FLASH-MM 0.1.0
Fast, scalable, and memory-efficient algorithm for fitting linear mixed-effects models (LMMs)
Requires Python: >=3.8
Keywords: mixed-effects model, large-scale data, single-cell differential expression

# FLASH-MM

FLASH-MM provides a fast and scalable algorithm for fitting linear mixed-effects models (LMMs). Mixed-effects models are essential for capturing intra-subject correlations and inter-subject variability, but traditional LMM estimation methods often suffer from limitations in computational speed and memory efficiency when applied to large-scale datasets. The FLASH-MM overcomes these challenges by offering a scalable and memory-efficient approach to LMM fitting.

The FLASH-MM Python package provides two functions for fitting LMMs: lmm and lmmfit. The lmm function takes summary statistics as input, whereas lmmfit is a wrapper around lmm that directly processes cell-level data and computes the summary statistics internally. While lmmfit is easier to use, it has the limitation of higher memory consumption. For extremely large-scale data, it is recommended to precompute and store the summary statistics and then use lmm function to fit LMMs.

In summary, FLASH-MM package provides the following main functions.

* lmm: fit LMM using summary-level (summary statistics) data.
* lmmfit: fit LMM using cell-level data.
* lmmtest: perform statistical tests on the fixed effects and their contrasts.
* sslmm: compute the summary-levle data.
* contrast_matrix: construct contrast matrix of the fixed effects for various comparisons.

## Installation
pip install .

## Example
```python
import numpy as np
import pandas as pd
from flash_mm import lmm, lmmfit, lmmtest, sslmm, contrast_matrix

# Generate data
np.random.seed(2024)
n, m = 1000, 10
# Create Y matrix (m x n)
Y = np.random.randn(m, n)
Y_names = [f"Gene{i+1}" for i in range(Y.shape[0])]

# Create treatment groups
trt = np.random.choice(["A", "B"], size=n, replace=True)

# Create model matrix (dummy variables for trt)
X = np.column_stack((trt == "A", trt == "B")).astype(float)
X_names = [f"Trt{i+1}" for i in range(X.shape[1])]

# Random effect matrix for individual samples
q = 20
sam = np.empty(n, dtype=object)
sam[trt == "A"] = [f"A{i}" for i in np.random.randint(1, q//2 + 1, size=(trt == "A").sum())]
sam[trt == "B"] = [f"B{i}" for i in np.random.randint(1, q//2 + 1, size=(trt == "B").sum())]
Z = np.column_stack([sam == f"A{i}" for i in range(1, q//2 + 1)] +
                    [sam == f"B{i}" for i in range(1, q//2 + 1)]).astype(float)

d = Z.shape[1]
#d = [int(q/2), int(q/2)]

method = "REML"
#method = "ML"

# Fit LMM based on sample-level data.
fit = lmmfit(Y, X, Z, d=d, Y_names=Y_names, X_names=X_names, method = method, output_cov=True, output_RE=True)

print(fit['theta'])
print(fit['coef'])

# Fit LMM based on summary-level (summary statistics) data.
## Compute the summary-level data.
n = X.shape[0]
XX = X.T @ X
XY = (Y @ X).T
ZX = Z.T @ X
ZY = (Y @ Z).T
ZZ = Z.T @ Z
Ynorm = np.sum(Y * Y, axis=1)

## Or use sslmm function to compute the summary statistics.
#ss = sslmm(X, Y, Z)
#XX = ss["XX"]
#XY = ss["XY"]
#ZX = ss["ZX"]
#ZY = ss["ZY"]
#ZZ = ss["ZZ"]
#Ynorm = ss["Ynorm"]
#n = ss["n"]

fitss = lmm(XX, XY, ZX, ZY, ZZ, Ynorm, n=n, d=d, method=method, output_cov=True, output_RE=True)

print(fitss['theta'])
print(fitss['coef'])
print(fitss['cov'])

# Test fixed effects.
test=lmmtest(fit)
print(test)

## Use contrasts.
model_vars = ["A", "B"]
contrast = {
    "AvsB": "A-B",
    "A": "A",
    "B": "B"
    }

cm = contrast_matrix(contrast, model_vars)
print(cm)
test_cm=lmmtest(fit, contrast = cm)
print(test_cm)

```