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BMC Genomics

Open Access

A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data

  • Yongli Hu1, 4Email author,
  • Takeshi Hase2,
  • Hui Peng Li3,
  • Shyam Prabhakar3,
  • Hiroaki Kitano2,
  • See Kiong Ng1,
  • Samik Ghosh2 and
  • Lawrence Jin Kiat Wee1, 4
BMC Genomics201617(Suppl 13):1025

https://doi.org/10.1186/s12864-016-3317-7

Published: 22 December 2016

Abstract

Background

The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a heterogeneous population of cells, one at a time. However, till date, there has not been a suitable computational methodology for the analysis of such intricate deluge of data, in particular techniques which will aid the identification of the unique transcriptomic profiles difference between the different cellular subtypes. In this paper, we describe the novel methodology for the analysis of single-cell RNA-seq data, obtained from neocortical cells and neural progenitor cells, using machine learning algorithms (Support Vector machine (SVM) and Random Forest (RF)).

Results

Thirty-eight key transcripts were identified, using the SVM-based recursive feature elimination (SVM-RFE) method of feature selection, to best differentiate developing neocortical cells from neural progenitor cells in the SVM and RF classifiers built. Also, these genes possessed a higher discriminative power (enhanced prediction accuracy) as compared commonly used statistical techniques or geneset-based approaches. Further downstream network reconstruction analysis was carried out to unravel hidden general regulatory networks where novel interactions could be further validated in web-lab experimentation and be useful candidates to be targeted for the treatment of neuronal developmental diseases.

Conclusion

This novel approach reported for is able to identify transcripts, with reported neuronal involvement, which optimally differentiate neocortical cells and neural progenitor cells. It is believed to be extensible and applicable to other single-cell RNA-seq expression profiles like that of the study of the cancer progression and treatment within a highly heterogeneous tumour.

Keywords

Single-cell RNA-seqMachine learningNetwork reconstructionSystems biology

Background

The advent of sequencing technology has brought about the unprecedented ability to sequence individual single cells. Now, the distinct gene expression profiles of seemingly similar yet genetically heterogeneous subpopulations of cells within different tissue types can be elucidated with the use of single-cell sequencing technology. The study of such subpopulations within tumours is especially important in the study of differential reactivity of patients to drug treatments and that of acquired drug resistance within cancer patients [1, 2]. The complex underlying transcriptomic dynamics elucidated will enhance our understanding of the distinct gene expression signatures of different carcinomas or subpopulations within disparate tumour tissues which will ultimately aid in the optimization of cancer treatments.

A major challenge, however remains, is that of a suitable computational analytic pipeline for the analysis of single-cell RNA-Seq transcriptomic data. To address this problem, this paper proposes the identification of the unique gene expression profile within each subpopulation through traditional statistical methodology, geneset enrichment analysis (GSEA), machine learning algorithms where genes identified are subsequently used to build predictive classifiers for cell type prediction. Computational analysis of RNA-Seq transcriptomic data using machine learning algorithms, particularly that of supervised learning algorithms, like rule-based machine learning techniques [3], Support Vector Machine (SVM)-based [4, 5] and network-based approaches [6], is not new. However, this paper is the first to utilize a combination of two different machine learning algorithms (SVM and Random Forest (RF)) on single-cell RNA-seq transcriptomic data to identify the key signatures of different cell types for cell type prediction. Using single-cell RNA-seq expression data from neocortical cells and those of neural progenitor cells as inputs, we have identified a set of 38 key genes which optimally differentiates developing neocortical cells and those of neural progenitor cells.

Further, relevance of the differentially expressed genes in neuronal cell differentiation were also investigated using network-based approaches where the gene regulatory networks (GRNs) inferred elucidated the potential underlying interactions/functions of the key hub genes (eg, genes that regulate many genes in neuronal cells but do not regulate genes in neuronal progenitor cells) which could be further validated in wet-lab experimentation [7, 8]. In summary, this paper described a novel computational pipeline for the study of single-cell RNA-Seq transcriptomic data where key genes identified were used, with high accuracy, to predict distinct neuronal cell subtypes where such a system could be used to uncover the different subpopulations within a newly sequenced brain tissue. In addition, downstream network studies lend a systems-level relevance where potential underlying relationships are unravelled and potentially be used for targeted for treatment in neuronal developmental diseases.

Results

Prefiltering of genes

The summary of the methodology employed in this paper is summarized in Fig. 1.
Fig. 1

Workflow for data analysis carried out in this paper. *All genes refers to the set of genes filtered by expression values and ^ selected genes refers to the optimal set of genes identified by geneset enrichment analysis (GSEA), statistical and machine-learning approaches (See Methods for more information)

Raw data, downloaded from the NCBI databased, was filtered (the criteria used for data filtering is reported in the materials and methods section) to obtain a total 8681 (~15%) genes which represents the dataset denoted by “all genes”. A total of 65 samples (15 NPC and 50 developing neuronal cells) were used as training data to SVM and RF algorithms. Significance of the cell types assignment is further validated using Pvclust [9], which employs a multiple bootstrap (50,000) resampling algorithm to calculate the approximately unbiased (AU) probability values for cluster distinctions which is shown in red in Fig. 2.
Fig. 2

Clustering of 65 neuronal cells. The approximately unbiased (AU) probability value at each node is shown in red font. There are four distinct clusters (red boxes labelled 1–4) with an AU higher than 80. Box1 comprises of mainly NPCs while boxes 2–4 primarily consists of neuronal cells only

SVM and RF-based classification of neuronal cell types on all expressed genes

Each set of gene expression data, extracted from different feature selection methods, was used to train two different machine learning (ML) models, namely a SVM and a RF classifier. The classification was reduced to a two-class problem where the predictor was designed to identify potential neocortical neuronal cells from NPCs.

Due to the limited number of samples, 65 (15 NPC and 50 neuronal cells) used in this study, the data was not separated into training and testing set for the construction of SVM-based classifiers. Instead, leave-one-out (LOO) cross validation was carried out. However, this was deemed unnecessary for RF as classifiers were built by aggregating a large number of different decision trees, predictors built with the random forests algorithm is expected to have low variance and low bias.

SVM and RF classifiers built with the filtered high dimensional single-cell RNA-seq expression dataset, consisting of more than 8000 transcripts (Table 1), yields an accuracy of 95.3 and 76.9% respectively (Table 2). It seems like classifiers built with all transcripts, sans those of low expression, are able produce classifiers of a reasonably high accuracy, however, the quality of such classifiers needs to be co-ordinately investigated. To this end, the Matthews correlation coefficient (MCC) was used to validate the quality of the classifiers constructed. A coefficient of 1 represents a prefect prediction while that of 0 indicates a classifier producing predictions similar to random prediction. The SVM classifiers were far superior to the cognate RF classifiers having a MCC of 0.91 and 0, respectively. Thus, there is a need for additional feature transcript selection process to enhance both the accuracy and the quality of the constructed classifiers. Additionally, the construction of classifiers based on all transcripts are computational inefficient and the inclusion of large number of “noisy genes” will obscure important underlying signatures of each phenotypic class due to data overfitting and this will greatly limit the accuracy and quality of the classifiers [10]. On a more biological note, such a method fails to identify a subset of key genes which might have important biological applications in novel biomarker discovery.
Table 1

Genes/features selected by disparate feature selection techniques

Feature selection techniquesa

Features/Genes (No.)

Filtered by low expression

8281

GSVA feature enrichment

1161

sRAP

837

SVM-RFE

38

RF-based Positive MDA

3339

T-test

60

aFeature selection is based on five different methodologies based on machine learning algorithms (SVM and RF) and also that of traditional differentially expressed genes (sRAP), t-test based analysis (limma) and genes in deregulated pathways (GSVA)

Table 2

Accuracy of RF and SVM classifiers on the neuronal dataset

Genes selected

Accuracy (%)a

MCC^

SVM

RF

SVM

RF

All genesb

95.3

76.9

0.91

0.00

GSVA feature enrichment

98.5

76.9

0.87

0.00

sRAP

100

76.9

1.00

0.00

SVM-RFE

100

100

1.00

1.00

RF-based Positive MDA

100

76.9

1.00

0.00

T-test

100

97.0

1.00

0.91

The accuracy of the SVM predictors were obtained from LOO cross validation. SVM and RF classifiers were constructed with each set of data listed in Table 2

aAll percentages are rounded off to three significant figures

bTranscripts with a total expression of zero and/or having more than six samples with expression levels less than one were excluded

^Matthews correlation coefficient (MCC) rounded to 2 decimal places

SVM and RF-based classification of neuronal cell types with enhanced feature selection

In this study, a total of five different feature selection techniques were employed in for dimensionality reduction and they are pathway-based selection by GSVA, statistical-based selection by sRAP and T-test approaches and ML-based selection by SVM-RFE and RF-based positive MDA approaches. The number of transcripts selected by each feature elimination method can be found in Table 1. Additionally, the corresponding accuracies and MCC of each classifier are listed in Table 2.

The feature selection process decreased the number of transcripts analysed by ~60% to more than 95%. The best classifier constructed was that with features selected by SVM-RFE. This selection gave the best prediction accuracies and MCC, 100% and 1 respectively, for both the SVM and RF classifiers. GSEA has enabled scientists to identify sets or group of deregulated genes where preliminary insights to alternation of cellular mechanisms under different biological conditions can be studied [10]. Given the usefulness of such a methodology, in this paper, we explore the impact on GSEA gene selection and prediction accuracy. Classifiers built with on GSVA-enriched genes did not considerably increase the prediction accuracy of the classifiers as a mere 3.2% increase in accuracy of the SVM predictor was obtained.

Nevertheless, it is interesting to note that RF classifiers generally have a lowered level of accuracy and a poor MCC value as compared to the cognate SVM models. For example, the RF classifier constructed using RF-based Positive MDA gene selection approach have an accuracy of 76.9, ~23% lower than that of the SVM classifier built with the same data. Also, the SVM classifier produces a perfect predictor (MCC = 1) while that of the RF classifier performs no better than random prediction (MCC = 0). This observation could be an indication of the short-coming of tree-based ML methods to build high quality classifiers with single-cell RNA-seq expression data.

Network-level differences between NPCs and neuronal cells and their biological relevance in neuronal development

Gene regulatory networks (GRNs) among these transcripts inferred from RNA-seq expression profiles of SVM-RFE genes are useful in the investigation of system-level differences between the two cell types. Hub-genes (genes/transcripts that have a large number of regulatory interactions with other genes/transcripts) identified in GRNs might play potentially key roles in the maintenance of a particular cellular state. Thus, “differential hub genes (DHGs)” that are hub-genes/transcripts in neuronal cells (or NPCs) but not hub-genes in NPCs (or neuronal cells) could have important roles to differentiate the two cell types.

In order to investigate network-level difference (eg, DHGs) between the two cell types, GRNs were inferred (see Methods for more details) in neuronal cells (see Fig. 3a), NPCs (see Fig. 3b) from RNA-seq expression profiles and the structure of the two GRNs were subsequently compared (Fig. 3c). As observed, a large number of regulatory interactions are activated in one cell type but are not activated in the other cell type (blue links represents regulatory interactions activated in NPCs but not in neuronal cells, while red links represents those activated in neuronal cells but not in NPCs). For example, several interactions of the Homeobox protein orthopedia (OTP) gene (red-colored node in Fig. 3c) are activated in neuronal cells but not in NPCs and this is indicative that OTP gene is a potentially important gene which is possibly regulated in neuronal cells but not in NPCs.
Fig. 3

GRN in NPCs (a) neuronal cells (b) and differential GRN between the two cell types. a GRN in NPCs. Nodes represent transcripts, while links between two nodes represent regulatory interactions between two transcripts in NPCs. Gene regulatory interactions with high confidence score (confidence score > 0.75) in NPCs are shown in the diagram. b GRN in neuronal cells. Nodes represent transcripts, while links between two nodes represent regulatory interactions between two transcripts in neuronal cells. Gene regulatory interactions with high confidence score (confidence score > 0.75) in neuronal cells are shown in the diagram. c Differential GRN between two cell lines. Nodes are transcripts. Red links represent gene regulatory interactions that are activated in neuronal cells but not activated in NPCs, while blue links represents those activated in NPCs but not in neuronal cells. In this diagram, we assumed that a regulatory interaction is activated in neuronal cells (or NPCs) but is not activated in NPCs (or neuronal cells), if difference in confidence score between the two cell types is greater than 0.75, eg, an interaction whose confidence scores are 0.99 and 0.20 in neuronal cells and NPCs, respectively. Note that OTP is a representative DHG (see Table 3) and thus the gene is highlighted in red

In order to identify DHGs between the two cell types, we used a representative network metric, “degree”, which is defined as the number of links to the transcript. For weighted network, d i , degree of a gene i is defined as, d j  = ∑ j = 1 N W i,j , where N is number of transcripts in a GRN and w i,j is weight (in this study, we used confidence score for a link as weight) for a regulatory interaction between two genes i and j. If a transcript have high-degree in a cell type, such transcript is defined as hub genes/transcripts in the cell type. In order to identify DHGs, for each of genes, we calculated difference in degrees between two cell types. For example, degree of MRPS36 in neuronal cells and that in NPCs are 57.08 and 33.99 respectively, and the degree difference of MPS is 23.09 (= |57.08 – 33.99|). Then, we ranked the transcripts according to their degree difference (Table 3). In Table 3, highly ranked transcripts (transcripts with high degree difference) are DHGs and may play an important role in differentiating neuronal cells from NPCs.
Table 3

Degree of difference between neuronal cells and NPCs in different genes

Genes

Degree in neuronal cellsa

Degree in NPCsa

Degree difference between neuronal cells and NPCsa

MRPS36

57.1

34.0

23.1

RP11_4K3

23.0

44.0

21.0

SENP5

19.9

35.7

15.8

CLCNKB

23.9

39.6

15.7

POLR2F

23.7

39.2

15.4

OTP

48.3

33.1

15.2

RP1_58B11_1

24.6

39.3

14.7

RP11_293M10_2

32.4

45.9

13.5

RP3_465N24_6

25.6

39.1

13.5

SNORA77

30.0

43.2

13.2

C3orf65

50.8

38.0

12.8

ANGPTL7

49.7

37.9

11.8

RP4_580O19_2

47.2

36.9

10.2

RNU4_27P

45.1

35.0

10.0

COL11A1

25.8

35.3

9.46

RP11_68I18_10

47.9

39.3

8.67

Y_RNA

44.1

35.8

8.32

THTPA

29.9

37.5

7.61

ZNF44

45.7

38.6

7.11

CTH

28.9

35.9

7.02

RP11_692M12_4

42.8

35.8

7.02

RP11_345P4_7

32.0

38.8

6.82

RP13_614K11_2

36.5

43.3

6.81

MIR4417

30.7

37.2

6.55

RP11_223J15_2

36.7

42.5

5.77

RNU6_1330P

47.1

41.4

5.67

AC004893_11

45.1

39.9

5.23

RP3_406A7_5

33.4

37.5

4.06

MIR378F

39.1

35.5

3.63

PNRC2

33.7

30.6

3.15

RP5_886K2_3

43.5

40.6

2.89

RP5_857K21_5

34.9

33.2

1.61

PCID2

35.8

37.3

1.47

MST1L

38.5

37.4

1.00

RP4_749H3_1

39.5

38.9

0.624

RPL18AP2

36.0

36.6

0.573

DHDDS

41.1

40.7

0.414

a Degree of difference is corrected to three significant figures

Among the top ranked DHGs, we identified two potential key transcripts (the mitochondrial ribosomal protein S36 (MRPS36) and OTP) which could be responsible for the differentiation of NPCs from neuronal cells. MRPS36 is reported to be important in the maintenance of an undifferentiated state as overexpression of MRPS36 retards cell proliferation and delays cell cycle, helping cells maintain their undifferentiated state [11]. Similarly, the protein OTP is reportedly expressed in the hypothalamus during mammalian embryonic brain development and is a key determinant underlying FezI and Pac1-mediated of hypothalamic neural differentiation [12, 13]. These results indicate that the putative transcripts present within the top ranked DHGs could act as candidate targets for further experimental validation of their role in neuronal development. It is pertinent to note that as shown in Table 3, several of the SVM-RFE genes did not have significant difference in network property (degree) between the two cell types. This can signify that at the network level, all the SVM-RFE genes (DHGs) may not play biologically relevant role in differentiating the two cell types.

Discussion

The advancement of high throughput sequencing technologies has brought about an unprecedented ability for scientists to analyze the highly complex eukaryotic transcriptome by RNA-Seq. As compared to its predecessors, RNA-Seq has a very high signal-to-noise ratio and very large dynamic range. Reproducibility of RNA-Seq sequencing is also high and is able to provide high correlation across biological and technical replicates [1416]. Further, single-cell RNA-seq techniques were developed, allowing finer insights to be elucidated with respect to the dynamics of disparate cellular differentiation, responses to stimulation and the stochastic nature of transcription within individual cells within a tissue or a tumour. Though it is still expensive to carry out RNA-seq sequencing in the current paradigm, it is expected that the sequencing cost will significantly decrease within the next few years [17].

In view of the impending information overflow, there is a concurrent need to develop more efficient techniques for the analysis such big data, especially for the construction of predictive models which can aid the identification and classification of different cell types as described in this paper. This work is one of the first to analyse single-cell RNA-seq profiles for the construction of predictive classifiers for neuronal cells and NPC. Also, classification accuracy of different models, built with features selected by different methods (ML based, GSEA or traditional DE genes based methods), have also been critically assessed. Further, we integrated the classification results with a network inference pipeline to infer potential regulatory network amongst genes/transcripts (GRN) and identify network signatures for specific cell types.

Four key insights were obtained from this piece of work. First, transcripts selected by ML algorithm build better classifiers with enhanced accuracy, up to 100%, as compared to DE genes selected by traditional methods (sRAP and GSVA). Second, models built with differentially expressed transcripts selected from biological pathway-based methods (GSVA) proved to be inferior to that of models built with highly deregulated genes identified by traditional statistical means (sRAP and T-test). While pathway-based techniques are able to lend biological relevance to selected genes, genes selected by such methodology might not be able to capture gene expression signature of the disparate cell types studied. Thirdly, accuracy differs between classifiers built by different ML algorithms where RF, as compared to SVM, is unable to produce high accuracy for the high dimensional data analysed in this paper. Finally, the system-level analysis of the set optimal transcripts, using GRN inference analysis, is useful in the identification of hub transcripts which defines the different cell types investigated. Candidate transcripts identified by GRNs have potential biological correlates which are important in providing biological insights to cellular development. It is believed that such a workflow is extensible and applicable to other single-cell RNA-seq expression profiles like that of the study of the cancer progression within the highly heterogeneous cancer cells within a tumour [18].

Conclusion

The advancement in sequencing technologies have always brought along immense computational challenges to accurately and rapidly analyse the large amount of data generated from such experiments. Single-cell sequencing will inevitably become the gold standard for the study of genetic/transcriptomic aberrations, thus, concurrent efforts need to be placed in parallel to devise computational pipelines which can effectively analyse such big data. In addition to the use of legacy algorithms passed down for the era of microarray data analysis, there is a need to inject novelty and creativity in the analysis of single-cell RNA-seq data. This can be achieved using the combination of machine learning algorithms like SVM and RF and network reconstruction algorithms as reported in this paper. We have demonstrated that predictors built from transcripts selected using machine-learning based feature selection techniques which outperforms the commonly used statistical techniques or geneset-based approaches. Also, the novel incorporation of network reconstruction techniques have led to the identification of existing interactions and also potentially new interaction networks are identified which can be further validated in a smaller number of wet lab experiments as candidate biomarker genes. We believe that such a pipeline is extensible to other single-cell RNA-seq datasets, including those of tumor samples where the intricate transcriptomic complexity of the highly heterogeneous tumor can be unravelled for the design of personalized treatment for individual patient.

Methods

Data preparation for Single-cell RNA-Seq

Single-cell RNA gene expression profiles of neural cells from Pollen et al. [19] were used for this study as training data for the SVM/RF classifiers and will be called the “Fluidigm neural dataset” in this study. The data contained expression profiles of four neuronal cell populations, 65 samples in total, including (i) neural non-progenitor cells (NPCs), (ii) cells from the germinal zone of human cortex at gestational week (GW) 16 (GW16), (iii) 21 (GW21) and (iv) a subset of cells at GW21 which were furthered cultured for 3 weeks (GW21 + 3). Raw reads, obtained from the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) [20] under accession number SRP041736 [3], were mapped to the reference genome using Tophat2 (v2.1.0) [21] and were subjected to Fragments per Kilobase of Exon per Million Fragments Mapped (FPKM) normalization, using cuffdiff (v2.2.1) [22], prior to downstream analysis. Expression levels were logged prior to filtering. Genes having log FPKM of greater than one in more than six cells were included in the analyses. Samples were assigned labels of NPCs and those of the neuronal cells (inclusive of expression data obtained from cells of GW16, GW21 and GW21 + 3).

Construction of support vector machine (SVM) and random forest (RF) predictive models for the identification of fetal neocortical neuronal cells from NPCs

Classification accuracies of each disparate dataset were explored using two different machine learning algorithms - Support Vector Machine (SVM) and Random Forest (RF).

The SVM predictors were built with the LIBSVM package [23] and the RF predictors were built with the randomForest package in the R programming environment [24].

The detailed methodology for the construction of a SVM classifier can be obtained from the article by Burges [25] and a brief description of the SVM algorithm from Wee et al. [26]. Briefly, the SVM algorithm is based on the structural risk minimization principle from statistical learning theory [27]. A set of positive (single-cell RNA-Seq neocortical transcription data) and negative (single-cell RNA-Seq NPC transcription data) examples were represented by the feature vectors x i (i = 1, 2,....N) with corresponding labels y i {+1,-1}. To classify the data as NPCs or neuronal cells, the SVM trains a classifier by mapping the input samples, using a kernel function (radial basis function (RBF) in this study), onto a high- dimensional space, and then seeking a separating hyperplane that differentiates the two classes with maximal margin and minimal error. Parameter optimization was carried out for g, which determines the capacity of the RBF kernel, and the regularization parameter C using leave-one-out (LOO) cross-validation. The optimal g and C values obtained from the optimization processes were used subsequently for training the entire training set to create the final SVM classifier.

RF is a tree-based classifier where classification is carried out by aggregating the votes for all trees built from different subsamples, randomly selected, with replacement, within the training set, from the training dataset. As the classifier is built by aggregating a large number of different decision trees, predictors built with the random forests algorithm is expected to have low variance and low bias. The number of trees (T) was set to 20,000 and the number of features to consider at each split in the decision tree (m) obtained from the optimization processes were used subsequently for training the entire training set to create the final RF classifier [28, 29].

Feature extraction and dimensionality reduction

Additionally, dimensionality reduction was carried out to obtain optimal subsets of gene/features for classifier construction and they are as listed below.
  1. (i)

    Selection of genes from deregulated pathways using geneset enrichment analysis (GSEA). A non-parametric, unsupervised G was carried out with the Gene Set Variation Analysis (GSVA) package [30] in the R programming environment [24]. The original ensemble gene (ENSG) identifiers were mapped to their cognate HUGO Gene Nomenclature Committee (HGNC)/Uniprot identifiers using the biomaRt package in R [31]. This was carried out in order to permit the mapping of genes to that of the curated C2 geneset (September 2014), obtained from the Broad Institute’s Molecular Signatures database version 4.0 (MSigDB) [10], for gene set analysis. Manual curation of the HGNC/Uniprot identifiers was subsequently carried out to obtain a curated list of identifiers. Genes with ENSG codes that were not matched to any symbols were removed. Also, gene fragments sharing the same symbol were excluded from analysis. The identities of the up and down-regulated pathways (p-value < 0.005), together with the corresponding genes within these genesets, were identified and reported.

     
  2. (ii)

    Selection of differentially expressed (DE) genes using the R package Simplified RNA-Seq Analysis Pipeline (sRAP) [32, 33].

     
  3. (iii)

    Selection of a subset of genes with the highest ranking criterion based on SVM-based classification [34] (SVM-RFE) using the R package pathClass [35]. SVM-RFE is an iterative gene selection process where features, expression values of different genes obtained from single-cell RNAseq experiments, with the smallest ranking criterion are recursively removed when the ranking criterion for all features are computed from the SVM-classifiers.

     
  4. (iv)

    Selection of genes with positive mean decrease in accuracy (MDA) from RF analyses where selected feature genes are deemed to reduce classification error.

     
  5. (v)

    Selection of DE genes using two-tailed T-test based analysis using R package limma [36] (p-value < 0.05).

     

Evaluation of model performance

A set of statistical variables were established to evaluate the performance, Accuracy and Matthews correlation coefficient (MCC) [37], of the SVM and RF classifiers. Only LOO cross validation was carried out for the SVM classifiers.

Inference of gene regulatory networks in neuronal cells and NPCs

A plethora of network-inference algorithms are now available and have been used to infer GRNs from gene expression datasets. As mentioned in Marbach et al. [38] and Hase et al. [39], different network-inference algorithms have different strength and weakness and complement with each other. Thus, by integrating heterogeneous network-inference algorithms, we can take advantage of their strengths to recover high-quality gene regulatory networks [38, 39].

Therefore, in this study, we selected 14 representative network-inference algorithms that are based on heterogeneous statistical techniques and integrated results from the selected algorithms. The selected algorithms includes six mutual information based methods (ARACNE [40], CLR [41], MRNET [42], RELNET [43], C3NET [44], and BC3NET [45]), two correlation based method (Spearman’s correlation and Pearson’s correlation [43]), one Bayesian network based method (SiGN-BN [46]), two random forest based method (GENIE3 [47] with two different parameter settings, see Table 4 for the details), two regression based method (TIGRESS [48] with two different parameter settings, see Table 4 for the details), and one method with both of ordinary differential equation based recursive optimization and mutual information (NARROMI [49]). The set of 14 algorithms includes several high-performance algorithms, ie, GENIE3 is the winner of both of DREAM4 (DREAM, Dialogue on Reverse Engineering Assessment and Methods) and DREAM5 network inference challenges [38, 47], while, in DREAM5, TIGRESS and CLR (and MRNET) are the best performer among regression techniques and that among mutual information techniques, respectively [38]. Zhang et al. demonstrated that NARROMI outperforms GENIE3 [49].
Table 4

Parameter used to optimize each network-inference algorithms

Network-inference algorithms

Parameter optimization settingc

GENIE3-Aa

K = “all”, nb.trees = 10,000

GENIE3-Ba

K = “sqrt”, nb.trees = 10,000

TIGRESS-Ab

scoring = “area”

TIGRESS-Bb

scoring = “max”

ARACNE

eps = 0.1

BC3NET

boot = 10, alpha1 = 0.99, alpha2 = 0.99

SiGN-BN

Number of iteration of bootstrap method = 1,000

aGENIE3-A and -B represent two different parameter settings for GENIE3 algorithm used in this study

bTIGRESS-A and -B represent two different parameter settings for TIGRESS algorithm used in this study

cWe used default settings for parameters that are not shown in this table

Integration of results from multiple network-inference algorithms

In this study, we have used a computational framework, “Top1net”, to integrate results from the selected 14 network-inference algorithms [39].

Each of individual network-inference algorithms calculates confidence score for each gene pair and, an interaction between a gene pair with higher confidence score is more likely to be true positive interaction [39, 40, 50]. Top1net applies bagging method introduced by Breiman [51], to integrate confidence scores for each of gene pairs from multiple individual network-inference algorithms [39]. Top1net assumes that, if at least one network-inference algorithm assigns high confidence score to a gene pair, one gene in the pair has a regulatory interaction with another gene [39]. As network-inference algorithms tend to assign high confidence scores to true positive interactions, Top1net would recover a large number of true positive interactions in a GRN. The procedure of Top1Net is composed of three steps.

Step 1

From an expression dataset, an individual network-inference algorithm assigns confidence score for each of gene pairs and the gene pairs are ranked according to their confidence scores, ie, a gene pair with highest confidence score has the rank value of 1.

Step 2

We normalized ranked scores from each algorithm by scaling from 0 to 1 and used the normalized ranked scores (NRSs) as confidence scores by the algorithm. If a pair of genes i and j has rank value of g i,j by an algorithm, the NRS i,j of the gene pair by the algorithm is defined as, \( NR{S}_{i,j}=\frac{N\left(N-1\right)+1-{g}_{i,j}}{N\left(N-1\right)} \), where N represents the number of genes in the gene expression dataset.

Step 3

We integrate NRSs from the algorithms by Top1net. For example, if we used the 14 network-inference algorithms to calculate 14 NRSs for each gene pairs. For each gene pairs, Top1net used the highest NRS among 14 NRSs as the confidence score of the gene pairs. For example, if the algorithms assign 14 NRSs, 0.98, 0.85, 0.8, 0.69, 0.65, 0.63, 0.62, 0.61, 0.58, 0.55, 0.53, 0.51, 0.50 and 0.35 for the gene pair, Top1net used 0.98 as the confidence score for the interaction between the gene pair.

RNA-seq expression profiles for GRN inference

Only 37 genes identified by SVM-RFE method were used for the inference of gene regulatory interactions within neuronal cells and NPCs as a single gene, RP4_803A2_1, was excluded for having expression values of 0 across all NPC samples.

Packages and parameters for individual network inference algorithms

To infer GRNs by individual algorithms, we used MINET package [52] for ARACNE, CLR, MRNET and RELNET, c3net packages for C3NET, bc3net packages for BC3NET, source code obtained from http://www.montefiore.ulg.ac.be/~huynh-thu/software.html for GENIE3, source code obtained from GP-DREAM network inference website (http://dream.broadinstitute.org/) for TIGRESS, and source code from http://comp-sysbio.org/narromi.htm for NARROMI. For SiGN-BN [28], we used software on the super-computing resource that was provided by Human Genome Center, the Institute of Medical Science, and the University of Tokyo. For PCC and SCC, we used R function (“cor” function) to calculate Pearson’s and Spearman’s correlation coefficient.

Nine, out of the 14, algorithms required optimization and the parameter settings for the nine network-inference algorithms are shown in Table 1. More information on algorithm customization can be found in references [40, 4549] and also within the manuals written for the individual algorithms.

Declarations

Acknowledgements

The authors would like to express their sincere gratitude to all individuals who have helped in this paper.

Declaration

This article has been published as part of BMC Genomics Volume 17 Supplement 13, 2016: 15th International Conference On Bioinformatics (INCOB 2016). The full contents of the supplement are available online at https://bmcgenet.biomedcentral.com/articles/supplements/volume-17-supplement-13.

Funding

Publication of this article was funded by the Agency for Science, Technology and Research (A*STAR) Joint Council (JCO) Project grant, EC-2013-063. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Availability of data and materials

Single-cell RNA gene expression profiles of neural cells from Pollen et al. [19] were used for this study and raw reads can be obtained from the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) [20] under accession number SRP041736 [3].

Authors’ contributions

Supervised the research: SP, HK, SKN, SG and LJKW. Conceived the experiments: SG, LJKW. Designed the experiments and analyses: YH, TH. Performed the experiments: YH, TH, HPL. Analyzed the data: YH, TH. Wrote the paper: YH, TH. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute for Infocomm Research, A*STAR
(2)
The Systems Biology Institute
(3)
Computational and Systems Biology, Genome Institute of Singapore, A*STAR
(4)
The Systems Biology Institute, Singapore Node hosted at the Institute for Infocomm Research, A*STAR

References

  1. Suzuki A, Matsushima K, Makinoshima H, Sugano S, Kohno T, Tsuchihara K, et al. Single-cell analysis of lung adenocarcinoma cell lines reveals diverse expression patterns of individual cells invoked by a molecular target drug treatment. Genome Biol. 2015;16:66. doi:10.1186/s13059-015-0636-y. PubMed PMID: 25887790, PubMed Central PMCID: PMC4450998.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Kim KT, Lee HW, Lee HO, Kim SC, Seo YJ, Chung W, et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 2015;16:127. doi:10.1186/s13059-015-0692-3. PubMed PMID: 26084335, PubMed Central PMCID: PMC4506401.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Cestarelli V, Fiscon G, Felici G, Bertolazzi P, Weitschek E. CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules. Bioinformatics. 2015;32(5):697–704. doi:10.1093/bioinformatics/btv635.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Yao F, Zhang C, Du W, Liu C, Xu Y. Identification of gene-expression signatures and protein markers for breast cancer grading and staging. PLoS One. 2015;10(9):e0138213. doi:10.1371/journal.pone.0138213. PubMed PMID: 26375396, PubMed Central PMCID: PMC4573873.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Chen L, Xuan J, Riggins RB, Clarke R, Wang Y. Identifying cancer biomarkers by network-constrained support vector machines. BMC Syst Biol. 2011;5:161. doi:10.1186/1752-0509-5-161. PubMed PMID: 21992556, PubMed Central PMCID: PMC3214162.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Sundaramurthy G, Eghbalnia HR. A probabilistic approach for automated discovery of perturbed genes using expression data from microarray or RNA-Seq. Comput Biol Med. 2015;67:29–40. doi:10.1016/j.compbiomed.2015.07.029.View ArticlePubMedGoogle Scholar
  7. Vidal M, Cusick ME, Barabasi AL. Interactome networks and human disease. Cell. 2011;144(6):986–98. doi:10.1016/j.cell.2011.02.016. PubMed PMID: 21414488, PubMed Central PMCID: PMC3102045.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Ahmad FK, Deris S, Othman NH. The inference of breast cancer metastasis through gene regulatory networks. J Biomed Inform. 2012;45(2):350–62. doi:10.1016/j.jbi.2011.11.015.View ArticlePubMedGoogle Scholar
  9. Suzuki R, Shimodaira H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics. 2006;22(12):1540–2. doi:10.1093/bioinformatics/btl117.View ArticlePubMedGoogle Scholar
  10. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50. doi:10.1073/pnas.0506580102. PubMed PMID: 16199517, PubMed Central PMCID: PMC1239896.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Chen YC, Chang MY, Shiau AL, Yo YT, Wu CL. Mitochondrial ribosomal protein S36 delays cell cycle progression in association with p53 modification and p21(WAF1/CIP1) expression. J Cell Biochem. 2007;100(4):981–90. doi:10.1002/jcb.21079.View ArticlePubMedGoogle Scholar
  12. Kaji T, Nonogaki K. Role of homeobox genes in the hypothalamic development and energy balance. Front Biosci (Landmark Ed). 2013;18:740–7.View ArticleGoogle Scholar
  13. Blechman J, Borodovsky N, Eisenberg M, Nabel-Rosen H, Grimm J, Levkowitz G. Specification of hypothalamic neurons by dual regulation of the homeodomain protein Orthopedia. Development. 2007;134(24):4417–26. doi:10.1242/dev.011262.View ArticlePubMedGoogle Scholar
  14. Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods. 2008;5(7):613–9. doi:10.1038/nmeth.1223.View ArticlePubMedGoogle Scholar
  15. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320(5881):1344–9. doi:10.1126/science.1158441. PubMed PMID: 18451266, PubMed Central PMCID: PMC2951732.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8. doi:10.1038/nmeth.1226.View ArticlePubMedGoogle Scholar
  17. Nagalakshmi U, Waern K, Snyder M. RNA-Seq: a method for comprehensive transcriptome analysis. Curr Protoc Mol Biology/edited by Frederick M Ausubel [et al.]. 2010;Chapter 4:Unit 4 11. 1–3. doi:10.1002/0471142727.mb0411s89.
  18. Hou Y, Fan W, Yan L, Li R, Lian Y, Huang J, et al. Genome analyses of single human oocytes. Cell. 2013;155(7):1492–506. doi:10.1016/j.cell.2013.11.040.View ArticlePubMedGoogle Scholar
  19. Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014;32(10):1053–8. doi:10.1038/nbt.2967. PubMed PMID: 25086649, PubMed Central PMCID: PMC4191988.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Kodama Y, Shumway M, Leinonen R. International nucleotide sequence database C. The sequence read archive: explosive growth of sequencing data. Nucleic Acids Res. 2012;40(Database issue):D54–6. doi:10.1093/nar/gkr854. PubMed PMID: 22009675, PubMed Central PMCID: PMC3245110.View ArticlePubMedGoogle Scholar
  21. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14(4):R36. doi:10.1186/gb-2013-14-4-r36. PubMed PMID: 23618408, PubMed Central PMCID: PMC4053844.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28(5):511–5. doi:10.1038/nbt.1621. PubMed PMID: 20436464, PubMed Central PMCID: PMC3146043.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Chang C-C, Lin C-J. LIBSVM : a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):27.View ArticleGoogle Scholar
  24. R_Core_Team. A Language and Environment for Statistical Computing Vienna. Austria: R Foundation for Statistical Computing; 2015 [cited 2015]. Available from: http://www.r-project.org/.
  25. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998;2(2):121–67. doi:10.1023/a:1009715923555.View ArticleGoogle Scholar
  26. Wee LJ, Simarmata D, Kam YW, Ng LF, Tong JC. SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction. BMC Genomics. 2010;11 Suppl 4:S21. doi:10.1186/1471-2164-11-S4-S21. PubMed PMID: 21143805; PubMed Central PMCID: PMC3005920.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–397.Google Scholar
  28. Breiman L. Random forests. Mach Learn. 2001;45:5–32.View ArticleGoogle Scholar
  29. Treeratpituk P, Giles CL. Disambiguating Authors in academic publications using random forests. In: JCDL ′09 Proceedings of the 9th ACM/IEEE-CS joint conference. 2009. p. 39–48. doi:10.1145/1555400.1555408.Google Scholar
  30. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. doi:10.1186/1471-2105-14-7. PubMed PMID: 23323831, PubMed Central PMCID: PMC3618321.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005;21(16):3439–40. doi:10.1093/bioinformatics/bti525.View ArticlePubMedGoogle Scholar
  32. Warden CD, Kanaya N, Chen S, Yuan YC. BD-Func: a streamlined algorithm for predicting activation and inhibition of pathways. PeerJ. 2013;1:e159. doi:10.7717/peerj.159. PubMed PMID: 24058887, PubMed Central PMCID: PMC3775632.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Warden CD, Yuan Y-C, Wu X. Optimal calculation of RNA-Seq fold-change values. Int J Comput Bioinformatics In Silico Model. 2013;2(6):285–92.Google Scholar
  34. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46:389–422.View ArticleGoogle Scholar
  35. Johannes M, Frohlich H, Sultmann H, Beissbarth T. pathClass: an R-package for integration of pathway knowledge into support vector machines for biomarker discovery. Bioinformatics. 2011;27(10):1442–3. doi:10.1093/bioinformatics/btr157.View ArticlePubMedGoogle Scholar
  36. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi:10.1093/nar/gkv007. PubMed PMID: 25605792; PubMed Central PMCID: PMC4402510.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta. 1975;405(2):442–51.View ArticlePubMedGoogle Scholar
  38. Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ, Camacho DM, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012;9(8):796–804. doi:10.1038/nmeth.2016. PubMed PMID: 22796662, PubMed Central PMCID: PMC3512113.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Hase T, Ghosh S, Yamanaka R, Kitano H. Harnessing diversity towards the reconstructing of large scale gene regulatory networks. PLoS Comput Biol. 2013;9(11):e1003361. doi:10.1371/journal.pcbi.1003361. PubMed PMID: 24278007, PubMed Central PMCID: PMC3836705.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006;7 Suppl 1:S7. doi:10.1186/1471-2105-7-S1-S7. PubMed PMID: 16723010; PubMed Central PMCID: PMC1810318.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007;5(1):e8. doi:10.1371/journal.pbio.0050008. PubMed PMID: 17214507, PubMed Central PMCID: PMC1764438.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Meyer PE, Kontos K, Lafitte F, Bontempi G. Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinform Syst Biol. 2007:79879. doi:10.1155/2007/79879. PubMed PMID: 18354736; PubMed Central PMCID: PMC3171353.
  43. Butte AJ, Kohane IS. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000:418–29.Google Scholar
  44. Altay G, Emmert-Streib F. Inferring the conservative causal core of gene regulatory networks. BMC Syst Biol. 2010;4:132. doi:10.1186/1752-0509-4-132. PubMed PMID: 20920161, PubMed Central PMCID: PMC2955605.View ArticlePubMedPubMed CentralGoogle Scholar
  45. de Matos SR, Emmert-Streib F. Bagging statistical network inference from large-scale gene expression data. PLoS One. 2012;7(3):e33624. doi:10.1371/journal.pone.0033624. PubMed PMID: 22479422, PubMed Central PMCID: PMC3316596.View ArticleGoogle Scholar
  46. Tamada Y, Shimamura T, Yamaguchi R, Imoto S, Nagasaki M, Miyano S. Sign: large-scale gene network estimation environment for high performance computing. Genome Inform. 2011;25(1):40–52.PubMedGoogle Scholar
  47. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P. Inferring regulatory networks from expression data using tree-based methods. PLoS One. 2010;5(9). doi:10.1371/journal.pone.0012776. PubMed PMID: 20927193; PubMed Central PMCID: PMC2946910.
  48. Haury AC, Mordelet F, Vera-Licona P, Vert JP. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Syst Biol. 2012;6:145. doi:10.1186/1752-0509-6-145. PubMed PMID: 23173819, PubMed Central PMCID: PMC3598250.View ArticlePubMedPubMed CentralGoogle Scholar
  49. Zhang X, Liu K, Liu ZP, Duval B, Richer JM, Zhao XM, et al. NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics. 2013;29(1):106–13. doi:10.1093/bioinformatics/bts619.View ArticlePubMedGoogle Scholar
  50. Altay G, Emmert-Streib F. Revealing differences in gene network inference algorithms on the network level by ensemble methods. Bioinformatics. 2010;26(14):1738–44. doi:10.1093/bioinformatics/btq259.View ArticlePubMedGoogle Scholar
  51. Breiman L. Bagging predictors. Mach Learn. 1996;24:123–40.Google Scholar
  52. Meyer PE, Lafitte F, Bontempi G. minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics. 2008;9:461. doi:10.1186/1471-2105-9-461. PubMed PMID: 18959772; PubMed Central PMCID: PMC2630331.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2016

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