---
id: "claim:normalization-for-sampled-count-data-2026-06-22-biorxiv-v4:f2bbb0fee639dc74af54"
kind: "claim"
title: "log1p and square-root transforms substantially flatten the technical mean-variance trend."
description: ""
url: "https://sina.bio/claim/log1p-and-square-root-transforms-substantially-flatten-the-technical-me-d90960db"
---

# log1p and square-root transforms substantially flatten the technical mean-variance trend.

## Content

log1p and square-root transforms substantially flatten the technical mean-variance trend.

## Metadata

```json
{
  "claim_position": 34,
  "source_publication_id": "version:normalization-for-sampled-count-data--2026-06-22-biorxiv-v4",
  "source_work_id": "work:normalization-for-sampled-count-data"
}
```

## Connections (1)

### Normalization for sampled count data

- ID: `version:normalization-for-sampled-count-data--2026-06-22-biorxiv-v4`
- Kind: `publication`
- Direction: `incoming`
- URL: <https://sina.bio/publication/normalization-for-sampled-count-data>

This publication contains this grounded claim.

> On the Angelidis2019 dataset (Angelidis et al., 2019), methods with- out explicit technical variance stabilization show low entropy in PC loadings, while transformations such as log1p and sqrt flatten the technical mean-variance trend substantially (Fig- ure 1a, top).

Source: 2026-06-22-biorxiv-v4.pdf — <https://sina.bio/source/2026-06-22-biorxiv-v4-pdf>