---
id: "claim:universal-preprocessing-of-single-cell-genomics-data-2023-09-15-biorxiv:e9e0fc7b92ee4851e395"
kind: "claim"
title: "Best-fit efficiency slopes were computed with sklearn LinearRegression."
description: ""
url: "https://sina.bio/claim/best-fit-efficiency-slopes-were-computed-with-sklearn-linearregression"
---

# Best-fit efficiency slopes were computed with sklearn LinearRegression.

## Content

Best-fit efficiency slopes were computed with sklearn LinearRegression.

## Metadata

```json
{
  "claim_position": 94,
  "source_publication_id": "version:universal-preprocessing-of-single-cell-genomics-data--2023-09-15-biorxiv",
  "source_work_id": "work:universal-preprocessing-of-single-cell-genomics-data"
}
```

## Connections (1)

### Universal preprocessing of single-cell genomics data

- ID: `version:universal-preprocessing-of-single-cell-genomics-data--2023-09-15-biorxiv`
- Kind: `publication`
- Direction: `incoming`
- URL: <https://sina.bio/publication/universal-preprocessing-of-single-cell-genomics-data>

This publication contains this grounded claim.

> In order to compute the sum of reads for each cell, we used bustools count with the –cm option. The resultant matrix is summed along the features to produce a vector for each cell. Then the sum of reads for each cell was plotted against the sum of UMIs for each cell. The slopes of the best fit lines were computed with the sklearn LinearRegression function.

Source: 2023-09-15-biorxiv.pdf — <https://sina.bio/source/2023-09-15-biorxiv-pdf>