text: user1369: I have 25 tumor samples with counts data. Initially, I filtered out low expressed genes and then converted counts to varianceStabilizingTransformation using deseq2 package. With this data I started using WGCNA for co-expression network analysis. 
  For selecting the soft threshold I see very strange plot. R2 cutoff is 0.8 and I see that none of the scale free topology model fit is above that. 
  Here is the code I used:
  df is a dataframe with genes as rows and 25 samples as columns with counts data.
  library("DESeq2")
  filtered.counts <- df[rowSums(df==0)<5, ]
  
  U3 <- as.matrix(filtered.counts)
  vsd <- vst(U3, blind=FALSE)
  
  oed <- vsd
  
  gene.names=rownames(oed)
  trans.oed=t(oed)
  dim(trans.oed)
  
  n=16462;
  datExpr=trans.oed[,1:n]
  dim(datExpr)
  
  SubGeneNames=gene.names[1:n]
  
  library(WGCNA)
  options(stringsAsFactors = FALSE);
  allowWGCNAThreads()
  
  powers = c(c(1:10), seq(from = 12, to=20, by=2));
  sft=pickSoftThreshold(datExpr,dataIsExpr = TRUE,
                        powerVector = powers,corFnc = cor,
                        corOptions = list(use = 'p'),networkType = "unsigned")
  
     Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
  1      1 0.000273  0.041          0.786 4140.00  4000.000 6730.0
  2      2 0.428000 -1.130          0.852 1570.00  1400.000 3770.0
  3      3 0.673000 -1.540          0.882  729.00   579.000 2410.0
  4      4 0.737000 -1.720          0.891  383.00   268.000 1670.0
  5      5 0.745000 -1.830          0.886  220.00   134.000 1210.0
  6      6 0.704000 -1.990          0.860  134.00    71.700  909.0
  7      7 0.737000 -1.980          0.890   85.90    40.300  701.0
  8      8 0.742000 -2.020          0.903   57.30    23.500  551.0
  9      9 0.733000 -2.090          0.917   39.40    14.200  441.0
  10    10 0.750000 -2.080          0.934   27.80     8.810  357.0
  11    12 0.770000 -2.080          0.952   14.80     3.670  243.0
  12    14 0.775000 -2.090          0.951    8.43     1.660  171.0
  13    16 0.397000 -2.820          0.459    5.06     0.801  124.0
  14    18 0.409000 -2.770          0.474    3.18     0.406   91.4
  15    20 0.421000 -2.720          0.490    2.06     0.215   68.8
  
  
  # Plot the results
  sizeGrWindow(9, 5)
  par(mfrow = c(1,2));
  cex1 = 0.9;
  
  # Scale-free topology fit index as a function of the soft-thresholding power
  plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit, signed R^2",type="n", main = paste("Scale independence"));
  text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],labels=powers,cex=cex1,col="red");
  
  
  Is this the right way? Which soft threshold power should I select? 
  Any help is appreciated. thanq.
  
  user851: Don't worry about it too much. I would go with power 8 based on my general experience (also reflected in WGCNA FAQ) and on the mean connectivity around 50 and median around 20, which seems reasonable to me.
  
original_text: 1369: <p>I have 25 tumor samples with counts data. Initially, I filtered out low expressed genes and then converted counts to <code>varianceStabilizingTransformation</code> using <code>deseq2</code> package. With this data I started using <code>WGCNA</code> for co-expression network analysis. </p>
  
  <p>For selecting the soft threshold I see very strange plot. R2 cutoff is 0.8 and I see that none of the scale free topology model fit is above that. </p>
  
  <p>Here is the code I used:</p>
  
  <p><code>df</code> is a dataframe with genes as rows and 25 samples as columns with counts data.</p>
  
  <pre><code>library("DESeq2")
  filtered.counts &lt;- df[rowSums(df==0)&lt;5, ]
  
  U3 &lt;- as.matrix(filtered.counts)
  vsd &lt;- vst(U3, blind=FALSE)
  
  oed &lt;- vsd
  
  gene.names=rownames(oed)
  trans.oed=t(oed)
  dim(trans.oed)
  
  n=16462;
  datExpr=trans.oed[,1:n]
  dim(datExpr)
  
  SubGeneNames=gene.names[1:n]
  
  library(WGCNA)
  options(stringsAsFactors = FALSE);
  allowWGCNAThreads()
  
  powers = c(c(1:10), seq(from = 12, to=20, by=2));
  sft=pickSoftThreshold(datExpr,dataIsExpr = TRUE,
                        powerVector = powers,corFnc = cor,
                        corOptions = list(use = 'p'),networkType = "unsigned")
  
     Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
  1      1 0.000273  0.041          0.786 4140.00  4000.000 6730.0
  2      2 0.428000 -1.130          0.852 1570.00  1400.000 3770.0
  3      3 0.673000 -1.540          0.882  729.00   579.000 2410.0
  4      4 0.737000 -1.720          0.891  383.00   268.000 1670.0
  5      5 0.745000 -1.830          0.886  220.00   134.000 1210.0
  6      6 0.704000 -1.990          0.860  134.00    71.700  909.0
  7      7 0.737000 -1.980          0.890   85.90    40.300  701.0
  8      8 0.742000 -2.020          0.903   57.30    23.500  551.0
  9      9 0.733000 -2.090          0.917   39.40    14.200  441.0
  10    10 0.750000 -2.080          0.934   27.80     8.810  357.0
  11    12 0.770000 -2.080          0.952   14.80     3.670  243.0
  12    14 0.775000 -2.090          0.951    8.43     1.660  171.0
  13    16 0.397000 -2.820          0.459    5.06     0.801  124.0
  14    18 0.409000 -2.770          0.474    3.18     0.406   91.4
  15    20 0.421000 -2.720          0.490    2.06     0.215   68.8
  
  
  # Plot the results
  sizeGrWindow(9, 5)
  par(mfrow = c(1,2));
  cex1 = 0.9;
  
  # Scale-free topology fit index as a function of the soft-thresholding power
  plot(sft<span class="math-container">$fitIndices[,1], -sign(sft$</span>fitIndices[,3])*sft<span class="math-container">$fitIndices[,2],xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit, signed R^2",type="n", main = paste("Scale independence"));
  text(sft$</span>fitIndices[,1], -sign(sft<span class="math-container">$fitIndices[,3])*sft$</span>fitIndices[,2],labels=powers,cex=cex1,col="red");
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/MJBpD.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/MJBpD.png" alt="enter image description here"></a></p>
  
  <p>Is this the right way? Which soft threshold power should I select? </p>
  
  <p>Any help is appreciated. thanq.</p>
   851: <p>Don't worry about it too much. I would go with power 8 based on my general experience (also reflected in WGCNA FAQ) and on the mean connectivity around 50 and median around 20, which seems reasonable to me.</p>
  