Breakthroughs in the development of high throughput technologies for profiling transcriptomes at the single-cell level have helped biologists to understand the heterogeneity of cell populations disease states and developmental lineages.However thse single cell RNA sequencing technologies generate an extraordinary amout of data, which creates analysis and interpretation challenges.Additionally织阳，scRNA-seq datasets often contain technical sources of moise owing to incomplete RNA cature PCR amplofication biases and batch effects specific to the patient or sample.If no addressed this technical noise can bias the analysis and inerpretation of the data. In tespomse to the se challenges ,a suite of computational tools has been developed to process analyse and visualize scRNA-seq datasets.Although the specific steps of any given scRNA-seq analysis might differ depending on the biological questions being asked,a core workflow is used in most analysis.Tyoically,raw sequencing reads are precessed into a gene expression matrix that is then normalized and scaled to remove technical noise. Next cells are grouped according to similarities in their patterns of gene expression,which can be summarized in two or three dimensions for visualization on a scatterplet. These data can then be further analysed to procide an in-depth view of the cell types or decelopmental trajectories in the sample if interest.
單細胞水平轉錄組分析高通量技術的發(fā)展突破薇缅，幫助生物學家了解細胞群體惭嚣、疾病狀態(tài)和發(fā)育譜系的異質性。然而塑娇，這些單細胞RNA測序技術產生了大量的數(shù)據(jù)，這給分析和解釋帶來了挑戰(zhàn)劫侧。此外埋酬，由于不完整的RNA捕獲PCR擴增偏差和特定于患者或樣本的批量效應，scRNA-seq數(shù)據(jù)集通常包含moise的技術來源烧栋。如果不解決写妥，這種技術噪音會使數(shù)據(jù)的分析和解釋產生偏差。為了應對se挑戰(zhàn)审姓，已經(jīng)開發(fā)了一套計算工具來處理珍特、分析和可視化scRNA-seq數(shù)據(jù)集。盡管任何給定的scRNA-seq分析的具體步驟可能因所問的生物學問題而有所不同魔吐，但在大多數(shù)分析中使用的是核心工作流程扎筒。通常，原始的測序序列被放入基因表達矩陣酬姆，然后被標準化和縮放以消除技術噪音砸琅。下一個細胞根據(jù)其基因表達模式的相似性進行分組，這些相似性可以在散點上以二維或三維的形式進行總結轴踱。如果感興趣症脂，這些數(shù)據(jù)可以進一步分析，以提供對樣品中細胞類型或發(fā)育軌跡的深入觀察淫僻。
