '_[0-9]$', '')
melted$variable <- factor(melted$variable,levels=c("WT_1h","AOX_1h","WT_2h","AOX_2h","WT_6h","AOX_6h"))
my_comparisons <- list( c("WT_1h","AOX_1h"), c("WT_2h","AOX_2h"),c("WT_6h","AOX_6h"))
ggbarplot(melted, x = "variable", y = "value", add = "mean_se",
color = "variable", palette = c("grey","black","grey","black","grey","black"),
facet.by = "Location")+
stat_compare_means(comparisons = my_comparisons, label = "p.signif")
It seems to me that you're looking for the aggregate() function. There's also the summarize() function in the Hmisc package.
EDIT: For more ideas, check this blog post.
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Jean-Karim Heriche
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