What Are the Probability Distributions of Counts, TPM, and Log-TPM in Single-Cell Transcriptome Data?
Counts
1. Counts refer to the number of RNA molecules detected for each gene, derived from gene quantification in sequencing data.
2. In single-cell transcriptome data, the probability distribution of counts is typically discrete, as the values are integer-based. The counts for each gene can range from 0 to very high numbers.
3. For an individual cell, the counts for each gene represent the expression level of that gene in that specific cell. In the entire single-cell dataset, the distribution of counts for each gene reflects the gene’s expression pattern across the entire cell population.
TPM (Transcripts Per Million)
1. TPM is a normalized measure of gene expression. It divides the expression level of each gene by the total transcript count and multiplies by a normalization factor, typically 1,000,000.
2. The probability distribution of TPM is continuous, as it is a relative measure of expression that can take any real value.
3. One advantage of TPM is that it enables comparison of expression levels of different genes both within the same sample and between different samples, without being affected by gene length or sequencing depth.
Log-TPM (Logarithm of TPM)
1. Log-TPM is obtained by applying a log-transformation to the TPM values. It is commonly used to reduce the influence of highly expressed genes and make low-expressed genes more comparable.
2. The probability distribution of Log-TPM is also continuous since it is derived from the transformation of TPM.
3. Log-TPM has the advantage of better fitting the normal distribution assumption, making subsequent statistical analyses more reliable.
While the probability distribution of counts is discrete, the distributions of TPM and Log-TPM are continuous. When analyzing single-cell transcriptome data, selecting the most suitable expression measure depends on the specific research goals and analysis methods.
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