%% BioMed_Central_Tex_Template_v1.06
%%                                      %
%  bmc_article.tex            ver: 1.06 %
%                                       %

%%IMPORTANT: do not delete the first line of this template
%%It must be present to enable the BMC Submission system to
%%recognise this template!!

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                     %%
%%  LaTeX template for BioMed Central  %%
%%     journal article submissions     %%
%%                                     %%
%%          <8 June 2012>              %%
%%                                     %%
%%                                     %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                                                 %%
%% For instructions on how to fill out this Tex template           %%
%% document please refer to Readme.html and the instructions for   %%
%% authors page on the biomed central website                      %%
%% http://www.biomedcentral.com/info/authors/                      %%
%%                                                                 %%
%% Please do not use \input{...} to include other tex files.       %%
%% Submit your LaTeX manuscript as one .tex document.              %%
%%                                                                 %%
%% All additional figures and files should be attached             %%
%% separately and not embedded in the \TeX\ document itself.       %%
%%                                                                 %%
%% BioMed Central currently use the MikTex distribution of         %%
%% TeX for Windows) of TeX and LaTeX.  This is available from      %%
%% http://www.miktex.org                                           %%
%%                                                                 %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%% additional documentclass options:
%  [doublespacing]
%  [linenumbers]   - put the line numbers on margins

%%% loading packages, author definitions

%\documentclass[twocolumn]{bmcart}% uncomment this for twocolumn layout and comment line below
\documentclass{bmcart}

%%% Load packages
%\usepackage{amsthm,amsmath}
%\RequirePackage{natbib}
%\RequirePackage[authoryear]{natbib}% uncomment this for author-year bibliography
%\RequirePackage{hyperref}
\usepackage[utf8]{inputenc} %unicode support
\usepackage{bm}
\usepackage{amsmath}
\usepackage{array}
\usepackage{graphicx}
\usepackage{hyperref}
\usepackage{xcolor}
%\usepackage[applemac]{inputenc} %applemac support if unicode package fails
%\usepackage[latin1]{inputenc} %UNIX support if unicode package fails


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                             %%
%%  If you wish to display your graphics for   %%
%%  your own use using includegraphic or       %%
%%  includegraphics, then comment out the      %%
%%  following two lines of code.               %%
%%  NB: These line *must* be included when     %%
%%  submitting to BMC.                         %%
%%  All figure files must be submitted as      %%
%%  separate graphics through the BMC          %%
%%  submission process, not included in the    %%
%%  submitted article.                         %%
%%                                             %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%\def\includegraphic{}
%\def\includegraphics{}



%%% Put your definitions there:
\startlocaldefs
\endlocaldefs


%%% Begin ...
\begin{document}

%%% Start of article front matter
\begin{frontmatter}

\begin{fmbox}
\dochead{Research}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                          %%
%% Enter the title of your article here     %%
%%                                          %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\title{A large-scale comparative study of isoform expressions measured on four platforms}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                          %%
%% Enter the authors here                   %%
%%                                          %%
%% Specify information, if available,       %%
%% in the form:                             %%
%%   <key>={<id1>,<id2>}                    %%
%%   <key>=                                 %%
%% Comment or delete the keys which are     %%
%% not used. Repeat \author command as much %%
%% as required.                             %%
%%                                          %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\author[
   addressref={aff1},                   % id's of addresses, e.g. {aff1,aff2}           
   %corref={aff1,aff2},                       % id of corresponding   
   noteref={},                        % id's of article notes, if any
   email={wzhang.cs@ucf.edu}   % email address
]{\inits{WZ}\fnm{Wei} \snm{Zhang}}
\author[
   addressref={aff2},                   % id's of addresses, e.g. {aff1,aff2}   
   noteref={},                        % id's of article notes, if any
   email={}   % email address
]{\inits{RP}\fnm{Raphael} \snm{Petegrosso}}
\author[
   addressref={aff3},                   % id's of addresses, e.g. {aff1,aff2}                   
   noteref={},                        % id's of article notes, if any
   email={}   % email address
]{\inits{JW}\fnm{Jae-Woong} \snm{Chang}}
{\color{red}\author[
   addressref={aff1},                   % id's of addresses, e.g. {aff1,aff2}   
   noteref={},                        % id's of article notes, if any
   email={}   % email address
]{\inits{JS}\fnm{Jiao} \snm{Sun}}}
\author[
   addressref={aff3},                   % id's of addresses, e.g. {aff1,aff2}                   
   noteref={},                        % id's of article notes, if any
   email={}   % email address
]{\inits{JY}\fnm{Jeongsik} \snm{Yong}}
\author[
   addressref={aff4},                   % id's of addresses, e.g. {aff1,aff2}                   
   noteref={},                        % id's of article notes, if any
   email={}   % email address
]{\inits{JC}\fnm{Jeremy} \snm{Chien}}
\author[
    addressref={aff2},
   corref={aff2},                       % id of corresponding address, if any
   noteref={},                        % id's of article notes, if any
   email={kuang@umn.edu}   % email address
]{\inits{RK}\fnm{Rui} \snm{Kuang}}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                          %%
%% Enter the authors' addresses here        %%
%%                                          %%
%% Repeat \address commands as much as      %%
%% required.                                %%
%%                                          %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\address[id=aff1]{%                           % unique id
  \orgname{Department of Computer Science, University of Central Florida}, % university, etc
  \street{4000 Central Florida Blvd},                     %
  \city{Orlando},   
  \postcode{FL 32816},                                % post or zip code                          % city
  \cny{USA}                                    % country
}
\address[id=aff2]{%
  \orgname{Department of Computer Science and Engineering, University of Minnesota Twin Cities},
  \street{200 Union Street SE},
  \city{Minneapolis},
\postcode{MN 55455},
  \cny{USA}
}
\address[id=aff3]{%
  \orgname{Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities},
  \street{200 Union Street SE},
  \city{Minneapolis},
\postcode{MN 55455},
  \cny{USA}
}
\address[id=aff4]{%
  \orgname{Department of Biochemistry and Molecular Medicine, University of California, Davis},
  \street{2700 Stockton Blvd.},
  \city{Sacramento},
\postcode{CA 95817},
  \cny{USA}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                          %%
%% Enter short notes here                   %%
%%                                          %%
%% Short notes will be after addresses      %%
%% on first page.                           %%
%%                                          %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{artnotes}
%\note{Sample of title note}     % note to the article
%\note[id=n1]{Equal contributor} % note, connected to author
\end{artnotes}

\end{fmbox}% comment this for two column layout

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                          %%
%% The Abstract begins here                 %%
%%                                          %%
%% Please refer to the Instructions for     %%
%% authors on http://www.biomedcentral.com  %%
%% and include the section headings         %%
%% accordingly for your article type.       %%
%%                                          %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{abstractbox}

\begin{abstract} % abstract
\parttitle{Background}
Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles and thus it is necessary to accurately measure isoform expressions as well as {\color{red}gene expressions}. While previous studies have demonstrated the strong agreement between mRNA-sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains.

\parttitle{Results} %if any
 In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. {\color{red}The goal is} to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 403 custom-designed probes for measuring the expressions of {\color{red}478} isoforms in 155 genes and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array and Microarray data of 46 of the 59 cell lines for the comparative analysis. 

\parttitle{Conclusion} In the comparisons of the platforms for evaluating expressions at both isoform and gene levels, we found that (1) the degree of agreement across platforms on quantifying isoform expressions is lower than gene expressions; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array quantification results on isoform-level compared to NanoString; (4) different RNA-seq isoform quantification algorithms showed inconsistent results, and two isoform quantification methods Net-RSTQ and eXpress are more consistent across the platforms in the comparison; {\color{red}and} (5) RNA-seq has the best overall {\color{red}consistency} with the other platforms on gene expression quantification.
\end{abstract}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                          %%
%% The keywords begin here                  %%
%%                                          %%
%% Put each keyword in separate \kwd{}.     %%
%%                                          %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{keyword}
\kwd{isoform-level expression}
\kwd{cross-platform comparison}
\kwd{NanoString}
\kwd{RNA-seq}
\kwd{Exon-arry}
\kwd{Microarray gene expression}
\end{keyword}

% MSC classifications codes, if any
%\begin{keyword}[class=AMS]
%\kwd[Primary ]{}
%\kwd{}
%\kwd[; secondary ]{}
%\end{keyword}

\end{abstractbox}
%
%\end{fmbox}% uncomment this for twcolumn layout

\end{frontmatter}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                          %%
%% The Main Body begins here                %%
%%                                          %%
%% Please refer to the instructions for     %%
%% authors on:                              %%
%% http://www.biomedcentral.com/info/authors%%
%% and include the section headings         %%
%% accordingly for your article type.       %%
%%                                          %%
%% See the Results and Discussion section   %%
%% for details on how to create sub-sections%%
%%                                          %%
%% use\cite{...} to cite references        %%
%%\cite{koon} and                         %%
%%\cite{oreg,khar,zvai,xjon,schn,pond}    %%
%%  \nocite{smith,marg,hunn,advi,koha,mouse}%%
%%                                          %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%% start of article main body
% <put your article body there>

%%%%%%%%%%%%%%%%
%% Background %%
%%
\section{Background}
In eukaryote genome, a single gene can contain multiple exons and introns, where the exons can be alternatively spliced together in different ways. Recent studies have estimated that alternative splicing events exist in more than 95\% of multi-exon genes in human and mouse\cite{zhiguo2013splicing,wang2015mechanism}, and the mechanism provides the opportunity to create protein isoforms of differing functions from a single gene in a cellular system. Therefore, elucidating {\color{red}gene expression} at the isoform resolution {\color{red} could improve our understanding} of molecular mechanisms and potentially {\color{red}improve} molecular signals for cancer phenotype predictions\cite{zhang2015network}. However, accurately quantifying isoform expression is significantly more challenging than estimating aggregated gene expressions due to ambiguity in overlapped regions between alternative isoforms in the same gene\cite{dapas2016comparative}.

Several high-throughput platforms have been developed during the last decades for transcriptome studies, including mRNA sequencing (RNA-seq), array-based technologies (Microarray and Exon-arrays), reverse transcriptome-quantitative polymerase chain reaction (RT-qPCR), and {\color{red}recently developed} NanoString's nCounter technology (Figure \ref{fig:platforms}). Currently RNA-seq is the most commonly used platform for measuring expression across the transcriptome. {\color{red}It provides} more sensitive and accurate gene and isoform expressions levels, {\color{red}and it detects} both known and novel features in a single assay (e.g., transcript isoforms, gene fusion, single nucleotide variants) without requiring prior knowledge compared to array-based technologies\cite{dapas2016comparative,zhao2014comparison,wang2009rna}. While at a lower-throughput, RT-qPCR enables the determination of the exact amounts of amplified DNAs in a sample, which are often considered as the gold standard for expression analysis to confirm the results of the studies based on RNA-seq or Microarray. The NanoString nCounter platform captures and counts individual mRNA transcripts. Its advantages over the other platforms include direct measurement of mRNA expression levels without enzymatic reactions, high sensitivity coupled with high multiplex capability, and digital readout\cite{geiss2008direct,kulkarni2011digital}. The strengths and limitations associated with each platform are described in Table \ref{tab:ADV}. Due to these discrepancies, different platforms can report inconsistent isoform expressions measured on the RNAs extracted from the same cell sample. 

In this paper, we study the correlations of the expressions estimated by four different platforms including RNA-seq, NanoString nCounter, Microarray/Exon-array and RT-qPCR, at isoform and gene levels to better understand the characteristics of the estimations by the platforms. To access the correlations across the platforms, we first performed custom NanoString nCounter experiments on 59 cancer cell lines with 403 custom-designed probes for measuring the expressions of {\color{red}478} isoforms in 155 genes. We then combined the nCounter data with the matched RNA-seq, exon-array and Microarray data of 46 of the 59 cell lines from Cancer Cell Line Encyclopedia (CCLE)\cite{barretina2012cancer} and Gene Expression Omnibus (GEO)\cite{edgar2002gene} to perform isoform and gene abundance estimation on raw expression profiles of the 46 cell lines on all the platforms. Additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines were also performed for validations.

Previous studies have demonstrated the agreement between RNA-seq and array-based platforms for isoform expression estimation\cite{dapas2016comparative} and gene expression estimation\cite{zhao2014comparison,guo2013large,wang2014concordance}. However, none of the studies have included large-scale NanoString data at isoform level in the comparison, nor did the studies investigate large-scale isoform profiles across multiple cancer types. {\color{red}More knowledge of the mechanisms behind} of estimating gene and isoform expressions with the four platforms could {\color{red}result in} better strategies to design experiments for cancer studies, and to integrate different platforms together to more accurately estimate the mRNA expressions for applications in disease biomarker detection, cancer outcome prediction, drug target identification and drug response prediction.

\section{Results}
\subsection{Cancer cell line data preparation}
The NanoString experiment was conducted on 59 cell lines including  12 ovary cell lines, 12 lung cell lines, 11 colon cell lines, 10 breast cell lines, four pancreas cell lines, two prostate cell lines, two stomach cell lines, and six cell lines of six other types of tissues as listed in Table S1 in the supplementary document. A total of 403 probes were customized to estimate the isoform expressions of 155 cancer genes curated from the literature \cite{COSMIC} with more reliable isoform annotations, where each of the 155 genes contains at least two isoforms. As shown in Table S1, 46 out of the 59 cell lines are also the cancer cells in CCLE. The raw mRNA-Sequencing (RNA-seq) data and Microarray expression data (HG-U133 Plus 2.0 Array) of the 46 cancer cell lines were downloaded from CCLE and processed for comparison with the NanoString nCounter data. Additionally, the raw Microrarray expression data of the same 46 cancer cell lines were downloaded from GEO as the replicates of the CCLE Microarray data in the analysis. The raw Exon-array (HuEx.1.0.st.v2) of 35 of the 59 cancer cell lines were also downloaded from GEO to compare with the other platforms. Finally, a RT-qPCR experiment was designed to measure the expressions of a subset of the genes and isoforms in 13 cell lines to validate the expressions measured on the other platforms. The complete list of the cell lines and the data availability on each platform in this study is available in Table \ref{tab:data} and Table S1.

\subsection{Comparison of isoform expression profiles across platforms}
\subsubsection{Low consistency among the estimated isoform expressions across the platforms.} The correlations of isoform expressions among different platforms are reported in Figure \ref{fig:isoform}. Each dot in the boxplot represents the {\color{red}Spearman} correlation coefficient of the {\color{red}478} isoforms expressions in 155 genes between two platforms for one cell line. Each boxplot shows the correlation coefficients measured for all the cell lines between two compared platforms. Five different isoform quantification methods (Net-RSTQ\cite{zhang2015network}, Cufflinks\cite{trapnell2010transcript}, RSEM\cite{li2011rsem}, eXpress\cite{roberts2013streaming} and Kallisto\cite{bray2016near}) were used to quantify the isoform expressions for the RNA-seq data. 

{\color{red}In general, the Spearman correlations of isoform expressions between NanoString and RNA-seq (median $\bm{R_s} = 0.55 \sim 0.63$) are lower than the correlations between Exon-array and RNA-seq (median $\bm{R_s} = 0.62 \sim 0.68$). The correlations between NanoString and Exon-array are relatively low (median $\bm{R_s} = 0.55$).} Figure \ref{fig:isoform3} shows the overall correlation coefficients between platforms with isoform quantification method Net-RSTQ for RNA-Seq. Each dot in the scatter plots represents one isoform in one cell line. {\color{red}The overall correlation coefficients are between 0.52 and 0.63 in the three comparisons.} In a similar analyses on gene-level expression later reported in Figure \ref{fig:gene} and Figure \ref{fig:gene2}, the overall correlations are much higher {\color{red}(median $\bm{R_s} = 0.68 \sim 0.82$)}. The observation is consistent with the fact that besides mapping reads to genes, additional analysis steps are required to  resolve the read assignment to the shared exon region(s) for estimating the isoform expressions, and thus, compared to gene expression quantification, the consistency across platforms on estimated isoform expression is lower.

\subsubsection{NanoString and Exon-array are less consistent on isoform quantification.} It is also {\color{red}beneficial} to measure the isoform proportions in each gene rather than the exact isoform expressions. e.g., an investigator might only need to know which isoform has higher expression than the other isoforms in the gene. In Figure \ref{fig:isoform2}, the isoform proportions estimated by NanoString were used as the reference proportions to evaluate different RNA-seq quantification methods and quantification based on the Exon-array platform. {\color{red}The differences of isoform proportions estimated by NanoString and other platforms are measured by Equation \eqref{eqn:diff} in the Methods section. From the result in Figure \ref{fig:isoform2}, we can see that isoform proportions estimated by Exon-array are less consistent with the proportions estimated by NanoString compared to those estimated by most RNA-seq quantification methods}, even though the hybridization reaction is a shared key step in both nCounter and array-based experiments but not in RNA sequencing techniques. This observation in this isoform proportion comparison also is consistent with the results for isoform expression comparison in Figure \ref{fig:isoform} and Figure \ref{fig:isoform3}.

\subsubsection{Net-RSTQ and eXpress provide more consistent isoform quantification with RNA-seq data.} In both Figure \ref{fig:isoform} and Figure \ref{fig:isoform2}, the compared isoform quantification methods on RNA-seq data show different levels of consistency with the other two platforms. In Figure \ref{fig:isoform}, the rankings of median correlations for the five methods are similar in the comparisons with NanoString and Exon-array platforms, even if the quantification results are not consistent between NanoString and Exon-array. Specifically, eXpress\cite{roberts2013streaming} and Net-RSTQ\cite{zhang2015network} are the most consistent methods, followed by RSEM\cite{li2011rsem}. {\color{red}Kallisto\cite{bray2016near} and Cufflinks\cite{trapnell2010transcript} are the least consistent methods.} By comparing with NanoString data and Exon-array data, eXpress and Net-RSTQ seems to provide more reliable isoform quantifications with RNA-seq data compared with the other methods. Similar results are also observed in the isoform proportion analysis in Figure \ref{fig:isoform2}, Net-RSTQ and eXpress provide the most consistent estimations of proportions compared with the estimation on the NanoString data.

{\color{red}\subsubsection{Isoform proportion estimation is a challenging problem.}}
To further evaluate the performance of isoform proportion estimation by different platforms and different methods, we compared the estimated proportions with the uniform (fixed) isoform proportions within each gene, i.e. we naively assume that all the isoforms in the same gene have the same proportion, and measured the difference between the uniform proportions and the proportions estimated on the NanoString data in Figure \ref{fig:isoform2}. {\color{red}It is interesting that only eXpress estimated isoform proportions close to the uniform distribution, and all the other four isoform estimation methods on RNA-seq data and the estimation on Exon-array data performed less consistently than the uniform distribution. Since some isoforms cannot be distinguished to the other isoforms in NanoString data, Equation \eqref{eqn:nano} may assign similar isoform proportions to these isoforms in the same gene. This may explain why most results are worse than the uniform distribution.}

\subsubsection{RT-qPCR experiments agree more with the RNA-seq and Exon-array results.}
Among the genes that NanoString, RNA-seq and Exon-array report the most different quantification results for, we selected four genes (ASXL1,  LRIG3,  NOTCH2  and  SF3B) to perform RT-qPCR experiments in the 13 cell lines with relatively high expression levels and feasibility of designing isoform-specific primers.

The isoform proportions estimated by NanoString data, Exon-array data, and five different methods for RNA-seq data analysis were compared to the RT-qPCR estimation in the boxplots in Figure \ref{fig:qRT-PCR} and {\color{red} scatter plots in Figure S2 in the supplementary document}. In general, the isoform proportions estimated by RNA-seq and Exon-array show better agreement with RT-qPCR quantification than NanoString. The median differences (Equation \eqref{eqn:diff} in the Methods section) across 13 cell lines are between 0.20 $\sim$ 0.24 for RNA-seq platform and 0.20 for Exon-array platform, while the median difference is 0.33 in comparison to NanoString, which is significantly higher than RNA-seq and Exon-array {\color{red}($p$-values {\textless} 1e-3)}.

\subsection{Comparison of gene expression profiles across platforms}
\subsubsection{RNA-seq is consistent with other platforms on gene expression quantification.}
The scatter plots comparing the gene expressions estimated by four platforms are shown in Figure \ref{fig:gene}. RSEM was applied to RNA-seq quantification and CCLE Microarray data used in this comparison. The correlations on the gene expressions between RNA-seq and the other three platforms shown in Figure \ref{fig:gene}(A), (D) and (E) are the most consistent in the comparisons of each pair of platforms ($\bm{R_s} = 0.75 \sim 0.82$). The other three paired comparisons on other three platforms shown in Figure \ref{fig:gene}(B), (C) and (F) show correlation coefficients between 0.68 and 0.71. Although Microarray and Exon-array platforms were both array based, the correlation was the lowest in all the six pairs. The high consistency between Microarray and RNA-seq platforms may have benefited from the same experimental conditions and cell materials used in the CCLE project. A more comprehensive comparison is shown in Figure \ref{fig:gene2}. Each dot in the boxplots represents the correlation coefficient of the expressions of 155 genes in one cell line between two platforms. Even though the gene expressions in the {\color{red}cell lines were estimated} by the same platform in the Microarray (GEO) and Microarray (CCLE), the correlation is still low {\color{red}(Figure S6 in the supplementary document). Cufflinks and RSEM yield similar consistent gene expressions with the other platforms in the RNA-seq data.}

\subsubsection{NanoString, RNA-seq and Exon-array detect lowly expressed genes with less consistency.}
Similar to the observations in previous studies \cite{wang2009rna}, the results in Figure \ref{fig:gene} also show that Microarray platforms are not sensitive enough to detect {\color{red}lowly expressed} genes due to the background noise from cross-hybridization. There is a clear pattern that in the range of lowly expressed genes estimated in NanoString and RNA-seq data, Microarray gene expressions are all at the lowest end with very small differences. Exon-array assigns many probes to the exons in a gene and thus, is slightly more sensitive than the Microarray only measuring gene expressions. 

The pairwise comparison of {\color{red}lowly expressed} genes across the platforms in Figure \ref{fig:gene_low} also indicate that the detected expressions of the low-abundance genes are less consistent across NanoString, RNA-seq, Exon-array, and Microarray. Some of the genes were very rarely expressed in one platform, but highly expressed in the other platform(s). {\color{red}Across the four platforms}, Exon-array has the least agreement to the other three platforms.

\subsubsection{Fold change and {\color{red}differential} expression analysis.}
The fold-change was calculated based on breast cancer cell lines versus all the other cell lines. {\color{red}The correlations of fold-change between platforms are reported in Figure \ref{fig:FC}}. Compared to the gene-expression correlations in Figure \ref{fig:gene}, the agreement in fold changes are lower. Among all the six pairwise comparisons, RNA-seq and Microarray show the highest consistency on the fold-change analysis in Figure \ref{fig:FC}(D). {\color{red}The ``x-shape" in Figure \ref{fig:FC}(D) shows that the up/down regulated genes are consistent in RNA-seq and Microarray platforms. However, in all the other plots in Figure \ref{fig:FC}, there are always some genes that show up-regulation in one platform but down-regulation in the other. In general, it is consistent between the correlation in gene expressions and the correlation in fold changes in Figure \ref{fig:gene} and Figure \ref{fig:FC}}.

In the differential gene expression analysis, ANOVA was applied to detect the top differentially expressed genes across six tissue types (ovary, lung, colon, breast pancreas, and prostate) on the gene expressions estimated by NanoString, RNA-seq and Microarray. The top-10 genes identified from each platform are reported in Table \ref{tab:DEG} and Figure S3-S5 in the supplementary document. {\color{red}Only MYB is identified by all the three platforms. MYB and EWSR1 are the only common genes detected by both NanoString and Microarray. Six out of the top-10 genes identified from RNA-seq were also detected either in NanoString or Microarray}.

\section{Discussions and conclusions}
During the last decade, mRNA sequencing has gradually taken over transcriptome-wide gene expression profiling from array-based technologies. NanoString nCounter platform promises to yield less biased and more scalable experimental results compared to RT-qPCR for targeted (customized) expression profiling. Many researchers and medical practitioners choose one platform over another without knowing the expected differences in the outcomes of the technologies. In this study, we evaluated four different platforms on both isoform and gene expression profiling for a large number of cancer cell lines. The experiments showed {\color{red}relatively high} agreement among the platforms on gene expression profiling (Figure \ref{fig:gene}), while isoform expressions estimated by the platforms were less consistent (Figure \ref{fig:isoform}). %Furthermore, isoform proportions within each gene estimated by isoform quantification methods on the platforms are more inconsistent than guessing by uniform distribution. 
Collectively, the results suggest isoform quantification is still a more challenging problem than gene expression profiling, both the experimental platform and the isoform quantification method play a critical role for reliable estimation of isoform expressions. Potentially, integration of the isoform expression data collected across the platforms may improve the reliability of isoform expression estimation.

A major challenge of evaluating isoform expression quantification techniques is the lack of large-scale benchmark data with ground-truth isoform expression levels. RT-qPCR is usually considered as the gold-standard for validating the results from the analysis of RNA-seq and array-based data \cite{dapas2016comparative,wang2014concordance}. While in our experiments, NanoString platform did not show more consistent results with array-based techniques and RT-qPCR compared with RNA-seq (Figure \ref{fig:isoform} - Figure \ref{fig:FC}), the discrepancy might be inherent between NanoString and the other three platforms---nCounter technology does not rely on amplification and reverse transcription as RNA-Seq, RT-qPCR and Microarray technologies do. RNA is commonly converted to more stable complementary DNA (cDNA) by reverse transcription for PCR amplification. It is desirable that the resulting cDNA population represents the original RNA population. However, both reverse transcription and PCR amplification introduce biases based on the base-compositions of gene transcripts in the quantification of the transcripts. The high error rate of reverse transcription can also impact data quality. In addition, the current technologies require that the cDNA molecules represent only fragments of the RNAs in an appropriate size for sequencing or hybridization to Microarray. The fragmentation sites are often non-random as a result of sequence-dependent features such as GC-content, epigenomic modifications and DNA conformational energy. Though the bias can be traced back to specifics of the preparation protocols, it is not possible to predict fragment distribution directly from a protocol in different technologies. 
Therefore, it is not clear if NanoString is indeed less reliable than RNA-seq. In principle, {\color{red}the} NanoString nCounter technique holds several advantages over RT-qPCR and RNA-seq, such as directly measuring RNAs without amplification or cloning, no enzymatic steps required, high level of sensitivity since both the probes and the target are in solution rather than binding to a surface, and digital readout. The current limit of nCounter is to measure up to 800 probes in one experiment, which is far from sufficient for transcriptome-wide profiling. Moreover, the capture and reporter probes can only be designed to identify known isoforms, and thus NanoString technology is not capable of detecting novel transcripts.

This study also made several additional useful observations. First, Net-RSTQ and eXpress tend to produce more consistent isoform quantification on the RNA-seq data than the other compared isoform quantification methods. Different from the other methods, Net-RSTQ is a network-based approach that directly incorporates additional information from protein domain-domain interactions to overcome the limitations of short-read alignments for transcript quantification, {\color{red}since the protein products of highly co-expressed transcripts are more likely to interact with each other by protein domain-domain binding \cite{zhang2015network}}. The eXpress model applies additional normalization strategies by jointly estimating parameters for the fragment-length distribution, sequencing errors and sequencing biases to improve the isoform expression estimation \cite{roberts2013streaming}. Second, NanoString and RNA-seq are more sensitive to lowly expressed genes than the array-base platforms, but the profiling by the two platforms are still not consistent with each other in the low-expression range. {\color{red}Third, the agreement among the platforms on fold-change analysis is moderate, which is lower than the agreement on gene expression analysis, suggesting that fold-change normalization against another sample will decrease the agreement across platforms. Fourth, the top differentially expressed genes across different tissue types identified by RNA-seq agree with the detection by one or the other platform while the other two platforms have less agreement. Fifth, correlation between the two Microarray datasets, CCLE and GEO, are more consistent compared to the analysis between two different platforms (Figure S6 in the supplementary document). However, the correlation for Microarray (GEO), which the samples were generated by different labs for different studies, are systematically lower than the correlation correlations for Microarray (CCLE), which the samples were generated by the same project (Figure 7). This might be caused by the technical bias of the Microarray (GEO) samples from different laboratories. Further normalization may improve the consistency with the other platforms.}

%\section{Conclusions}
%This study made several useful observations. In the comparison, the four different platforms agree more on the gene expression estimation than isoform-level. RNA-seq is consistent with other platforms on both gene expression and isoform expression quantification. On the isoform-level, we found that (1) NanoString and Exon-array report inconsistent results; (2) Net-RSTQ and eXpress are more accurate compared to other quantification methods with RNA-seq data; (3) isoform proportion estimation within each gene is a challenge task, it may worse than assign the same expression values for all the isoforms in the same gene. On the gene-level, (1) NanoString, RNA-seq and Exon-array are more sensitive than Microarray platform and can detect rare expressed gene, but the results are not consistent with each other; (2) the agreement among platforms on fold-change analysis is lower than on gene expression analysis; (3) the top differentially expressed genes identified by different platforms across different tissue types are not consistent.

\section{Methods}
\subsection{NanoString probe design and nCounter experiments}
{\color{red}We designed an experiment for measuring the isoform expressions of 155 multi-isoform cancer genes in 59 cell lines. 79 genes contain two isoforms, 37 genes contain three isoforms, 18 genes contain four isoforms, and 21 genes contain more than four isoforms. We customized 403 capture-reporter probes to capture the 478 isoforms expressions in the genes based on the RefSeq hg19 annotation (RefSeq release 66)}. In addition, 14 spike-in probes for quality control and 10 probes for measuring 10 house keeping genes for normalization of the data are also introduced. Each probe is 100 bps long designed to capture a target sequence in one or multiple isoforms. For each gene, the probes were designed to distinguish the expressions {\color{red}of all known isoforms} in the gene, and one of the probes was always designed to measure the gene expression. We showed one real example in Figure \ref{fig:probedesign}: four probes were designed to measure the four isoform expressions in gene FLI1. \emph{Probe max} measures the expression of gene level. \emph{Probe 1}, \emph{2}, and \emph{3} measure the isoform expression of NM\_001271012, NM\_001167681, and NM\_001271010, respectively. The expression level of isoform NM\_002017 can be calculated by {\color{red}taking} the gene expression minus the total expression of the other three isoforms. The designed probes in only two out of 155 genes cannot distinguish the expressions of all the isoforms in the gene. Two technical replicates of each cell line were measured. The log($\bm{x}$+1) normalized probe signals for all the 403 probes from two technical replicates are shown in Figure S1. The plot shows that the nCounter experiments are highly reproducible: a linear fit to the log transformed data results in a correlation coefficient of 0.9867. The dataset is deposited to GEO (\href{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133226}{GSE133226}).

\subsection{NanoString gene and isoform abundance estimation}
The geometric mean of the ten house keeping genes (TAF5L, HPS6, DNTTIP2, GNRHR, ZNF407, KCNK7, KIAA1539, RBM12, DGCR14, and KHDRBS1) was {\color{red}applied to normalize the} NanoString signal in each cell line. Then, the expressions of isoforms in one gene can be estimated by minimizing the differences between the {\color{red}assessed} and observed intensities for all probes in the gene as
\begin{equation}\label{eqn:nano}
\begin{aligned}
& \underset{\bm{x}}{\text{min}}
& & ||\bm{y}-\bm{Ax}||^{2}_{F}\\
& \text{s.t.} & &  x_i \geq 0,\\
\end{aligned}
\end{equation}
where vector $\bm{y}$ represents the probe intensities. $\bm{A}$ is a $m$-$by$-$n$ indicator matrix with values 0 or 1, where $m$ is the total number of probes, and $n$ is the total number of isoforms in the gene. If probe $j$ covers the exon region of isoform $i$ in the gene, then $\bm{A}_{(j,i)}=1$, otherwise $\bm{A}_{(j,i)}=0$. Vector $\bm{x}$ represents the isoform expressions to learn. After the isoform expression vector $\bm{x}$ is learned, the total gene expression can be derived by accumulating {\color{red}the isoform expressions}. Finally, the gene and isoform expressions are all normalized by log($\bm{x}$+1). 

\subsection{RNA-seq gene and isoform abundance estimation}
The RNA-seq paired-end reads were aligned and quantified using hg19 reference genome with STAR\cite{dobin2013star} with default parameters. RNA-seq gene abundances were estimated using Cufflinks\cite{trapnell2010transcript} and RSEM\cite{li2011rsem} and the isoform abundances were estimated using Net-RSTQ\cite{zhang2015network}, Cufflinks, RSEM, eXpress\cite{roberts2013streaming}, and Kallisto\cite{bray2016near} with RefSeq annotation\cite{o2015reference}. The Fragments Per Kilobase Million (FPKM) or Transcripts Per Million (TPM) were reported by each method for each isoform (transcript) as the expression value. The gene-level expressions were estimated by summing all the isoform abundances in the gene. To account for the large dynamic range of abundances, the expressions are finally normalized by log(FPKM+1) or log(TPM+1).

\subsection{Exon-array gene and isoform abundance estimation}
Gene and isoform expressions were generated from the raw Exon-array data (.CEL files) of the 35 cancer cell lines using Multi-Mapping Bayesian Gene eXpression (MMBGX)\cite{turro2009mmbgx}. Isoforms and genes were quantified using the Ensembl hg19 reference annotation {\color{red}(Ensembl release 70)}. The method disaggregates the signal between alternative transcripts of a gene to estimate the expression of each individual isoform. {\color{red}To compare Exon-array with other platforms, we only used the genes with the same annotations in both Ensembl and RefSeq annotations.} 

\subsection{Microarray gene abundance estimation}
We collected two independent Microarray gene expression datasets for the same 46 cancer cell lines from {\color{red} the CCLE\footnote{\url{https://portals.broadinstitute.org/ccle}} and GEO\footnote{\url{https://www.ncbi.nlm.nih.gov/geo/}} websites}. The raw CEL files were normalized by Robust Multichip Average (RMA)\cite{irizarry2003exploration}. After merging probes by gene symbols according to the RefSeq reference annotation, gene expressions derived from the 22,283 probes are included in this study.

\subsection{RT-qPCR experiment design}
The RT-qPCR experiment was designed to measure the isoform proportion of four multi-isoform genes (ASXL1, LRIG3, NOTCH2 and SF3B) in 13 cell lines. The four genes were selected from the genes with the most different quantification results reported by NanoString, RNA-seq and Exon-array platforms, and based on the availability of the primers to distinguish the isoforms in the genes in the RT-qPCR experiments. The 13 human cell lines were selected based on the availability of cell culture in our labs.

In the experiments, {\color{red} RNAs from the cells were isolated by the Trizol method according to manufacturer’s protocol (Thermo Fisher Scientific\footnote{\url{https://assets.thermofisher.com/TFS-Assets/LSG/manuals/trizol_reagent.pdf}}). Reverse transcription reaction using Oligo-d(T) priming and superscript III was carried out according to the manufacturer's protocol. (Thermo Fisher Scientific\footnote{\url{https://assets.thermofisher.com/TFS-Assets/LSG/manuals/superscriptIII_man.pdf}}). The SYBR Green (Bio-Rad) was used to detect and quantify PCR products in real-time reactions. The comparative Ct method was used for data analyses, and we normalized the Ct values to the amount of total RNAs. The primer sequences used to measure the expression for each transcript are the following:\\
hASXL1 iso1,2 for		TAAACTGCCTGGCCGAATCA\\
hASXL1 iso1 (NM\_001164603.1) rev	TAAGATGAAGGGGCCTGGTG\\
hASXL1 iso2 (NM\_015338.5) rev 		CTGTAGCTGGATGGCGAGAC\\
hLRIG3 iso1,2 rev				CTCATGGAACTTGCCTTGATGA\\
hLRIG3 iso1 (NM\_001136051.2) for 	TTGTTCTCCCTCTGCTTGCT\\
hLRIG3 iso2 (NM\_153377.4) for		CGTCTTCCCGAGCCACTC\\
hNOTHC2 iso1,2 for				ACCTTGTGAACCATTTCAAGTGC\\
hNOTHC2 iso1 (NM\_024408.3) rev		GGCACAGTCATCAATGTTCTCT\\
hNOTHC2 iso2 (NM\_001200001.1) rev	GACAATGCCCTGGATGGAAAA\\
hSF3B iso1,2 for				GGCGGACCATGATAATTTCCC\\
hSF3B iso1 (NM\_012433.3) rev		TTAGGATCAGGGGTTTTCCCTC\\
hSF3B iso2 (NM\_001005526.2) rev		TCAGCAGTTCTGACTTCAAGC.\\

\noindent {\bf Cell culture}: Human cancer cell lines were obtained from the American Type Culture Collection (ATCC; www.atcc.org) and cultured according to standard mammalian tissue culture protocols and sterile technique:\\ 
MCF7 (www.atcc.org/Products/All/HTB-22.aspx\#culturemethod)\\ 
AGC (www.atcc.org/products/all/CRL-1739.aspx\#culturemethod)\\
MDA-MB-231 (www.atcc.org/products/all/HTB-26.aspx\#culturemethod)\\
T-47D (www.atcc.org/products/all/HTB-133.aspx\#culturemethod)\\
HCT-15 (www.atcc.org/products/all/CCL-225.aspx\#culturemethod)\\
Hep G2 (www.atcc.org/products/all/HB-8065.aspx\#culturemethod)\\
HCT 116 (www.atcc.org/products/all/CCL-247.aspx\#culturemethod)\\
HT-29 (www.atcc.org/products/all/HTB-38.aspx\#culturemethod)\\
HT-1080 (www.atcc.org/products/all/CCL-121.aspx\#culturemethod)\\
PC-3 (www.atcc.org/products/all/CRL-1435.aspx\#culturemethod)\\
DU 145 (www.atcc.org/products/all/HTB-81.aspx\#culturemethod)\\
A549 (www.atcc.org/products/all/CCL-185.aspx\#culturemethod)\\
BT-549 (www.atcc.org/products/all/HTB-122.aspx\#culturemethod).}
{\color{red}
\subsection {Compare isoform quantification consistency}
The differences of isoform proportions estimated by two platforms are measured by the following formula:
 \begin{equation}
 d = \frac{\sum_{i=1}^{n}\frac{\sum_{j=1}^{m_i}|p^{a}_{ij}-p^{b}_{ij}|}{m_i}}{n},\label{eqn:diff}
 \end{equation}
where $i$ is the index of the $n$ genes in the cell line, $j$ is the index of the $m_i$ isoforms in the gene $i$ in the cell line, $a$ and $b$ denote two different platforms, and $p^{a}_{ij}$ denotes the proportion of the $j$th isoform in the gene $i$ in the platform $a$. For each gene $i$, $\sum_{j=1}^{m_i}p^{a}_{ij} = 1$.}


\subsection{Correlation, fold-change and differential expression analysis}
Cell line by cell line correlations of the isoform and gene expressions between two platforms were evaluated by the {\color{red}Spearman correlation coefficient. The lowly expressed genes were defined as the bottom one third genes by expression in each platform.} Fold-change between the cell lines in one tissue type and all the other cell lines was calculated to compare the power of detecting the differentially expressed genes among the different platforms. {\color{red}RSEM for RNA-seq was used to derive the gene expressions for the differential expression analysis.} The top differentially expressed genes were detected by ANalysis Of VAriance (ANOVA)\cite{anderson2001new} across different tissue types.

{\color{red}All the source code and processed datasets in this study are available at: \href{https://github.com/compbiolabucf/Cross-Platform-Comparison}{https://github.com/compbiolabucf/Cross-Platform-Comparison}}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                                          %%
%% Backmatter begins here                   %%
%%                                          %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\section*{Declarations}
\begin{backmatter}
\section*{Ethics approval and consent to participate}
Not applicable.

\section*{Cosent for publication}
Not applicable.

\section*{Availability of data and materials}
The raw NanoString data is deposited to GEO (\href{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133226}{GSE133226}).
All the source code and processed datasets in this study are available at: \href{https://github.com/compbiolabucf/Cross-Platform-Comparison}{https://github.com/compbiolabucf/Cross-Platform-Comparison}

\section*{Competing interests}
 The authors declare that they have no competing interests.

\section*{Funding}
The study was supported by a grant from the National Science Foundation (1117153) and a grant from the National Institutes of Health (R01GM113952). WZ is partially supported by a grant from the National Science Foundation (1755761) and RK is also partially supported by a grant from the National Institute of Health (1R01CA225435). 

\section*{Authors' contributions}
WZ, RK, RP and JC conceived the study and planned the analysis. WZ, RP and RK designed the probes for NanoString data to capture the isoform expressions. JC and JY designed and performed RT-qPCR experiments. WZ JS performed data analysis. WZ, JC and RK wrote the manuscript. All authors read and approved the final manuscript.

\section*{Acknowledgements}
We thank the anonymous reviewers for helpful comments.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                  The Bibliography                       %%
%%                                                         %%
%%  Bmc_mathpys.bst  will be used to                       %%
%%  create a .BBL file for submission.                     %%
%%  After submission of the .TEX file,                     %%
%%  you will be prompted to submit your .BBL file.         %%
%%                                                         %%
%%                                                         %%
%%  Note that the displayed Bibliography will not          %%
%%  necessarily be rendered by Latex exactly as specified  %%
%%  in the online Instructions for Authors.                %%
%%                                                         %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% if your bibliography is in bibtex format, use those commands:
\bibliographystyle{bmc-mathphys} % Style BST file (bmc-mathphys, vancouver, spbasic).
\bibliography{bmc_article}      % Bibliography file (usually '*.bib' )
% for author-year bibliography (bmc-mathphys or spbasic)
% a) write to bib file (bmc-mathphys only)
% @settings{label, options="nameyear"}
% b) uncomment next line
%\nocite{label}

% or include bibliography directly:
% \begin{thebibliography}
% \bibitem{b1}
% \end{thebibliography}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                               %%
%% Figures                       %%
%%                               %%
%% NB: this is for captions and  %%
%% Titles. All graphics must be  %%
%% submitted separately and NOT  %%
%% included in the Tex document  %%
%%                               %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%
%% Do not use \listoffigures as most will included as separate files
\newpage
\section*{Figures}
\begin{figure}[h!]
\begin{tabular}{c}
{\scalebox{0.45}{\includegraphics{Figure1.pdf}}}
\end{tabular}
\caption{\csentence{Four platforms.} (A) NanoString nCounter. A capture probe works together with a reporter probe to capture the signal in the target sequences. Each capture-reporter probe pair is tagged with a distinct color-coded barcode to represent the detection of a single target molecule for direct digital readout.  nCounter Digital Analyzer reports the expression levels of targeted mRNAs. In this example, Probe 1 measures the total expressions of all the isoforms of the gene; Probe 2 measures the expression of Isoform 1; Probe 3 measures the expression of Isoform 3. (B) mRNA-sequencing (RNA-seq). Paired-end sequencing of fragmented cDNAs generated by next-generation sequencing technology are aligned to reference genome. The read coverage can be analyzed to infer the mRNA abundance by RNA-seq quantification methods. (C) Exome Microarray. In the Microarray experiment, exon-level expressions are estimated based on the hybridization intensity measurements by multiple probes targeting the putative exonic regions. After normalization, quantification methods are applied to estimate gene and isoform expressions. (D) Quantitative reverse transcription PCR (RT-qPCR). In this platform, RNAs are first transcribed into cDNAs by reverse transcriptase from the total RNA. Then the cDNAs are used as the template for the qPCR reaction. The gene and isoform expression can be estimated based on the amplified DNAs after performing qPCR.}\label{fig:platforms}
\end{figure}
\begin{figure}[h]
\begin{tabular}{c}
{\scalebox{0.2}{\includegraphics{Figure2.pdf}}}
\end{tabular}
\caption{\csentence{Correlations between estimated isoform expressions on two platforms.} In the boxplots, each dot represents the correlation coefficient measured on one compared cell line. The mean and the standard deviation of the correlation coefficients of all the compared cancer cell lines are also reported. Five different isoform quantification methods are used to generate the profiles with RNA-seq data in the comparison.}\label{fig:isoform}
\end{figure}

\begin{figure}[h]
\begin{tabular}{c}
{\scalebox{0.2}{\includegraphics{Figure3.pdf}}}
\end{tabular}
\caption{\csentence{Scatter plots of isoform expressions estimated between two different platforms.} Each dot represents one isoform in one cell line. The isoform quantification by Net-RSTQ is applied to the RNA-seq data in the comparison. The overall correlation is also reported for each comparison.}\label{fig:isoform3}
\end{figure}

\begin{figure}[h]
\begin{tabular}{c}
{\scalebox{0.25}{\includegraphics{Figure4.pdf}}}
\end{tabular}
\caption{\csentence{Validation by comparison with NanoString.} The boxplots show the differences between NanoString and the other two platforms (RNA-seq and Exon-array) on isoform proportion estimation. Each dot represents the average isoform proportion differences between NanoString and one quantification method for RNA-seq or Exon-array in one cell line. A `Uniform' method which assigns the same proportion to all the isoforms in one gene is added as the baseline.}\label{fig:isoform2}
\end{figure}

\begin{figure}[h]
\begin{tabular}{c}
{\scalebox{0.28}{\includegraphics{Figure5.pdf}}}
\end{tabular}
\caption{\csentence{Validation with RT-qPCR results.} The boxplots shows the differences in the estimation of isoform proportions between RT-qPCR and the other platforms. Each dot represents the average isoform proportion differences between RT-qPCR and another platform or one quantification method on RNA-seq data in one cell line. `Uniform' represents assigning the same proportion to all the isoforms in one gene.}\label{fig:qRT-PCR}
\end{figure}

\begin{figure}[h]
\begin{tabular}{c}
{\scalebox{0.27}{\includegraphics{Figure6.pdf}}}
\end{tabular}
\caption{\csentence{Scatter plots of gene expressions estimated on two platforms.} Each dot represents one gene in one cell line. RSEM is applied for RNA-seq quantification and the CCLE Microarray gene expression data are used in the comparison. The overall correlation is also reported for each comparison.}\label{fig:gene}
\end{figure}

\begin{figure}[h]
\begin{tabular}{c}
{\scalebox{0.23}{\includegraphics{Figure7.pdf}}}
\end{tabular}
\caption{\csentence{Correlation between gene expressions estimated on two platforms.} The box plots show the results of different cancer cell lines with each dot representing the correlation coefficient for one cell line. Two different RNA-seq quantification methods and two different versions of Microarray gene expression datasets (CCLE and GEO) are compared.}\label{fig:gene2}
\end{figure}

\begin{figure}[h]
\begin{tabular}{c}
{\scalebox{0.25}{\includegraphics{Figure8.pdf}}}
\end{tabular}
\caption{\csentence{Scatter plots of the lowly expressed genes estimated on two platforms.} Each dot represents one gene in one cell line.  RSEM is applied for RNA-seq quantification and the CCLE Microarray gene expression data is used in the comparison.}\label{fig:gene_low}
\end{figure}


\begin{figure}[h]
\begin{tabular}{c}
{\scalebox{0.4}{\includegraphics{Figure9.pdf}}}
\end{tabular}
\caption{\csentence{Scatter plots of fold-changes in gene expressions (breast cancer cell line versus another cell line) between two platforms.} Each dot represents the fold-change of one gene between each breast cancer cell line and one cell line of other cancers.  RSEM is applied for RNA-seq quantification and the CCLE Microarray gene expression data is used in the comparison.}\label{fig:FC}
\end{figure}

\begin{figure}[h]
\begin{tabular}{c}
{\scalebox{0.35}{\includegraphics{Figure10.pdf}}}
\end{tabular}
\caption{\csentence{Probe design of gene FLI1.} There are four isoforms in gene FLI1 and four probes were designed to distinguish the expressions of the isoforms. \emph{Probe max} measures the expression for the gene and the other three probes were designed to quantify three individual isoforms.}\label{fig:probedesign}
\end{figure}

%\begin{figure}[h!]
%\caption{\csentence{Reproducibility.} NanoString correlation plot showing technical replicates of 59 cell line samples. Normalized log values of signal are shown.}\label{fig:rep}
%\end{figure}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                               %%
%% Tables                        %%
%%                               %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%% Use of \listoftables is discouraged.
%%
\newpage
\section*{Tables}
%
\begin{table}[h]
\centering
\caption{{Advantages and limitations of each platform.}}
\label{tab:ADV}
\begin{tabular}{| m{5em} | m{22em}| m{22em} |}
\hline
&\textbf{Advantages}&\textbf{Limitations}\\
\hline
\textbf{NanoString}&{(1) RNAs are directly measured without amplification or cloning; (2) Digital readout generates less background noise; and (3) Allows high dynamic range of expressions.}&{(1) Only a limited number of probes are available to measure isoform or gene expression; and (2) Sophisticated custom probe design is often needed.}\\
\hline
\textbf{RNA-seq}&{(1) No requirement of gene annotations; (2) Capable of detecting novel splicing isoforms; (3) Allows high dynamic range of expressions; and (4) Provides transcriptome-wide expression profiling.}&{(1) Transcript specific bias/ 3' end bias; and (2) Various sampling biases as a result of library preparation protocols.}\\
\hline
\textbf{Exon-array}&{(1) Provides transcriptome-wide expression profiling;  and (2) Capable of measuring expressions at exons and exon-junctions.}&{(1) Limited dynamic range; (2) Requires a higher amount of molecules in RNA preparation; and (3) Depends on existing genome annotation.}\\
\hline
\textbf{RT-qPCR}&{(1) High dynamic range of expressions; and (2) Less biased results and much cheaper in comparison to RNA-seq and Microarray on a small scale study.}&{(1) Only a limited number of transcripts can be measured; and (2) Custom primer designs are often needed.}\\
\hline
\end{tabular}
\end{table}

%\begin{table}[h]
%\centering
%\caption{{Advantages and limitations of each platform.}}
%\begin{tabular}{| c | cccc |}
%\hline
%\textbf{Criteria/Platform}&\textbf{NanoString}&\textbf{RNA-seq}&\textbf{Exon-array}&\textbf{RT-qPCR}\\
%\hline
%\textbf{Probe Design}&{Yes}&{No}&{Yes}&{Yes (Primer Design)}\\
%\hline
%\textbf{Transcriptome-wide}&{No}&{Yes}&{Yes}&{No}\\
%\hline
%\textbf{Inference Isoform Expression}&{No}&{Yes}&{Yes}&{No}\\
%\hline
%\end{tabular}
%\end{table}




\begin{table}[h]
\scriptsize
\centering
\caption{{Summary of the availability of cell line data in each platform.}}
\label{tab:data}
\begin{tabular}{|c| ccccc |}
\hline
&\textbf{\# of cell lines}&\textbf{\# of genes}&\textbf{\# of isoforms}&\textbf{Resource}&\textbf{Platform}\\
\hline
\textbf{NanoString}&{59}&{155}&{{\color{red}478}}&{}&{nCounter}\\
\hline
\textbf{RNA-seq}&{46}&{transcriptome-wide}&{transcriptome-wide}&{CCLE}&{Illumina HiSeq 2000}\\
\hline
\textbf{Microarray Gene Expression}&{46}&{transcriptome-wide}&{transcriptome-wide}&{CCLE and GEO}&{HG-U133 Plus 2.0 Array}\\
\hline
\textbf{Exon-array}&{\color{red}35}&{transcriptome-wide}&{transcriptome-wide}&{GEO}&{Affy HuEx.1.0.st.v2}\\
\hline
\textbf{RT-qPCR}&{13}&{4}&{8}&{}&{}\\
\hline
\end{tabular}
\end{table}

\begin{table}[h]
\centering
\caption{{The top-10 differentially expressed genes.} The genes identified by three platforms are marked in red and by two platforms are marked in blue.}
\label{tab:DEG}
\begin{tabular}{|cc|cc|cc|}
\hline
\multicolumn{2}{|c|}{\textbf{NanoString}} & \multicolumn{2}{c|}{\textbf{RNA-seq}}  & \multicolumn{2}{c|}{\textbf{Microarray}} \\
\hline
\textbf{Gene Name}  & \textbf{p-value}  & \textbf{Gene Name} & \textbf{p-value} & \textbf{Gene Name}  & \textbf{p-value}  \\
\hline
\color{red}{MYB}                 & 7.176e-06         & \color{blue}{LIFR}               & 3.813e-05        & \color{red}{MYB}                 & 1.132e-05         \\
\color{blue}{LIFR}                & 1.667e-04         & \color{red}{MYB}                & 6.103e-05        & PRDM16              & 2.846e-03         \\
ATRX                & 8.292e-04         & PRF1               & 1.359e-04        & \color{blue}{SLC34A2}              & 3.766e-03         \\
SMARCA4             & 3.455e-03         & \color{blue}{SLC34A2}            & 5.697e-04        & \color{blue}{EML4}                & 4.375e-03         \\
KLK2                & 8.929e-03         & ZNF331             & 8.192e-04        & HNRNPA2B1           & 4.783e-03         \\
\color{blue}{ETV4}                & 0.01022           & WWTR1              & 2.557e-04        & SYK                 & 5.649e-03         \\
\color{blue}{EWSR1}               & 0.01138           & \color{blue}{EML4}               & 3.915e-03        & \color{blue}{EWSR1}                & 5.877e-03         \\
MSI2                & 0.01167           & \color{blue}{ETV4}               & 7.149e-03        & \color{blue}{BCL6}                 & 8.001e-03         \\
SRSF2               & 0.01218           & TFG                & 8.614e-03        & NONO                & 8.039e-03         \\
TCF3                & 0.01561           & \color{blue}{BCL6}               & 9.211e-03        & GPC3                & 8.258e-03\\
\hline
\end{tabular}
\end{table}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                               %%
%% Additional Files              %%
%%                               %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%\section*{Additional Files}
%  \subsection*{Additional file 1 --- Sample additional file title}
%    Additional file descriptions text (including details of how to
%    view the file, if it is in a non-standard format or the file extension).  This might
%    refer to a multi-page table or a figure.
%
%  \subsection*{Additional file 2 --- Sample additional file title}
%    Additional file descriptions text.


\end{backmatter}
\end{document}
