Prepare input data for WGCNA (format, QC), then choose the appropriate soft thresholding power for network construction, by analysing scale free topology with different soft thresholding powers.
Usage
prepare_WGCNA(
se_obj,
assay = 2,
networkType = "signed",
RsquaredCut = 0.8,
MeanConnectivity = 100,
powers = NULL,
fontsize = 8,
...
)Arguments
- se_obj
A SummarizedExperiment object. Data normalised to time point 0 can be in the second assay slot, created by
normalise_to_start(). Features can be pre-filtered, e.g. by residual variance calculated bydecomp_variance(), to remove noisy features before running WGCNA.- assay
Which assay slot of the SummarizedExperiment object to use for WGCNA input (default is 2, which is where the time 0 normalised data is stored by
normalise_to_start())- networkType
(Optional) Parameter of
WGCNA::pickSoftThreshold()(default is "signed")- RsquaredCut
(Optional) Parameter of
WGCNA::pickSoftThreshold()(default is 0.8)- MeanConnectivity
(Optional) Line of mean connectivity (default is 100)
- powers
(Optional) Parameter of
WGCNA::pickSoftThreshold()(default: NULL, auto-assigned asseq(1, 20))- fontsize
Base font size for diagnostic plots (default: 8).
- ...
Additional parameters to be passed to
WGCNA::pickSoftThreshold()
Value
A list containing results of the scale-free topology
fit indices with different powers, suggested power, network type and prepared
input data used for reuse in run_WGCNA()
Examples
data(example_obj)
example_obj <- normalise_to_start(example_obj)
#> Normalising to group baseline at each feature's first non-NA time point.
wgcna_input <- prepare_WGCNA(example_obj, assay = 2, powers = seq(1, 30),
networkType = "signed", RsquaredCut = 0.8)
#> Allowing multi-threading with up to 24 threads.
#> pickSoftThreshold: will use block size 100.
#> pickSoftThreshold: calculating connectivity for given powers...
#> ..working on genes 1 through 100 of 100
#> Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
#> 1 1 0.8430 3.800 0.9140 52.5000 53.3000 61.000
#> 2 2 0.0646 -0.315 0.1410 30.5000 30.8000 42.500
#> 3 3 0.6650 -1.280 0.5850 19.0000 18.6000 31.600
#> 4 4 0.7580 -1.150 0.8160 12.5000 11.8000 24.500
#> 5 5 0.6820 -1.050 0.7270 8.6000 7.7300 19.500
#> 6 6 0.7190 -1.130 0.7330 6.1400 5.2300 15.900
#> 7 7 0.7580 -1.100 0.8040 4.5100 3.6800 13.100
#> 8 8 0.8680 -1.140 0.9360 3.4000 2.6200 11.000
#> 9 9 0.8240 -1.260 0.8680 2.6200 2.0100 9.350
#> 10 10 0.8460 -1.220 0.8770 2.0600 1.5000 8.000
#> 11 11 0.8670 -1.160 0.8860 1.6400 1.2300 6.900
#> 12 12 0.7820 -1.250 0.7270 1.3200 1.0000 5.990
#> 13 13 0.8750 -1.290 0.8720 1.0800 0.7600 5.240
#> 14 14 0.8790 -1.240 0.8780 0.8940 0.5780 4.610
#> 15 15 0.8480 -1.210 0.8220 0.7450 0.4460 4.080
#> 16 16 0.8010 -1.240 0.7450 0.6270 0.3560 3.620
#> 17 17 0.8350 -1.230 0.7900 0.5310 0.2740 3.240
#> 18 18 0.1200 -1.940 -0.1080 0.4530 0.2120 2.900
#> 19 19 0.1220 -1.870 -0.1070 0.3890 0.1670 2.610
#> 20 20 0.8880 -1.200 0.8590 0.3360 0.1340 2.360
#> 21 21 0.8730 -1.210 0.8370 0.2920 0.1080 2.140
#> 22 22 0.8080 -1.170 0.7550 0.2540 0.0886 1.950
#> 23 23 0.8450 -1.190 0.8030 0.2230 0.0739 1.780
#> 24 24 0.1450 -2.560 -0.0914 0.1960 0.0618 1.620
#> 25 25 0.1460 -2.540 -0.0918 0.1730 0.0512 1.490
#> 26 26 0.1510 -2.540 -0.0875 0.1540 0.0423 1.370
#> 27 27 0.0977 -1.490 -0.0975 0.1370 0.0344 1.260
#> 28 28 0.9180 -1.150 0.8950 0.1220 0.0280 1.160
#> 29 29 0.9220 -1.120 0.9020 0.1090 0.0225 1.080
#> 30 30 0.8760 -1.120 0.8400 0.0979 0.0181 0.999
wgcna_input$fitIndices
#> Power SFT.R.sq slope truncated.R.sq mean.k. median.k.
#> 1 1 0.84286571 3.8040087 0.91423798 52.51960238 53.34184052
#> 2 2 0.06456817 -0.3146023 0.14136303 30.53003771 30.76853194
#> 3 3 0.66481255 -1.2767899 0.58472439 19.01674430 18.57511438
#> 4 4 0.75838466 -1.1499238 0.81633829 12.51048871 11.84379473
#> 5 5 0.68163955 -1.0462208 0.72716272 8.60319272 7.72652181
#> 6 6 0.71878451 -1.1308681 0.73335186 6.13592098 5.22626809
#> 7 7 0.75754120 -1.1024293 0.80426538 4.51077219 3.68053165
#> 8 8 0.86835973 -1.1446400 0.93573615 3.40121169 2.61507055
#> 9 9 0.82448245 -1.2633108 0.86803680 2.62007559 2.01016163
#> 10 10 0.84575084 -1.2179895 0.87657268 2.05548331 1.50432291
#> 11 11 0.86662570 -1.1568222 0.88604377 1.63803619 1.22596921
#> 12 12 0.78183532 -1.2522205 0.72720615 1.32325157 1.00069379
#> 13 13 0.87501515 -1.2939303 0.87217562 1.08177336 0.75980830
#> 14 14 0.87892749 -1.2364530 0.87771012 0.89371838 0.57809789
#> 15 15 0.84752610 -1.2051610 0.82190396 0.74530426 0.44613190
#> 16 16 0.80123884 -1.2403673 0.74533663 0.62677815 0.35607325
#> 17 17 0.83484572 -1.2265512 0.79043207 0.53111009 0.27379134
#> 18 18 0.11955170 -1.9402328 -0.10826258 0.45314831 0.21187911
#> 19 19 0.12163397 -1.8714360 -0.10737965 0.38906091 0.16718735
#> 20 20 0.88781004 -1.1996277 0.85914411 0.33595920 0.13438680
#> 21 21 0.87332488 -1.2077457 0.83713926 0.29163891 0.10825081
#> 22 22 0.80763163 -1.1709049 0.75520878 0.25439934 0.08860988
#> 23 23 0.84529468 -1.1899539 0.80308953 0.22291500 0.07387197
#> 24 24 0.14533913 -2.5600205 -0.09137579 0.19614317 0.06181385
#> 25 25 0.14636875 -2.5395493 -0.09184163 0.17325658 0.05118420
#> 26 26 0.15064567 -2.5419407 -0.08750052 0.15359366 0.04232804
#> 27 27 0.09768514 -1.4862196 -0.09748448 0.13662144 0.03441214
#> 28 28 0.91763379 -1.1487339 0.89520184 0.12190765 0.02803675
#> 29 29 0.92239635 -1.1225358 0.90195533 0.10909937 0.02253314
#> 30 30 0.87570167 -1.1205509 0.84048182 0.09790681 0.01807656
#> max.k.
#> 1 61.0086938
#> 2 42.4575218
#> 3 31.5655844
#> 4 24.4616573
#> 5 19.5066573
#> 6 15.8859320
#> 7 13.1490520
#> 8 11.0267391
#> 9 9.3479355
#> 10 7.9986024
#> 11 6.8997128
#> 12 5.9947653
#> 13 5.2423238
#> 14 4.6113587
#> 15 4.0782261
#> 16 3.6246430
#> 17 3.2362938
#> 18 2.9018468
#> 19 2.6122467
#> 20 2.3601963
#> 21 2.1397703
#> 22 1.9461248
#> 23 1.7752746
#> 24 1.6239227
#> 25 1.4893261
#> 26 1.3691917
#> 27 1.2615932
#> 28 1.1649050
#> 29 1.0777494
#> 30 0.9989543
picked_power <- wgcna_input$powerEstimate