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Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

Abstract

Background

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis.

Results

The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.

Conclusions

MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

Background

RNA-seq is commonly used to obtain genome-wide expression data for genes [1, 2] and to identify differentially expressed genes (DEGs) for different groups or conditions [3, 4]. To date, several methods to enable the analysis of RNA-seq data have been developed, including normalization [5,6,7,8,9,10], various R packages [11,12,13,14,15,16], and graphical user interfaces (GUI) [17,18,19]. Research on more efficient and accurate methods to identify DEGs continues, and new findings continue to be reported by researchers [20,21,22,23,24,25].

Obtaining a ranked gene list for the degree of differential expression (DE) is a starting point for gaining biological insights into the groups being compared [26]. Several analysis methods such as gene ontology and the construction of co-expression networks have been used to examine the biological mechanisms underlying DEGs [25]. Additionally, gene clustering based on the similarity of expression patterns has been widely used to group and classify DEGs [27,28,29]. Gene clustering has also been used for time-course and multi-group data in which several expression patterns may exist. However, the method is usually not used to classify DEGs obtained by two-group comparisons which is the minimum size for a comparative analysis and is used even less to identify DEGs.

In this study, we propose the use of model-based clustering algorithms for the identification of DEGs from RNA-seq count data. Although various sophisticated algorithms for gene clustering are available [30,31,32], we focus our analysis on the R package MBCluster.Seq [28], as its framework is compatible with DE analysis and it is comparable with other R packages dedicated to detecting DEGs. We describe the application of the model-based gene clustering method and the incorporation of a robust normalization algorithm called DEGES [7, 14] for detecting DEGs. Next, we compare the method with other competing packages dedicated to DE analysis such as edgeR [11], DESeq2 [15], and TCC [14] using simulated data and real data. Further, we discuss the potential limitations of the method and present a set of guidelines for practical use.

Results

Proposed method for identification of DEGs based on gene clustering

In this study, we devised a new analysis method that uses the functionalities of the MBCluster.Seq package. Therefore, for consistency and clarity, we used notations that were similar to those described in the original paper [28]. First, we outlined a method to describe the input/output relationship. We denote an input count matrix as one where each row indicates a gene g (= 1, …, G), each column a replicate j (= 1, …, ni) of group i (= 1, …, I), and each cell the number of counts. Here, G is the number of genes, I is the number of compared groups, and ni is the number of replicates for the group i. MBCluster.Seq clusters gene vectors βg = (βg1, …, βgI), where βgi indicates the count of gene g in the group i relative to the overall mean on a log-scale. Therefore, the summation of βgi for a gene g across all compared groups was 0. βg can be described as the log fold-change (FC) between compared groups. Given a preselected number of clusters K, MBCluster.Seq gives two results as an output. One is the center for cluster k, μk = (μk1, …, μkI) for k = 1, …, K, and the other is the posterior probability (PP) that gene g belongs to the kth cluster, pg = (pg1, …, pgK) for g = 1, …, G.

A ranked gene list for a cluster mainly consisting of non-DEGs can be obtained via MBCluster.Seq by using the assigned PPs. To identify the non-DEG cluster, we consider the L2 Norm of μk for each cluster center across groups, ||μk||2 = (|μk1|2 +  +|μkI|2)1/2. A smaller value of the norm for cluster k indicates a smaller degree of DE across groups for the cluster. Accordingly, we can regard the probability of a gene being located in the kth column as that in the non-DEG cluster, where k = argmin(||μ1||2, …, ||μK||2). A lower value of the PP for gene g in the kth cluster (i.e., pgk) indicates a higher degree of DE between the compared groups. For simplicity, we call the proposed method based on MBCluster.Seq as “MBCdeg”.

MBCluster.Seq employs a scaling normalization algorithm [33] as the default, where the upper quartile (i.e., 75th percentile) counts for individual samples are used as the scaling factors to obtain equal values for upper quartile counts across all samples after the normalization. This type of conventional normalization algorithm assumes that the proportion of DEGs (PDEG) in the data is small (e.g., less than 5%) or that the proportion of genes upregulated in individual groups in the DEGs is approximately balanced (i.e., P1P2 when comparing groups 1 and 2; P1 + P2 = 1) [34,35,36]. However, in practice, these assumptions are invalid in certain cases, such as when PDEG ≈ 60% [37] and P1 >  > P2 [38]. To investigate the effect of various normalization algorithms on the data analysis, we employed a competing method (called DEGES) [7, 14]. The main arguments in its favor are (1) DEGES was originally designed to manipulate such scenarios (~ 25% of PDEG with P1 >  > P2), (2) it can be applied to ~ 60% of PDEG [35], and (3) the output (i.e., normalization factors) can easily be applied to the framework of MBCluster.Seq. Henceforth, we refer to MBCdeg using the default normalization algorithm and with the DEGES normalization algorithm as “MBCdeg1” and “MBCdeg2”, respectively.

Analysis of simulated data for a two-group comparison

We show an example of MBCdeg based analysis for simulated data for two groups: G = 10,000, n1 = n2 = 3, PDEG = 0.25, P1 = 0.9 (or P2 = 0.1), and FC = 4. We performed a run for MBCdeg with K = 3, assuming three expression patterns: up-regulated in group 1 (“DEG1” pattern), up-regulated in group 2 (“DEG2”), and consistent in both groups (“non-DEG”). Using this approach, we ideally obtain the centers for the cluster k (= 1, 2, 3) as μ1 = (0.69, − 0.69), μ2 = (− 0.69, 0.69), and μ3 = (0.00, 0.00), with loge(2) = 0.69. Clearly, the third cluster has the smallest L2 Norm (i.e., ||μ3||2 = 0). We therefore regard this cluster as containing many non-DEGs and used the PPs assigned to this cluster for estimating the overall degree of DE.

Here, MBCdeg1 gives the output μ1 = (0.56, − 0.56), μ2 = (− 0.14, 0.14), and μ3 = (− 0.81, 0.81) and uses the PPs of the second cluster displaying the smallest norm (||μ2||2 = 0.195) for gene ranking. Meanwhile, MBCdeg2 outputs μ1 = (0.68, − 0.68), μ2 = (− 0.69, 0.69), and μ3 = (− 0.02, 0.02) and uses the PPs of the third cluster displaying the smallest norm (||μ3||2 = 0.027) for gene ranking. The expression patterns for the cluster centers from MBCdeg2 were closer to the ideal patterns than those from MBCdeg1. This result suggests that the DEGES normalization may be useful in the framework for MBCdeg, at least in similar scenarios.

Next, we evaluated the performance of the overall gene ranking using the area under the receiver operating characteristic (ROC) curve (i.e., AUC). The AUC enables data comparisons without a tradeoff in sensitivity and specificity because the ROC curve is created by plotting the true positive rate (i.e., sensitivity) against the false positive rate (1 − specificity) obtained for each possible threshold value [39,40,41]. Therefore, an accurate method should provide high AUC values. We compared five methods: two clustering-based methods (MBCdeg1 and MBCdeg2) and three conventional methods (edgeR [11], DESeq2 [15], and TCC [14]). The edgeR and DESeq2 packages are widely used [25]. TCC is used as the main alternative to MBCdeg as it utilizes the DEGES normalization algorithm.

Figure 1 shows the AUC values for the five methods using various simulations: PDEG = 0.05 and 0.25, with P1 = 0.5, 0.7, 0.9, and 1.0. A higher P1 value indicates a higher degree of upregulated DEGs in group 1, ranging from symmetric (P1 = 0.5) to completely asymmetric (P1 = 1.0) conditions. The AUC values for the two MBCdeg methods were higher than those from the conventional DE methods. TCC showed the best among the three conventional methods because the simulated data was generated using this package and the DEGES normalization algorithm in TCC was originally designed to manipulate such asymmetric scenarios (i.e., P1 > 0.5). Therefore, the high performance of MBCdeg over TCC is an interesting result.

Fig. 1
figure1

Results for two-group simulated data (PDEG ≤ 0.25). Boxplots of AUC values (100 trials) for five methods for a total of eight conditions, P1 = 0.5 (left) to 1.0 (right) with PDEG = 0.05 (upper) and 0.25 (lower). The performance of MBCdeg (with K = 3) was high in most trials. The explanation for a representative trial (AUC = 0.9295) and the worst trial (AUC = 0.633) using MBCdeg2 with PDEG = 0.25 and P1 = 0.5 is given in Table 1

The performance of MBCdeg does not appear to depend on whether the simulation scenario is symmetric or asymmetric. Most of the trials of MBCdeg showed AUC values of 0.93, and the range of P1 from 0.5 to 0.9, suggesting that the framework of MBCdeg is intrinsically robust for both symmetric and asymmetric data. As indicated above, we confirmed that MBCdeg2 tends to have lower norm values for non-DEG clusters (i.e., ||μk||2 with k = argmin(||μ1||2, …, ||μK||2)) than MBCdeg1 in scenarios with P1 > 0.5. This can be explained by the use of DEGES in the MBCdeg2 algorithm.

The overall performance of MBCdeg2 was very similar to that of MBCdeg1 although the former performs better than the latter in completely asymmetric scenarios (i.e., P1 = 1.0). We hypothesized that the key to accurate gene ranking may be to identify non-DEG cluster, and not to make the ideal non-DE pattern. This trend was also observed when the number of replicates per group was increased to n1 = n2 = 6, 9, and 12 (Additional file 1). In a comparison of the performance of each method using different numbers of replicates (Fig. 1 and Additional file 1), we observed that AUC values tend to increase as the number of replicates increases and this trend is consistent with a previous report [20].

Effect on different numbers of clusters

We noted that MBCdeg tends to increase the variation in AUC between trials in association with an increase in P1 values, and this effect was greatest at P1 = 1.0. For example, the AUC values for MBCdeg1 and MBCdeg2 were less than 0.8 for ten and three trials, respectively, when PDEG = 0.05 (Fig. 1). The relatively poor performance of MBCdeg at P1 = 1.0 than with other values of P1 can be explained by the difference between the true number of clusters (i.e., Ktruth = 2) in this condition and the number of clusters that were used (K = 3). The simulated data obtained with P1 = 1.0 has two expression patterns; “DEG1” and “non-DEG”, and does not have the “DEG2” pattern.

To demonstrate the effect of using MBCdeg on a predefined number of clusters, we investigated the AUC values for MBCdeg with K = 2–4 (Fig. 2). Our results showed that the AUC values were the highest when K = Ktruth. The highest AUC values for P1 = 0.5, 0.7, 0.9, and 1.0 were obtained using K = 3, 3, 3, and 2, respectively (indicated by the downward arrow in light blue). This result suggests that the use of an accurate number of clusters is important when performing an analysis using MBCdeg. The distributions of the AUC values indicate the K values that should be used in practice. At P1 = 0.5 and 0.7 (i.e., Ktruth = 3), the results for K > Ktruth (i.e., K = 4) were more accurate than those with K < Ktruth (i.e., K = 2). Further, this trend was observed when the number of replicates per group was increased to n1 = n2 = 6, 9, and 12 (Additional file 2).

Fig. 2
figure2

Effect of the different cluster numbers for MBCdeg (K = 2–4). Boxplots of AUC values (100 trials) for MBCdeg1 (K = 2–4; colored in black) and MBCdeg2 (K = 2–4; colored in red) are shown. The remaining legends are the same as in Fig. 1. The AUC values for MBCdeg with K = 3 were similar to those in Fig. 1 (different trials were used). Note that two trials result in AUC < 0.4 (not shown): MBCdeg1 with K = 2 with PDEG = 0.05 and P1 = 0.9 (AUC = 0.3498), and MBCdeg1 with K = 4 with PDEG = 0.25 and P1 = 1.0 (AUC = 0.3893)

In practice, it may be safe to adopt a larger K value. For two-group comparisons, the user would only need to run MBCdeg with K ≥ 3. Although the frequency of trials that result in a low performance of gene ranking is relatively high at larger P1 values, the probability of obtaining extremely asymmetric results such as P1 = 1.0 is low, and may at most indicate P1 = 0.9. Therefore, both methods (MBCdeg1 and MBCdeg2) may be useful in scenarios that are similar to the conditions investigated.

Although the probability of extremely low performance at P1 ≤ 0.9 is at most 2%, we should discuss the reasons. For example, the use of MBCdeg2 with K = 3 outputs one such result (AUC = 0.634) with PDEG = 0.25 and P1 = 0.5. As this simulated data consists of 1250 DEG1, 1250 DEG2, and 7500 non-DEG patterns, almost all trials with high AUC values (> 0.92) gave three clusters, each of which consisted of many genes that resembled one of the three patterns, and having one predominant pattern (Table 1a). However, for the trial with low AUC value (0.634), the numbers of genes in the three clusters were 1299, 8670, and 31, respectively (Table 1b). Of these, the first cluster consisted of the most genes showing the DEG2 pattern, i.e., 12 DEG1, 935 DEG2, and 352 non-DEG patterns. The second cluster consisted of a great majority of genes with non-DEG patterns, i.e., 1238 DEG1, 289 DEG2, and 7143 non-DEG patterns. As this non-DEG cluster contains 1238/1250 = 99.0% of the genes with a true DEG1 pattern, these ranking inaccuracies may be responsible for the low AUC values obtained. As per our analysis, most low AUC values result due to the incorporation of DEGs (i.e., the DEG1 or DEG2 patterns) into the non-DEG clusters.

Table 1 Results for trials using MBCdeg2 with PDEG = 0.25 and P1 = 0.5

The third cluster shown in Table 1b had very few genes (i.e., 26 DEG2 and 5 non-DEGs). These results indicate that the first two clusters determine the performance of the trial. The results from using K = 2 with the same simulation conditions (i.e., PDEG = 0.25 and P1 = 0.5 in Fig. 2) indicate that the distribution of AUC values located around 0.63 may thus be reasonable. Similar inferences can be made for other results, for example, with PDEG = 0.25 and P1 = 0.7. As the simulated data consists of 1750 DEG1, 750 DEG2, and 7500 non-DEG patterns, MBCdeg with K = 2 tends to output one cluster that is mainly composed of non-DEG and DEG2 genes, with the second cluster mainly composed of DEG1 genes. The median AUC values (≈ 0.77) when using MBCdeg with K = 2 at P1 = 0.7 are higher than those (≈ 0.63) at P1 = 0.5, and this can be explained by the smaller number of true DEG patterns (750 < 1250) contained in the non-DEG cluster. As this phenomenon was observed using both MBCdeg1 and MBCdeg2, it does not result due to the differences in the normalization algorithms. MBCdeg determines clusters stochastically, and therefore an output of results that appear as outliers will be obtained with a certain probability.

Effect on different degrees of DE

For the simulation analysis described above, the degree of DE was fixed at fourfold (i.e., FC = 4), which limits the expression pattern of genes upregulated in a group to one, which is favorable when using MBCdeg. However, in practice, the degree of DE would differ for genes. Therefore, we performed simulations using different degrees of DE [7], where the FCs for DEGs were randomly sampled from “1.2 + a gamma distribution with shape = 2.0 and scale = 0.5.” Accordingly, the minimum and mean fold-change were approximately 1.2 and 2.2 (= 1.2 + 2.0 × 0.5), respectively.

The results of the analysis indicated that greater variability in the performance of MBCdeg is observed compared to the results obtained using fixed FC, as shown in Fig. 1 (Additional file 3). AUC values are lower overall than those from the fixed FC comparison (Fig. 1) in the same condition, and this can be explained by the lower degree of DE (fourfold → 2.2-fold). Though MBCdeg gave superior results than other packages (edgeR, DESeq2, and TCC) with PDEG = 0.25, it did not perform as well using PDEG = 0.05. This may occur due to the small number of DEGs (10,000 × 0.05 = 500 genes) under the conditions used for analysis and the resulting instability of clusters that mainly consisted of DEG1 or DEG2 patterns. As the total number of DEGs is relatively large with PDEG = 0.25, clusters containing more DEG1 or DEG2 patterns are more likely to result and are probably more stable.

Notably, the AUC values for MBCdeg are not always the highest when K = Ktruth (Additional file 4). With PDEG = 0.25 and P1 = 0.9, the AUC values tend to increase as the number of clusters K increases. This may occur because the simulated data contains more expression patterns than Ktruth. In the conditions used here, Ktruth = 3 was used for convenience. However, genes upregulated in a group may show various degrees of DE, such as 2.1-fold and 2.7-fold, and allowing the formation of separate clusters would have reduced the possibility of the inclusion of these genes in the non-DEG cluster.

Effect on larger PDEG values

The results described above were obtained with PDEG ≤ 0.25 and K = 3, and it may be reasonable to expect the largest cluster to consist of many non-DEGs for PDEG < 0.5. However, there may be instances where PDEG > 0.5 [37, 42], and we investigated the effect of P1 = 0.5, 0.7, 0.9, and 1.0 on larger PDEG values (= 0.45–0.75) (Fig. 3). Our results showed that MBCdeg gave high AUC values in most trials with PDEG ≤ 0.55 and P1 ≤ 0.9. However, its performance was decreased drastically in other conditions (e.g., P1 ≥ 0.9 and PDEG ≥ 0.65).

Fig. 3
figure3

Effect on larger PDEG values (PDEG ≥ 0.45). Boxplots of AUC values (50 trials) for five methods for a total of 16 conditions, P1 = 0.5 (left) to 1.0 (right) with PDEG = 0.45 (top) to 0.75 (bottom). MBCdeg1 and MBCdeg2 were used for analysis with K = 3. The performance of MBCdeg2 partly depended on the of DEGES normalization (see Tables 2 and 3)

Overall, the performance of MBCdeg was similar to TCC, and there were few differences between the performance of MBCdeg1 and MBCdeg2. However, MBCdeg1 is superior to both TCC and MBCdeg2 for PDEG = 0.75 and P1 = 0.7. This may be explained by the failure of DEGES normalization that was used in TCC and MBCdeg2. The DEGES normalization works well in the conditions where PDEG ≤ 0.45 and P1 ≤ 1.0, PDEG ≤ 0.55 and P1 ≤ 0.9, PDEG ≤ 0.65 and P1 ≤ 0.7, and PDEG ≤ 0.75 and P1 ≤ 0.5. However, the performance of the normalization decreases in other conditions, and these results are similar to those reported by Evans et al. [35].

Table 2 shows a representative result for MBCdeg for PDEG = 0.75, and P1 = 0.7. In this condition, 5250 DEG1, 2250 DEG2, and 2500 non-DEG patterns were observed. MBCdeg1 gave three clusters, each of which consisted of many genes that resembled one of the three patterns, with a particular pattern predominating. In the trial shown in Table 2a, MBCdeg1 successfully identified the third cluster displaying the minimum norm value (= 0.3145) as the non-DEG cluster and produced an accurately ranked gene list with a high AUC value (= 0.9329). However, in the same trial shown in Table 2b, MBCdeg2 incorrectly determined the third cluster displaying the minimum norm value (= 0.0762) as the non-DEG cluster and produced an inaccurately ranked gene list with a low AUC value (= 0.2421). As the third cluster mainly consists of genes with the DEG1 pattern (4942/5637 = 87.7%), genes that belong to the first and second clusters will be located at the top of the ranked gene list. The AUC value (= 0.2421) may result due to genes in the first cluster showing predominantly DEG2 patterns (1979/2436 = 81.2%) that are correctly ranked at the top.

Table 2 Representative results for MBCdeg with PDEG = 0.75 and P1 = 0.7

In the scenario where MBCdeg1 is inferior to MBCdeg2, we observed 22% of trials (= 11/50) with extremely low AUC values (< 0.2) for MBCdeg1 with PDEG = 0.45 and P1 = 1.0. As this condition has 4500 DEG1 and 5500 non-DEG patterns, the accurate number of clusters is two (i.e., Ktruth = 2). Table 3 shows the representative case of an MBCdeg trial with K = 3. We observed that MBCdeg1 incorrectly determined the third cluster that displayed the minimum norm value (0.4100) as non-DEG one and produced an inaccurately ranked gene list with an extremely low AUC value (= 0.1195). As this condition does not contain any DEG2 pattern, the failure to identify non-DEG cluster results in a non-ideal and incorrectly ranked gene list. This produces several extremely inferior results (11/50 = 22% of trials) when using MBCdeg1. A greater number of successful trials using MBCdeg1 (39/50 = 78% of trials) with PDEG = 0.45 and P1 = 1.0 may result due to the number of non-DEG patterns being greater than DEG1 patterns (i.e., 55:45). Similar explanations for the fewer successful cases using MBCdeg with PDEG = 0.55 and P1 = 1.0 may be valid as the number of non-DEG patterns is fewer than the number of DEG1 patterns (i.e., 45:55). MBCdeg2 shows stable and relatively accurate performance for PDEG ≤ 0.45, regardless of the value of P1 and it may be useful in these conditions. This trend was also observed when the number of replicates per group was increased to n1 = n2 = 6, 9, and 12 (Additional file 5).

Table 3 Results for MBCdeg with PDEG = 0.45 and P1 = 1.0

Three-group simulated data

Next, we investigated the performance of MBCdeg in a multi-group comparison. Tang et al. [43] evaluated the performance of 12 DE pipelines available across nine R packages (TCC [14], edgeR [11], DESeq [44], DESeq2 [15], limma [45], samr [46], PoissonSeq [47], baySeq [12], and EBSeq [13]) and recommended the use of TCC when performing three-group count data. Therefore, we followed the conditions where TCC performed the best in the previous simulation analysis and investigated whether MBCdeg could exceed TCC as well as the other two methods (i.e., edgeR and DESeq2).

As in the evaluation study described [43], we focused on a three-group data with equal numbers of replicates (i.e., three replicates per group; n1 = n2 = n3 = 3). The other simulation conditions were G = 10,000, PDEG = 0.25, and FC = 4. For the proportion of upregulated DEGs in individual groups (P1, P2, P3), we prepared a total of four conditions: (1/3, 1/3, 1/3), (0.6, 0.2, 0.2), (0.5, 0.5, 0.0), and (1.0, 0.0, 0.0). The numbers of true clusters Ktruth for these conditions were 4, 4, 3, and 2, respectively. The first two conditions are the same as those used in the previous study, and the other two conditions were used to examine the effect on different Ktruth values (i.e., 3 and 2). We compared a total of four K values (K = 2, 3, 4, and 5) when performing an analysis using MBCdeg. For the three packages (edgeR, DESeq2, and TCC), gene ranking was performed based on an ANOVA-like p-value, where a low p-value for a gene indicates a high degree of DE in at least one of the groups compared.

Figure 4 shows the boxplots of AUC values using these methods in the four conditions described. We observed that the AUC values were the highest when K = Ktruth and that the performances with K > Ktruth were higher than those with K < Ktruth. Further, this trend was observed when the number of replicates per group was increased to n1 = n2 = n3 = 6, 9, and 12 (Additional file 6). However, for simulated data with variable FC values, AUC values for MBCdeg with K ≥ Ktruth tended to be higher than those with K = Ktruth (Additional file 7). This is consistent with the results of the two-group data (Additional file 4). Together, these results indicate that MBCdeg yields better performance with larger K values (e.g., K > I + 1) when using real data with a group number I.

Fig. 4
figure4

Results for three-group simulated data. Boxplots of AUC values (50 trials) for eleven methods (DESeq2, edgeR, TCC, and MBCdeg with K = 2–5) are shown. The simulation conditions used were PDEG = 0.25, FC = 4, and n1 = n2 = n3 = 3

Two-group real data

We examined the performance of MBCdeg for a real count dataset available from the recount2 database [48]. As with previous analyses, we compared a total of five methods: edgeR, DESeq2, TCC, MBCdeg1, and MBCdeg2. For the first three packages, genes with a false discovery rate (FDR) of 0.1 or higher were defined as non-DEGs. The other genes were identified as having DEG1 or DEG2 patterns in the direction of DE. Some may argue that this cut-off (10% FDR) is somewhat looser than the commonly used ones (e.g., 1% or 5%). However, we empirically know that even with such a loose threshold of 10% FDR, the number of genes satisfying the threshold will be lower than the true number of DEGs [20]. Hence, we decided that it is more reasonable to use the default TCC threshold of 10% FDR for the sake of comparison in the context of the MBCdeg results. For MBCdeg, we investigated the effect on different K values (= 3, 4, and 5). As previously described, genes belonging to the cluster with the smallest L2 Norm were considered as non-DEGs. The other genes were identified as having DEG1 or DEG2 patterns in the direction of DE, regardless of the cluster to which the genes belonged. As the true DE results are not known, we focus our attention on the similarities between the methods.

The real dataset (called “Pickrell”) consisting of 51,910 × 69 samples was designed to compare the expression levels of lymphoblastoid cell lines between 40 females and 29 males [49]. This dataset has been widely used for validation purposes [50]. Table 4 shows the results of the gene classification into one of the three patterns. Overall, MBCdeg1 with K = 5 showed (i) the maximum PDEG value of (1291 + 3433)/51,910 = 0.0910, and (ii) the maximum degree of asymmetry, defined as max(P1, P2), of 3433/(1291 + 3433) = 0.7267. These values are within the range used in the simulation analysis (see Additional files 2 and 4). The values in parentheses indicate the average log2(FC) values for the genes. Our results showed that the values for DEG1 and DEG2 are approximately 1.8 (i.e., the average FC is about 2^1.8 = 3.5), which is within the values of FC = 2.5 and 4 introduced in the current simulation analysis.

Table 4 DE results using the Pickrell data

DESeq2 and TCC showed similar results (99.8% concordance), and edgeR and MBCdeg also showed similar results (> 97% concordance). Even the lowest concordance rate was found between DESeq2 and MBCdeg1 with K = 5 at 90.86% (see “Sheet1” in Additional file 8). This high concordance rate (≥ 90.86%) among the methods is reasonable as the maximum PDEG value is 9.10% (i.e., mostly determined as non-DEGs). We observed a high similarity between the results from six MBCdeg methods, where the lowest concordance rate was found between MBCdeg1 with K = 5 and MBCdeg2 with K = 4 at 97.26%. This suggests that a cluster of DEG1 (or DEG2) patterns obtained by K = 3 is divided into sub-clusters when K = 4 or 5. Indeed, MBCdeg1 with K = 4 produced two clusters (containing 1939 genes and 1454 genes, respectively) assigned to the DEG2 pattern (see “Sheet2” in Additional file 8). Of a total of 3393 genes, 2713 genes were included in the 3298 DEG2 genes obtained by MBCdeg1 with K = 3 (Table 4).

Similar to the analysis using MBCdeg1 with K = 4, MBCdeg2 with K = 4 produced two clusters (containing 60 genes and 1291 genes, respectively) assigned to the DEG1 pattern (“Sheet2” in Additional file 8). Interestingly, the 1291 genes in the second cluster were identical to the 1291 DEG1 genes obtained using MBCdeg2 with K = 3, and 1290 of the 1291 genes were the same as those obtained using MBCdeg1 with K = 3–4. In the results obtained using MBCdeg2 with K = 4, we found that the representative expression patterns for the remaining 60 genes assigned to the DEG1 pattern (the cluster centers μ = (0.184, − 0.184)) were very similar to those assigned to the non-DEG pattern (μ = (0.014, − 0.014)) than the other 1291 genes to the DEG1 pattern (μ = (3.117, − 3.117)), given the degrees of DE. These 60 genes were assigned to non-DEG pattern at K = 3. Although we defined only one cluster with the smallest L2 Norm as of non-DEG, it may be better to define the clusters with low L2 Norm as of non-DEGs and to consider their assignments when using other K values.

In the results with K = 5, we observed that MBCdeg2 gave two DEG1 clusters containing 38 and 1291 genes, and two DEG2 clusters containing 1938 and 1364 genes. Additionally, MBCdeg1 gave one DEG1 cluster containing 1291 genes and three DEG2 clusters containing 1923, 39, and 1471 genes, respectively. As described earlier, the 1291 DEG1 genes obtained by MBCdeg2 with K = 5 were identical to those obtained by MBCdeg2 with K = 3–4 and 1290 out of the 1291 DEG1 genes were the same as those obtained by MBCdeg1 with K = 3–5. The degrees of DE in the three DEG2 clusters obtained when using MBCdeg1 were sorted in descending order of the clusters consisting of 1923, 39, and 1471 genes (“Sheet2” in Additional file 8). The 1962 genes from the first two DEG2 clusters were included in the DEG2 cluster containing 3298 genes, that was obtained by using MBCdeg1 with K = 3. However, 751 (or 720) of the remaining 1471 genes with the lowest degree of DE from the last DEG2 cluster, were included alone in the DEG2 (or non-DEG) cluster obtained by using MBCdeg1 with K = 3. These results suggest that the use of MBCdeg for DE analysis should take into account the fluctuation of expression patterns in clusters obtained at various K values, as well as the PPs assigned to each gene.

Discussion

Gene clustering has been used to classify DEGs with similar expression patterns. The MBCluster.Seq package was originally developed to be used as a post-DE analysis tool. In this study, we propose the use of this model-based gene clustering algorithm for DE analysis itself (i.e., the identification of DEGs). Although the creators of MBCluster.Seq have not envisioned this possibility, the DE framework based on the package (called MBCdeg) outperformed three other packages (edgeR, DESeq2, and TCC) dedicated to DEG identifications in many DE scenarios with PDEG < 50%.

Several bioinformatics studies have made comparisons with typical conventional analysis methods and claimed about superior results by using the methods of interest [51]. However, a typical package may not be the best suited for the analysis. As demonstrated in this study, the less well-known TCC package gave better results in our analysis than the typical packages (edgeR and DESeq2). The main contribution of this study is the demonstration that clustering-based DE framework (MBCdeg) performed better than TCC under the scenarios for which TCC performed well.

MBCdeg2 (MBCdeg with robust DEGES normalization) was slightly more stable and accurate than MBCdeg1 (MBCdeg with the default normalization) when using the simulation analysis employed in our study. A common disadvantage of the MBCdeg is that in datasets with a very large proportion of DEGs (PDEG ≥ 50%), the identification of the non-DEG cluster, which is the key to the framework proposed here, fails frequently and results in incorrect rankings. Therefore, conventional methods must be used in conjunction with MBCdeg to ensure that the overall similarities in the rankings are maintained. Additionally, it is necessary to verify various K values to investigate the cluster-wise fluctuations for DEGs and the number of clusters identified as optimal by MBCdeg. While the current simulation analysis was done with TCC, there are several other simulation frameworks available (e.g., polyester [52] and countsimQC [53]). The proposed method requires (i) evaluation based on those simulation frameworks, (ii) real data with various experimental settings and organisms, and (iii) additional fine-tuning described above.

Conclusions

The model-based gene clustering allowed by the MBCluster.Seq package is a promising tool for both the identification and classification of DEGs in one step. The proposed procedure, MBCdeg, can be used in the context of RNA-seq count data in which the percentage of DEGs is less than half (PDEG < 50%).

Methods

All analyses were performed using CRAN/R (ver. 3.6.3) [54] and Bioconductor [55]. The versions of the major R packages used were MBCluster.Seq ver. 1.0 [28], TCC ver. 1.26.0 [14], edgeR ver. 3.28.1 [11], DESeq2 ver. 1.26.0 [15], ROC ver. 1.6.3, and recount ver. 1.12.1. The R-codes leading to the results described in this paper are available on GitHub (https://github.com/takosa/MBCdeg-paper). A two-group sample data and R-codes (MBCdeg1 and MBCdeg2) for data execution are also provided.

Simulated data

The simulated data for two- and three-group comparisons were generated using the simulateReadCounts function in TCC [14]. The number of genes (G = 10,000) was given in the Ngene option. The number of replicates for individual groups (i.e., n1, n2, …, nI for I-group comparison) were specified in the replicates option. The proportion of DEGs (PDEG) was given in the PDEG option. The assignment of DEGs upregulated in individual groups (i.e., P1, P2, …, PI, and P1 + P2 + … + PI = 1) was specified in the DEG.assign option. The fixed degrees of DE (FC = 4) were specified in the DEG.foldchange option. To generating different degrees of DE, the makeFCMatrix function was used, and the output object was used as the input for the fc.matrix option in the simulateReadCounts function. The output of the simulateReadCounts function was stored in the TCC class object with information about the simulation conditions and is therefore ready-to-analyze in the DE analysis.

Real data

Pickrell’s real data was obtained by searching the recount2 database with the accession number “SRP001540”. The original count matrix (58,037 genes × 160 samples) consists of human data obtained from two different sequencing centers; one from Yale and the other from Argonne [48, 49]. Since both datasets have been found to show similar results [50], we analyzed the Yale count data of 79 samples alone. The matrix was collapsed by summing the data for technical replicates, resulting in a reduced number of columns/samples (79 → 69). Genes with a count of zero in all samples were excluded (58,037 → 51,910). DE analysis was performed using 40 female samples vs. 29 male samples.

DE analysis

DE analysis using edgeR was performed by applying the following functions with the default options in the sequence indicated: DGEList, calcNormFactors, estimateDisp, glmQLFit, glmQLFTest, and topTags. The p-values for individual genes were obtained by the output of the topTags function. The genes were ranked in ascending order based on the p-values and the ranks were used to calculate the AUC values. The AUC values were calculated using the AUC function in the ROC package. The p-values were adjusted for multiple testing by using the Benjamini–Hochberg procedure [56]. The adjusted p-values (q-values) were obtained using the p.adjust function with the method = ”BH” option. Genes that had a q-value greater than 0.1 were defined as having a non-DEG pattern. The log2(FC) values for individual genes were obtained from the output of the topTags function (the logFC column in the table slot). Genes that showed a q-value less than 0.1, and genes with log2(FC) values less (or greater) than 0 were defined as having a DEG1 (or DEG2) pattern.

DE analysis using DESeq2 [15] was performed by applying the following functions with default options in the sequence indicated: DESeqDataSetFromMatrix, DESeq, and results. The p-values for individual genes were obtained by using the output of the results function (the pvalue column). The adjusted p-values (q-values) were obtained from the output of the results function (the padj column). The other procedures followed were the same as those described in edgeR.

DE analysis using TCC was performed by using the following functions with default options in the sequence indicated: new, calcNormFactors, estimateDE, and getResult. The gene ranking information and q-values were obtained directly from the output of the getResult function. The other procedures were the same as those described in edgeR. The normalization factors assigned for individual samples were obtained from the output of the calcNormFactors function in TCC. The normalization factors were converted to size factors, and the log-transformed values were used in the Normalizer option in the RNASeq.Data function when using MBCdeg2.

The DE analysis based on the MBCluster.Seq method [28] (i.e., MBCdeg) was performed by applying the following functions in sequence: RNASeq.Data, KmeansPlus.RNASeq, and Cluster.RNASeq. The Normalizer = NULL option in the RNASeq.Data function was used when performing an analysis using MBCdeg1 as it corresponds to the default normalization algorithm [33] employed in MBCluster.Seq. The preselected number of clusters K was introduced in the nK option in the KmeansPlus.RNASeq function. The negative binomial model (“nbinom”) was used in the model option in both KmeansPlus.RNASeq and Cluster.RNASeq functions. The output of the KmeansPlus.RNASeq function was used as the initial cluster center when executing the Cluster.RNASeq function. The expectation–maximization (EM) algorithm was used to iteratively update the estimates of clusters and their centers. This corresponds to the method = “EM” option in the Cluster.RNASeq function. The centers for K clusters (k = 1, …, K) across I groups (i = 1, …, I), μki, were used to calculate the L2 Norm values ||μk||2 for individual clusters. The information for cluster centers and the PPs for individual genes (g = 1, …, G) across clusters were obtained as the outputs of the Cluster.RNASeq function. Genes in a cluster that had the smallest L2 Norm value were regarded as non-DEGs. The other genes were determined to have DEG1 or DEG2 patterns in the direction of DE when performing a two-group comparison (I = 2). The overall gene ranking was performed based on the PPs assigned to the non-DEG cluster.

Availability of data and materials

The dataset (SRP001540) analyzed during the current study is available in the recount2 website, [https://jhubiostatistics.shinyapps.io/recount/]. The R-codes leading to the results described in this paper are available on GitHub, [https://github.com/takosa/MBCdeg-paper]. A two-group sample data and R-codes (MBCdeg1 and MBCdeg2) for data execution are also provided.

Abbreviations

ANOVA:

Analysis of variance

AUC:

The area under the receiver operating characteristic curve

β gi :

Count of gene g in group i relative to the overall mean on log-scale

DE:

Differential expression

DEG:

Differentially expressed gene

DEG1:

Expression pattern up-regulatd in group 1

DEG2:

Expression pattern up-regulatd in group 2

EM:

Expectation-maximization

FC:

(Degree of) fold-change

G:

Number of genes

GUI:

Graphical user interface

I :

Number of compared groups

K :

Preselected number of clusters

K truth :

True number of clusters

MBCdeg:

The proposed DEG identification procedure based-on gene clustering with MBCluster.Seq

MBCdeg1:

MBCdeg with default normalization algorithm

MBCdeg2:

MBCdeg with a robust normalization algorithm called DEGES

μ k :

Center for cluster k

n i :

Number of replicates for group i

non-DEG:

No differential expression pattern between groups

p gk :

Posterior probability (PP) of gene g belonging to the kth cluster

P i :

Proportion of up-regulated DEGs in group i

P DEG :

Proportion of DEG

PP:

Posterior probability

ROC:

Receiver operating characteristic

References

  1. 1.

    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621–8.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008;18:1509–17.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Kudo A, Shigenobu S, Kadota K, Nozawa M, Shibata TF, Ishikawa Y, et al. Comparative analysis of the brain transcriptome in a hyper-aggressive fruit fly Drosophila prolongata. Insect Biochem Mol Biol. 2017;82:11–20.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Ohde T, Morita S, Shigenobu S, Morita J, Mizutani T, Gotoh H, et al. Rhinoceros beetle horn development reveals deep parallels with dung beetles. PLoS Genet. 2018;14:e1007651.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  5. 5.

    Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. 6.

    Risso D, Schwartz K, Sherlock G, Dudoit S. GC-content normalization for RNA-seq data. BMC Bioinform. 2011;12:480.

    CAS  Article  Google Scholar 

  7. 7.

    Kadota K, Nishiyama T, Shimizu K. A normalization strategy for comparing tag count data. Algorithms Mol Biol. 2012;7:5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Li J, Witten DM, Johnstone IM, Tibshirani R. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics. 2012;13:523–8.

    PubMed  Article  Google Scholar 

  9. 9.

    Maza E, Frasse P, Senin P, Bouzayen M, Zouine M. Comparison of normalization methods for differential gene expression analysis in RNA-seq experiments: a matter of relative size of studied transcriptomes. Commun Integr Biol. 2013;6:e25849.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  10. 10.

    Tran DT, Bhaskara A, Kuberan B, Might M. A graph-based algorithm for RNA-seq data normalization. PLoS ONE. 2020;15:e0227760.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    CAS  Article  Google Scholar 

  12. 12.

    Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinform. 2010;11:422.

    Article  Google Scholar 

  13. 13.

    Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BMG, et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 2013;29:1035–43.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Sun J, Nishiyama T, Shimizu K, Kadota K. TCC: An R Package for comparing tag count data with robust normalization strategies. BMC Bioinform. 2013;14:219.

    Article  Google Scholar 

  15. 15.

    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. 16.

    Gao Z, Zhao Z, Tang W. DREAMSeq: an improved method for analyzing differentially expressed genes in RNA-seq data. Front Genet. 2018;9:588.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Russo F, Righelli D, Angelini C. Advancements in RNASeqGUI towards a reproducible analysis of RNA-seq experiments. Biomed Res Int. 2016;2016:7972351.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. 18.

    Su W, Sun J, Shimizu K, Kadota K. TCC-GUI: a shiny-based application for differential expression analysis of RNA-seq count data. BMC Res Notes. 2019;12:133.

    PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Reyes ALP, Silva TC, Coetzee SG, Plummer JT, Davis BD, Chen S, et al. GENAVi: a shiny web application for gene expression normalization, analysis and visualization. BMC Genom. 2019;20:745.

    Article  CAS  Google Scholar 

  20. 20.

    Zhao S, Sun J, Shimizu K, Kadota K. Silhouette scores for arbitrary defined groups in gene expression data and insights into differential expression results. Biol Proced Online. 2018;20:5.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  21. 21.

    Alessandrì L, Arigoni M, Calogero R. Differential expression analysis in single-cell transcriptomics. Methods Mol Biol. 2019;1979:425–32.

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    Osabe T, Shimizu K, Kadota K. Accurate classification of differential expression patterns in a bayesian framework with robust normalization for multi-group RNA-seq count data. Bioinform Biol Insights. 2019;13:1177932219860817.

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Nguyen Y, Nettleton D. rmRNAseq: differential expression analysis for repeated-measures RNA-seq data. Bioinformatics. 2020;36:4432–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Yu L, Fernandez S, Brock G. Power analysis for RNA-Seq differential expression studies using generalized linear mixed effects models. BMC Bioinform. 2020;21:198.

    CAS  Article  Google Scholar 

  25. 25.

    Ueda Y, Ohtsuki N, Kadota K, Tezuka A, Nagano AJ, Kadowaki T, et al. Gene regulatory network and its constituent transcription factors that control nitrogen-deficiency responses in rice. New Phytol. 2020;227:1434–52.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Han Y, Gao S, Muegge K, Zhang W, Zhou B. Advanced applications of RNA sequencing and challenges. Bioinform Biol Insights. 2015;9(Suppl 1):29–46.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Li P, Ponnala L, Gandotra N, Wang L, Si Y, Tausta SL, et al. The developmental dynamics of the maize leaf transcriptome. Nat Genet. 2010;42:1060–7.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  28. 28.

    Si Y, Liu P, Li P, Brutnell TP. Model-based clustering for RNA-seq data. Bioinformatics. 2014;30:197–205.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  29. 29.

    Fu Y, Dong T, Tan L, Yin D, Zhang M, Zhao G, et al. Identification of shoot differentiation-related genes in Populus euphratica Oliv. Genes (Basel). 2019;10:1034.

    CAS  Article  Google Scholar 

  30. 30.

    Li J, Bushel PR. EPIG-Seq: extracting patterns and identifying co-expressed genes from RNA-Seq data. BMC Genomics. 2016;17:255.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. 31.

    Silva A, Rothstein SJ, McNicholas PD, Subedi S. A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data. BMC Bioinform. 2019;20:394.

    Article  Google Scholar 

  32. 32.

    Erola P, Bjorkegren JLM, Michoel T. Model-based clustering of multi-tissue gene expression data. Bioinformatics. 2020;36:1807–13.

    CAS  PubMed  Google Scholar 

  33. 33.

    Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinform. 2010;11:94.

    Article  CAS  Google Scholar 

  34. 34.

    Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 2013;14:671–83.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Evans C, Hardin J, Stoebel DM. Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions. Brief Bioinform. 2018;19:776–92.

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Vieth B, Parekh S, Ziegenhain C, Enard W, Hellmann I. A systematic evaluation of single cell RNA-seq analysis pipelines. Nat Commun. 2019;10:4667.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. 37.

    Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, et al. Brain structure. Cell types in the mouse cortex and Hippocampus revealed by single-cell RNA-seq. Science. 2015;347:1138–42.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Lin CY, Lovén J, Rahl PB, Paranal RM, Burge CB, Bradner JE, et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell. 2012;151:56–67.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Kadota K, Nakai Y, Shimizu K. A weighted average difference method for detecting differentially expressed genes from microarray data. Algorithms Mol Biol. 2008;3:8.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. 40.

    Kadota K, Nakai Y, Shimizu K. Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity. Algorithms Mol Biol. 2009;4:7.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41.

    Kadota K, Shimizu K. Evaluating methods for ranking differentially expressed genes applied to MicroArray Quality Control data. BMC Bioinform. 2011;12:227.

    Article  Google Scholar 

  42. 42.

    Schurch NJ, Schofield P, Gierliński M, Cole C, Sherstnev A, Singh V, et al. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA. 2016;22:839–51.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Tang M, Sun J, Shimizu K, Kadota K. Evaluation of methods for differential expression analysis on multi-group RNA-seq count data. BMC Bioinform. 2015;16:361.

    Article  Google Scholar 

  44. 44.

    Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol., 2004;3:Article3.

  46. 46.

    samr package. https://CRAN.R-project.org/package=samr. Accessed 31 July 2020.

  47. 47.

    Li J, Witten DM, Johnstone IM, Tibshirani R. Normalization, testing and false discovery rate estimation for RNA-sequencing data. Biostatistics. 2012;13:523–38.

    PubMed  Article  Google Scholar 

  48. 48.

    Collado-Torres L, Nellore A, Kammers K, Ellis S, Taub MA, Hansen KD, et al. Reproducible RNA-seq analysis using recount2. Nat Biotechnol. 2017;35:319–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010;464:768–72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013;14:7.

    Article  Google Scholar 

  51. 51.

    Kadota K, Shimizu K. Commentary: A systematic evaluation of single cell RNA-seq analysis pipelines. Front Genet. 2020;11:941.

    PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Frazee AC, Jaffe AE, Langmead B, Leek JT. Polyester: simulating RNA-seq datasets with differential transcript expression. Bioinformatics. 2015;31:2778–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Soneson C, Robinson MD. Towards unified quality verification of synthetic count data with countsimQC. Bioinformatics. 2018;34:691–2.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  54. 54.

    R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2016.

    Google Scholar 

  55. 55.

    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5:R80.

    PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57:289–300.

    Google Scholar 

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Acknowledgements

We would like to thank Editage (www.editage.jp) for English language editing.

Funding

This work was supported by JSPS KAKENHI Grant Number JP21K12120. The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data, or in writing the manuscript.

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Authors

Contributions

TO made the R codes, performed the analysis and drafted the manuscript. KS designed the study, supervised the critical discussion and revised the paper. KK confirmed the analysis, revised the paper and led this project to completion. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Koji Kadota.

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Supplementary Information

Additional file 1.

Results corresponding to Fig. 1 with a larger number of replicates. Boxplots of AUC values (100 trials) for individual methods with n1 = n2 = (a) 6, (b) 9, and (c) 12 are shown. In the simulation, the degree of DE was fixed at 4-fold (i.e., FC = 4).

Additional file 2.

Results corresponding to Fig. 2 with a larger number of replicates. Boxplots of AUC values (100 trials) for MBCdeg (K = 2–4) with n1 = n2 = (a) 6, (b) 9, and (c) 12 are shown.

Additional file 3.

Effect on different degrees of DE for the five methods. Boxplots of AUC values (100 trials) for individual methods with n1 = n2 = (a) 3, (b) 6, (c) 9, and (d) 12 are shown. In contrast to Fig. 1 and Additional file 1, simulations were performed using different degrees of DE.

Additional file 4.

Effect on different degrees of DE for MBCdeg with K = 2–4. Boxplots of AUC values (100 trials) for MBCdeg (K = 2–4) with n1 = n2 = (a) 3, (b) 6, (c) 9, and (d) 12 are shown. In contrast to Fig. 2 and Additional file 2, simulations were performed using different degrees of DE. The AUC values for MBCdeg with K = 3 were almost the same as those in Additional file 3 (different trials were used).

Additional file 5.

Results corresponding to Fig. 3 with a larger number of replicates. Boxplots of AUC values (50 trials) for individual methods with n1 = n2 = (a) 6, (b) 9, and (c) 12 are shown.

Additional file 6.

Results corresponding to Fig. 4 with a larger number of replicates. Boxplots of AUC values (50 trials) for individual methods with n1 = n2 = n3 = (a) 6, (b) 9, and (c) 12 are shown.

Additional file 7.

Results corresponding to Additional file 4 on the three-group simulated data. Boxplots of AUC values (50 trials) for individual methods with n1 = n2 = n3 = (a) 3, (b) 6, (c) 9, and (d) 12 are shown.

Additional file 8.

Concordance rate between methods for Pickrell data. This file provides additional information given in Table 4. The “Sheet1” shows the percentage of genes assigned to the same pattern that are common between the two methods. For example, DESeq2 and TCC have a total of 51,812 similar patterns, and the concordance rate is therefore calculated as 51,812/51,910 = 0.9981. The “Sheet2” shows the results for each cluster obtained using MBCdeg-based methods: (a–c) MBCdeg1 with K = 3–5 and (d–f) MBCdeg2 with K = 3–5. The number of genes (column B), centers m (columns C–E), L2 Norm (column F), and the patterns assigned (column G) for each cluster are shown.

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Osabe, T., Shimizu, K. & Kadota, K. Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data. BMC Bioinformatics 22, 511 (2021). https://doi.org/10.1186/s12859-021-04438-4

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Keywords

  • RNA-seq
  • Differential expression
  • Gene clustering
  • Posterior probability