fix inlineMath vs displayMath

_layouts/default.html

@@ -11,7 +11,8 @@
     <script>
         MathJax = {
           tex: {
-            inlineMath: [['$$', '$$'], ['\\(', '\\)']]
+            inlineMath: [['$', '$'], ['\\(', '\\)']],
+            displayMath: [['$$', '$$'], ['\\[', '\\]']]
           },
           svg: {
             fontCache: 'global'

docs/Advanced_Step-by-step_Peak_Calling.md

@@ -162,7 +162,7 @@ control.
 
 The whole genome background can be calculated as
 `the_number_of_control_reads\fragment_length/genome_size`, and in our
-example, it is $$199867*254/2700000000 ~= .0188023$$. You don\'t need to
+example, it is $199867*254/2700000000 ~= .0188023$. You don\'t need to
 run subcommands to build a genome background track since it\'s just a
 single value.
 

docs/bdgdiff.md

@@ -35,9 +35,9 @@ The likelihood function we used while comparing two conditions: ChIP
 
 $$ln(LR) = x*(ln(x)-ln(y)) + y - x$$
 
-Here $$LR$$ is the likelihood ratio, x is the signal (fragment pileup)
+Here $LR$ is the likelihood ratio, x is the signal (fragment pileup)
 we observed in condition 1, and y is the signal in condition
-2. And $$ln$$ is the natural logarithm.
+2. And $ln$ is the natural logarithm.
 
 Note: All regions on the same chromosome in the bedGraph file should
 be continuous so only bedGraph files from MACS3 are acceptable.

docs/callvar.md

@@ -157,37 +157,37 @@ likelihood functions of ChIP and control data:
 
 $$L(\omega,\phi,g_c,g_i:D)=L(\omega,g_c:D_c)L(\phi,g_i:D_i)$$
 
-where $$D_c$$ and $$D_i$$ represent the ChIP-Seq and control (e.g.,
+where $D_c$ and $D_i$ represent the ChIP-Seq and control (e.g.,
 genomic input) data observed at the position including base coverage
-and base qualities. The parameter $$\omega$$ stands for the allele ratio
+and base qualities. The parameter $\omega$ stands for the allele ratio
 of allele A (chosen as the more abundant or stronger allele compared
-with the others) from the ChIP-Seq data and $$\phi$$ represents the
-allele ratio in the control. The parameter $$g_c$$ represents the
+with the others) from the ChIP-Seq data and $\phi$ represents the
+allele ratio in the control. The parameter $g_c$ represents the
 actual number of ChIPed DNA fragments containing allele A, which could
-differ from the observed count $$r_{c,A}$$ considering that some
-observations could be due to sequencing errors. The symbol $$g_i$$
-represents the control analogously to $$g_c$$. We use $$r_c$$ to
-denote the total number of observed allele A ($$r_{c,A}$$) and allele
-B ($$r_{c,B}$$). We assume the occurrence of the allele A ($$g_c$$)
-is from a Bernoulli trial from $$r_c$$ with the allele ratio
-$$\omega$$. The probability of observing the ChIP-Seq data at a certain
+differ from the observed count $r_{c,A}$ considering that some
+observations could be due to sequencing errors. The symbol $g_i$
+represents the control analogously to $g_c$. We use $r_c$ to
+denote the total number of observed allele A ($r_{c,A}$) and allele
+B ($r_{c,B}$). We assume the occurrence of the allele A ($g_c$)
+is from a Bernoulli trial from $r_c$ with the allele ratio
+$\omega$. The probability of observing the ChIP-Seq data at a certain
 position under a given type is as follows:
 
 
 $$Pr(D_c|g_c,\omega) = Pr(D_c|g_c) =
  \sum^{r_{c,A}}_{j=1}\left((1-\epsilon_j)g_c/r_c+\epsilon_j(1-g_c/r_c)\right)\sum_{j=1}^{r_{c,B}}\left((1-\epsilon_j)(1-g_c/r_c)+\epsilon_j
  g_c/r_c\right)$$
 
-where $$\epsilon_j$$ represents the sequencing error of the base
+where $\epsilon_j$ represents the sequencing error of the base
 showing difference with reference genome in case of mismatch
 (corresponding to SNV) and insertion. In case of deletion, the
 sequencing errors from the two bases on sequenced read surrounding the
 deletion would be considered. We model the control data in the similar
 way. We assess the likelihood functions of the 4 major type using the
-following parameters: $$\omega=1,\phi=1,g_c=r_{c,0},g_i=r_{i,0}$$ for
-A/A genotype; $$\omega=0,\phi=0,g_c=0,g_i=0$$ for B/B genotype,
-$$\omega=0.5,\phi=0.5$$ and $$g_c,g_i$$ as free variables for A/B
-genotype with unbiased binding; $$\phi=0.5$$ and $$\omega,g_c,g_i$$ as
+following parameters: $\omega=1,\phi=1,g_c=r_{c,0},g_i=r_{i,0}$ for
+A/A genotype; $\omega=0,\phi=0,g_c=0,g_i=0$ for B/B genotype,
+$\omega=0.5,\phi=0.5$ and $g_c,g_i$ as free variables for A/B
+genotype with unbiased binding; $\phi=0.5$ and $\omega,g_c,g_i$ as
 free variables for A/B genotype with biased binding or allele
 usage. Next, we apply the Bayesian Information Criterion (BIC) to
 select the best type as our prediction with the minimal BIC value