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Network-based clustering methods frequently require the number of communities to be specified \emph{a priori}. Moreover, most of the existing methods for estimating the number of communities assume the number of communities to be fixed and not scale with the network size $n$. The few methods that assume the number of communities to increase with the network size $n$ are only valid when the average degree $d$ of a network grows at least as fast as $O(n)$ (i.e., the dense case) or lies within a narrow range. This presents a challenge in clustering large-scale network data, particularly when the average degree $d$ of a network grows slower than the rate of $O(n)$ (i.e., the sparse case). To address this problem, we proposed a new sequential procedure utilizing multiple hypothesis tests and the spectral properties of Erdös Rényi graphs for estimating the number of communities in sparse stochastic block models (SBMs). We prove the consistency of our method for sparse SBMs for a broad range of the sparsity parameter. As a consequence, we discover that our method can estimate the number of communities $K^{(n)}_{\star}$ with $K^{(n)}_{\star}$ increasing at the rate as high as $O(n^{(1 - 3γ)/(4 - 3γ)})$, where $d = O(n^{1 - γ})$. Moreover, we show that our method can be adapted as a stopping rule in estimating the number of communities in binary tree stochastic block models. We benchmark the performance of our method against other competing methods on six reference single-cell RNA sequencing datasets. Finally, we demonstrate the usefulness of our method through numerical simulations and by using it for clustering real single-cell RNA-sequencing datasets.