Github官网里的介绍:

SPRING ( https://doi.org/10.1093/bioinformatics/btx792 ) 是预处理脚本的集合和基于 Web 浏览器的工具，用于可视化高维数据并与之交互。在此处查看示例数据集。SPRING 是为单细胞 RNA-Seq 数据开发的，但可以更广泛地应用。最小输入是高维数据点（细胞）矩阵和维名称（基因）列表

高维数据的低维可视化通常是不完美的。SPRING 不是试图呈现单个细胞数据的单一“确定性”视图，而是允许探索多种可视化，以开发数据结构的直觉。SPRING 的核心是创建数据点的 k 最近邻 (kNN) 图，并使用力导向布局在 2D 中可视化该图。

环境配置

conda create python=2.7 -n SPRING
source /home/miniconda3/bin/activate SPRING
conda install scikit-learn numpy scipy matplotlib h5py
pip install networkx fa2 python-louvain

分析使用

参考数据集和脚本

下载一个案例看看：

wget https://github.com/AllonKleinLab/SPRING_dev/blob/master/data_prep/spring_example_pbmc4k.ipynb

主目录应包含以下文件:

matrix.mtx
counts_norm_sparse_cells.hdf5
counts_norm_sparse_genes.hdf5
genes.txt

以及每个 SPRING 图的一个子目录：

categorical_coloring_data.json
cell_filter.npy
cell_filter.txt
color_data_gene_sets.csv
color_stats.json
coordinates.txt
edges.csv
graph_data.json
run_info.json

安装jupyter notebook

conda install jupyter

下载SPRING的github包

git clone https://git@github.com/AllonKleinLab/SPRING.git
sudo chmod -R a+w SPRING
cd SPRING

测试是否完成部署

• 通过输入启动本地服务器 python -m SimpleHTTPServer 8000 &
• 然后在网络浏览器（最好是 Chrome）中转到 http://192.168.3.33:8000/springViewer.html?datasets/centroids
  

说明环境搭建基本完成

然后在R中，用自己Seurat对象的数据矩阵、便签进行预处理转换成输入文件

# 保存矩阵
library(data.table)
setwd('./SPRING/example_inputs/project/')
tt<-data.frame(seu@assays$RNA@counts)
tt[1:10, 1:10]
fwrite(tt, file = 'seu_mtx.xls', quote = F, sep = '\t', row.names = T, col.names = T)

# 保存Seurat分群、样本来源、细胞类型标签
write.table(data.frame(orig.ident=as.character(seu$orig.ident),
                  cluster=as.character(seu$seurat_clusters)), 
                  file = 'orig.ident_list.xls', sep = '\t', 
                  quote = F, col.names = T, row.names = F)

数据预处理

启动Jupyer Notebook

jupyter notebook --ip 192.168.3.33 --port 8889 &

打开浏览器输入并用Token登录：

192.168.3.33:8889

运行Python代码：

import pickle, numpy as np
# Import SPRING helper functions
from preprocessing_python import *

#矩阵导入
import numpy as np
import pandas as pd

mtx = pd.read_csv('./example_inputs/project/seu_mtx.xls', 
                            delimiter='\t', encoding='utf-8')

#矩阵调整格式
mtx.index = mtx.iloc[:,:1]['Unnamed: 0']
mtx.index 
mtx = mtx.iloc[:, 1:]
mtx.head()
gene_list = list(mtx.index)
gene_list[1:10]

#矩阵转置
mtx = mtx.transpose()
# 转换为numpy数组
mtx  = mtx.to_numpy()

# 数据过滤、PCA降维, kNN聚类
# Filter out cells with fewer than 1000 UMIs
print 'Filtering cells'
mtx,cell_filter = filter_cells(mtx, 1000)

# Normalize gene expression data
# Only use genes that make up <
print 'Row-normalizing'
mtx = row_normalize(mtx)

# Filter genes with mean expression < 0.1 and fano factor < 3
print 'Filtering genes'
_,gene_filter = filter_genes(mtx,0.1,3)

# Z-score the gene-filtered expression matrix and do PCA with 20 pcs
print 'Zscoring and PCA'
Epca = get_PCA(Zscore(mtx[:,gene_filter]),20)

# get euclidean distances in the PC space
print 'Getting distance matrix'
D = get_distance_matrix(Epca)

# 导入标签信息
tt = pd.read_csv('./example_inputs/project/orig.ident_list.xls', delimiter='\t')
orig_ident = list(tt['orig.ident'])
cluster_list = list(tt['cluster'])
celltype=list(tt['celltype'])

# 整合成字典
cell_groupings = dict() #'cluster':cluster_list, 'timepoint':orig_ident
cell_groupings['cluster'] = cluster_list
cell_groupings['timepoint'] = orig_ident
cell_groupings['celltype'] = celltype

# 生成网页可视化的输入文件夹里面的文件
# load additional data (gene_list, cell_groupings, custom_colors)
# gene_list is a list of genes with length E.shape[1]
# cell_groupings is a dict of the form: { <grouping_name> : [<cell1_label>, <cell2_label>,...] }
# a "grouping" could be the sample id, cluster label, or any other categorical variable
# custom_colors is a dict of the form { <color_track_name> : [<cell1_value>, <cell2_value>,...] }
# a "custom color" is any continuous variable that you would like to use for coloring cels. 
# gene_list, cell_groupings, custom_colors = pickle.load(open('example_inputs/python_data.p'))

# save a SPRING plots with k=5 edges per node in the directory "datasets/frog_python/"
# coarse graining can also be performed using the optional coarse_grain_X parameter
print 'Saving SPRING plot'
save_spring_dir(mtx,D,5,gene_list,'datasets/project', 
   cell_groupings=cell_groupings, 
  # custom_colors=custom_colors, 
   coarse_grain_X=1)

在浏览器中可视化数据并进行下载

打开浏览器，输入：http://192.168.3.33:8000/springViewer.html?datasets/project
后面就可以进行对单个基因表达量的可视化

以及把自己的标签映射上去

然后是下载保存图片

总结

• 个人认为这个SPRING算法效果不是特别好，尤其针对几个样本合并的情况
• 即使原始的输入数据不变，但是每一次运行出来的图像样子都是不一样的，没办法复现（按作者的说法是“Low-dimensional visualizations of high-dimensional data are usually imperfect. Rather than attempting to present a single ‘definitive’ view of single cell data, SPRING allows exploration of multiple visualizations in order to develop an intuition for data structure. ”）
• 更多功能和坑后面再摸索

==================================================================

后续dev开发板踩坑

环境搭建差不多，遇到没有的库pip安装即可
官网：https://github.com/AllonKleinLab/SPRING_dev

https://github.com/AllonKleinLab/SPRING_dev.git
#创建conda环境后启动环境
source path/bin/activate SPRING_dev
cd ./SPRING_dev 
#启动Jupyter 
jupyter notebook --ip 192.168.3.33 --port 8899 &

打开浏览器输入 192.168.3.33:8899

数据预处理

#import modules, define some functions for loading, saving and processing a gene-barcode matrix
%matplotlib inline
import pkg_resources
import collections
import numpy as np
import pandas as pd
import scipy.sparse as sp_sparse
import tables
import matplotlib
import matplotlib.pyplot as plt
import h5py

np.random.seed(0)

GeneBCMatrix = collections.namedtuple('GeneBCMatrix', ['gene_ids', 'gene_names', 'barcodes', 'matrix'])

def get_matrix_from_h5(filename, genome):
    with tables.open_file(filename, 'r') as f:
        try:
            dsets = {}
            for node in f.walk_nodes('/' + genome, 'Array'):
                dsets[node.name] = node.read()
            matrix = sp_sparse.csc_matrix((dsets['data'], dsets['indices'], dsets['indptr']), shape=dsets['shape'])
            if 'id' in dsets.keys():
                return GeneBCMatrix(dsets['id'], dsets['name'], dsets['barcodes'], matrix)
            else:
                return GeneBCMatrix(dsets['genes'], dsets['gene_names'], dsets['barcodes'], matrix)
        except tables.NoSuchNodeError:
            raise Exception("Genome %s does not exist in this file." % genome)
        except KeyError:
            raise Exception("File is missing one or more required datasets.")

def save_matrix_to_h5(gbm, filename, genome):
    flt = tables.Filters(complevel=1)
    with tables.open_file(filename, 'w', filters=flt) as f:
        try:
            group = f.create_group(f.root, genome)
            f.create_carray(group, 'genes', obj=gbm.gene_ids)
            f.create_carray(group, 'gene_names', obj=gbm.gene_names)
            f.create_carray(group, 'barcodes', obj=gbm.barcodes)
            f.create_carray(group, 'data', obj=gbm.matrix.data)
            f.create_carray(group, 'indices', obj=gbm.matrix.indices)
            f.create_carray(group, 'indptr', obj=gbm.matrix.indptr)
            f.create_carray(group, 'shape', obj=gbm.matrix.shape)
        except:
            raise Exception("Failed to write H5 file.")
        
def subsample_matrix(gbm, barcode_indices):
    return GeneBCMatrix(gbm.gene_ids, gbm.gene_names, gbm.barcodes[barcode_indices], gbm.matrix[:, barcode_indices])

def get_expression(gbm, gene_name):
    gene_indices = np.where(gbm.gene_names == gene_name)[0]
    if len(gene_indices) == 0:
        raise Exception("%s was not found in list of gene names." % gene_name)
    return gbm.matrix[gene_indices[0], :].toarray().squeeze()

def save_count_matrix_to_h5(E, gene_list, filename):
    with h5py.File(filename, 'w') as hf:
        hf.create_dataset("indptr" ,  data= E.indptr)
        hf.create_dataset("indices",  data= E.indices)
        hf.create_dataset("data"   ,  data= E.data)
        hf.create_dataset("shape"  ,  data= E.shape)
        hf.create_dataset("genes"  ,  data= gene_list)

%pylab inline
from helper_functions import *
from collections import defaultdict

plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'DejaVu Sans'
plt.rc('font', size=14)
plt.rcParams['pdf.fonttype'] = 42

sample_list = ["sample1", "sample2"]

# D stores all the data; one entry per library
D = {}

for j, s in enumerate(sample_list):
    print(j, s)

# D stores all the data; one entry per library
D = {}

for j, s in enumerate(sample_list):
    filename = './Project/'+ s +'/outs/raw_feature_bc_matrix.h5'
    raw_matrix_h5 = filename
    print raw_matrix_h5
    if filename == './Project/'+ s +'/outs/raw_feature_bc_matrix.h5':
        genome = "matrix"
    else:
        genome = 'GRCh38'
    D[s] = {}
    D[s]['meta'] = {}
    gbm = get_matrix_from_h5(raw_matrix_h5, genome)
    D[s]['E'] = transpose(gbm.matrix)
    D[s]['meta']['gene_list']=gbm.gene_names
    D[s]['meta']['gene_id']=gbm.gene_ids
    print D[s]['E'].shape
    D[s]['cell_index']=gbm.barcodes
    print (len(D[s]['cell_index']))

gene_list = D[s]['meta']['gene_list']
gene_id = D[s]['meta']['gene_id']

Filter cells by total counts and number of genes detected

# plot total counts histograms - don't actually filter out any barcodes yet


# adjust total counts thresholds

for j,s in enumerate(sample_list):
    D[s]['total_counts'] = np.sum(D[s]['E'], axis=1).A[:,0]
    D[s]['genes_detected'] = np.sum(D[s]['E']>0, axis=1).A[:,0]
    D[s]['meta']['min_tot']= np.mean(D[s]['total_counts'])+np.std(D[s]['total_counts'])
    D[s]['meta']['min_genes_detected']=200;

    fig3 = plt.figure()
    ax3 = fig3.add_subplot(111)
    ax3.set_xlabel('Transcripts per barcode (log10)')
    ax3.set_ylabel('genes detected')

    ax3.scatter(log(1 + D[s]['total_counts']),D[s]['genes_detected']);
    ax3.plot(ax3.get_xlim(),[D[s]['meta']['min_genes_detected'],D[s]['meta']['min_genes_detected']]);
    title(s)

D_orig=D
for j,s in enumerate(sample_list):
    ix = (D[s]['genes_detected'] > 200) & (D[s]['total_counts'] > 500)
    if np.sum(ix) > 0:
        print s, np.sum(ix), '/', D[s]['E'].shape[0]

# Actually filter out low-count barcodes
for j,s in enumerate(sample_list):
    print '---  %s ---' %s
    print 'Pre-filter: %i barcodes' %D[s]['E'].shape[0]
    tmpfilt = np.nonzero((D[s]['genes_detected'] > 200) & (D[s]['total_counts'] > 500))[0]
    D[s] = filter_dict(D[s], tmpfilt)
    print 'Post-filter: %i barcodes' %D[s]['E'].shape[0]

Filter cells by mito fraction

# get mitochondrial genes

mt_ix = [i for i,g in enumerate(gene_list) if g.startswith('MT-')]
print [gene_list[i] for i in mt_ix]

# plot mito-gene frac histograms - don't actually filter out any cells yet

# set mito-gene frac threshold
for j,s in enumerate(sample_list):
    D[s]['meta']['max_mt'] = 0.2

for j,s in enumerate(sample_list):
    fig = plt.figure(figsize=(8, 6))
    ax = fig.add_subplot(111, xscale='linear', yscale='linear',
        xlabel='MT frac.', ylabel='no. cells')

    D[s]['mito_frac'] = np.sum(D[s]['E'][:,mt_ix], axis=1).A[:,0] / np.sum(D[s]['E'], axis=1,dtype=float).A[:,0]

    ax.hist(D[s]['mito_frac'], cumulative=False, 
            bins=np.linspace(0, 1, 30))

    ax.plot([D[s]['meta']['max_mt'],D[s]['meta']['max_mt']],ax.get_ylim());
    title(s)

    print D[s]['E'].shape[0], np.sum(D[s]['mito_frac'] <= D[s]['meta']['max_mt'])


# Actually filter out mito-high cells 
D_filt=D
for s in sample_list:
    print '---  %s ---' %s
    print 'Pre-filter: %i barcodes' %D[s]['E'].shape[0]
    tmpfilt = np.nonzero(D[s]['mito_frac'] <= D[s]['meta']['max_mt'])[0]
    D[s] = filter_dict(D[s], tmpfilt)
    print 'Post-filter: %i barcodes' %D[s]['E'].shape[0]

Merge data, normalize

# create master dataset (all SPRING subsets will refer back to this)

samp_lookup = {}
samp_id_flat = np.array([],dtype=str)

for s in D.keys():
    samp_id_flat = np.append(samp_id_flat, [s] * D[s]['E'].shape[0])

        
E = scipy.sparse.lil_matrix((len(samp_id_flat), len(gene_list)), dtype=int)
total_counts = np.zeros(len(samp_id_flat), dtype=int)
mito_frac = np.zeros(len(samp_id_flat), dtype=float)
genes_detected = np.zeros(len(samp_id_flat), dtype=int)

for s in D.keys():
    print s
    E[samp_id_flat == s, :] = D[s]['E']
    total_counts[samp_id_flat == s] = D[s]['total_counts']
    mito_frac[samp_id_flat == s] = D[s]['mito_frac']
    genes_detected[samp_id_flat == s] = D[s]['genes_detected']

E = E.tocsc()

E_full=E
# remove genes that are not expressed by any cells
# optionally remove mito and rps genes
gene_list = D[sample_list[0]]['meta']['gene_list']
gene_id = D[sample_list[0]]['meta']['gene_id']
remove_crapgenes = 1

if remove_crapgenes:
    import re
    mt_ix = [i for i,g in enumerate(gene_list) if g.startswith('MT-')]
    rp_ix = [i for i,g in enumerate(gene_list) if g.startswith('RP1') 
             or g.startswith('RP2') or g.startswith('RP3')
             or g.startswith('RP4') or g.startswith('RP5')
             or g.startswith('RP6') or g.startswith('RP7')
             or g.startswith('RP8') or g.startswith('RP9')
             or g.startswith('RPL') or g.startswith('RPS')
            ]
    #or g.startswith('RP4','RP5','RP6') or g.startswith('RP7','RP8','RP9')]
    keep_genes = (E.sum(0) > 0).A.squeeze() #* rp_ix * mt_ix
    keep_genes[rp_ix] = 0
    keep_genes[mt_ix] = 0
    print sum(keep_genes), '/', len(keep_genes)
else:
    keep_genes = (E.sum(0) > 0).A.squeeze()
    print sum(keep_genes), '/', len(keep_genes)

# normalize by total counts
E = E[:,keep_genes]
E = tot_counts_norm_sparse(E)[0]

print shape(E)
gene_list = gene_list[keep_genes]
gene_id = gene_id[keep_genes]
merged_list=[x+"_"+gene_id[i] for i,x in enumerate(gene_list)]

Save base directory files

main_spring_dir=os.getcwd()

if not os.path.exists(main_spring_dir):
    os.makedirs(main_spring_dir)

# Option to save the barcode information for later use
BCR = 1

if BCR:
    barcode_dir = main_spring_dir + "/barcodes/"
    if not os.path.exists(barcode_dir):
        os.makedirs(barcode_dir)
        for s in sample_list:
            print '---  %s ---' %s
            np.savetxt(barcode_dir + s, D[s]['cell_index'], fmt='%s')

# option to save mitochondrial percentages for later use
MT = 1

if MT:
    MT_dir = main_spring_dir + "/mito/"
    if not os.path.exists(MT_dir):
        os.makedirs(MT_dir)
        for s in sample_list:
            print '---  %s ---' %s
            np.savetxt(MT_dir + s, D[s]['mito_frac'], fmt='%s')

gene_list_new=np.array(merged_list)
np.savetxt(main_spring_dir + '/genes.txt', merged_list, fmt='%s')

# save master expression matrix in hdf5 format
import h5py

print 'Saving hdf5 file for fast gene loading...'
E = E.tocsc()
hf = h5py.File(main_spring_dir + '/counts_norm_sparse_genes.hdf5', 'w')
counts_group = hf.create_group('counts')
cix_group = hf.create_group('cell_ix')

hf.attrs['ncells'] = E.shape[0]
hf.attrs['ngenes'] = E.shape[1]

for iG, g in enumerate(merged_list):
    if iG % 3000 == 0:
        print g, iG, '/', len(merged_list)
    counts = E[:,iG].A.squeeze()
    cell_ix = np.nonzero(counts)[0]
    counts = counts[cell_ix]
    counts_group.create_dataset(g, data = counts)
    cix_group.create_dataset(g, data = cell_ix)

hf.close()

##############

print 'Saving hdf5 file for fast cell loading...'
E = E.tocsr()
hf = h5py.File(main_spring_dir + '/counts_norm_sparse_cells.hdf5', 'w')
counts_group = hf.create_group('counts')
gix_group = hf.create_group('gene_ix')

hf.attrs['ncells'] = E.shape[0]
hf.attrs['ngenes'] = E.shape[1]

for iC in range(E.shape[0]):
    if iC % 3000 == 0:
        print iC, '/', E.shape[0]
    counts = E[iC,:].A.squeeze()
    gene_ix = np.nonzero(counts)[0]
    counts = counts[gene_ix]
    
    counts_group.create_dataset(str(iC), data = counts)
    gix_group.create_dataset(str(iC), data = gene_ix)

hf.close()

写入矩阵

scipy.io.mmwrite(main_spring_dir + '/matrix.mtx', E)

Save merged samples

merge_setup = {'/Dataset_v1':sample_list}

for s, smerge in merge_setup.items():
    print smerge
    t0 = time.time()
    print t0
    print '________________', s
    
    cell_ix = np.in1d(samp_id_flat, smerge)
    
    run_all_spring_1_6(E[cell_ix,:],
                     list(merged_list), s, main_spring_dir, normalize=False, tot_counts_final = total_counts[cell_ix],
                     min_counts = 3, min_cells = 3, min_vscore_pctl = 90, 
                     show_vscore_plot = True, num_pc = 60, pca_method = '', k_neigh=4, use_approxnn = False,
                     output_spring = True, num_force_iter = 2000,
                     cell_groupings = {'Sample': list(samp_id_flat[cell_ix])})
    
    print time.time() - t0
    
    np.save(main_spring_dir + s + '/cell_filter.npy', np.nonzero(cell_ix)[0])
    np.savetxt(main_spring_dir + s + '/cell_filter.txt', np.nonzero(cell_ix)[0], fmt='%i')
    
    print time.time() - t0

SignacX包进行分群和标签生成

#install.packages('SignacX')
library(SignacX)

# dir is the subdirectory generated by the Jupyter notebook; it is the directory that contains the 'categorical_coloring_data.json' file.
dir = "./Dataset_v1/" 

# load the expression data
E = CID.LoadData(dir)

# generate cellular phenotype labels
labels = Signac(E, spring.dir = dir, num.cores = 14)
# alternatively, if you're in a hurry, use:
# labels = SignacFast(E, spring.dir = dir, num.cores = 4)
celltypes = GenerateLabels(labels, E = E, spring.dir = dir)

# write cell types and Louvain clusters to SPRING
dat <- CID.writeJSON(celltypes, spring.dir = dir)

添加自己的标签（略）

网页可视化

python -m SimpleHTTPServer 8080 &

http://192.168.3.33:8080/springViewer.html?datasets/project